research and data analysis courses

Data Analysis Courses

• Social Sciences

Introduction to Digital Humanities

Develop skills in digital research and visualization techniques across subjects and fields within the humanities.

Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

Data Science: Productivity Tools

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

Data Science: Linear Regression

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Data Science: Capstone

Show what you’ve learned from the Professional Certificate Program in Data Science.

Data Science: R Basics

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Data Science: Visualization

Learn basic data visualization principles and how to apply them using ggplot2.

Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

Here to help you grow

Whether you're looking to build your business, develop your career, or pick up a new digital skill, we can help you get started.

What can we help you with?

And what would you like to do?

• Show me everything
• Prepare for a new job
• Develop communication skills
• Increase my productivity
• Learn about digital marketing
• Learn coding & development skills
• Get started with artificial intelligence
• Get started with cloud computing
• Stay safe online
• Learn design skills
• Improve my digital wellbeing
• Champion diversity
• Learn about sustainability
• Understand my audience
• Start selling online
• Expand internationally
• Keep my business safe online

Grow your career

Whether you're writing your first CV or deepening your technical knowledge, our library is full of ways to sharpen your digital skillset.

Google Career Certificates

Earn a Google Career Certificate to prepare for a job in a high-growth field like Data Analytics, UX Design, and more.

Introductory digital skills courses

Get started with a range of digital skills, with entry level courses in everything from online marketing to coding.

Cloud computing fundamentals

From intro to advanced-level learning, find out more about cloud computing principles and career paths.

Google product trainings

Learn how to get the most out of the Google products you use, like Google Ads or Analytics.

Grow your business

From bringing your business online for the first time to growing its reach internationally, our library of online learning and tools can help you take your business further.

Your Digital Essentials Guide

Get an introduction to the products, tools and tips that can help you build an online presence for your small business.

Flexible online training

Learn online, at your own pace, with a library of training made to help strengthen your business with digital skills.

Resources for startups

Google for Startups connects you to the right people, products and best practices to help your business thrive.

Helpful tools for small business owners

Google Business Profile

Manage how your business shows up on Google Search and Maps to help new customers find you more easily.

Market Finder

Identify new potential markets and start selling to customers at home and around the world.

Growth stories

Meet people all over Europe who are using technology to adapt and grow their business or career.

About Grow with Google

Grow with Google is a programme that helps people to grow their careers or businesses by learning new skills and making the most of digital tools. We partner with governments and local organisations to develop digital skills and tools where they are needed most.

Data Analysis for Life Sciences

Master key concepts using the r programming language.

This HarvardX professional certificate program gives learners the necessary skills and knowledge to analyze data in the life sciences.

What You'll Learn

Technological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.

Using the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.

Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.

The course will be delivered via edX and connect learners around the world. 

Courses in this Program

2–4 hours per week, for 4 weeks An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

2–4 hours per week, for 4 weeks Learn to use R programming to apply linear models to analyze data in life sciences.

2–4 hours per week, for 4 weeks A focus on the techniques commonly used to perform statistical inference on high throughput data.

2–4 hours per week, for 4 weeks A focus on several techniques that are widely used in the analysis of high-dimensional data.

Your Instructor

Rafael Irizarry

Professor of Biostatistics at Harvard University Read full bio.

Michael Love

Assistant Professor, Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health Read full bio.

Job Outlook

• R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
• Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
• 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
• Data Scientists are few in number and high in demand. (source: TechRepublic)

Ways to take this program

When you enroll in this program, you will register for a Verified Certificate for all 4 courses in the Professional Certificate Series. 

Alternatively, learners can Audit the individual course for free and have access to select course material, activities, tests, and forums. Please note that Auditing the courses does not offer course or program certificates for learners who earn a passing grade.

Cookie Policy

We use cookies to operate this website, improve usability, personalize your experience, and improve our marketing. Privacy Policy .

By clicking "Accept" or further use of this website, you agree to allow cookies.

• Data Science
• Data Analytics
• Machine Learning

The 5 Best Courses to Learn Data Analytics for 2024

The increasing demand for data analysts has spurred a proliferation of data-related courses. However, many of these programs do not offer a well-rounded learning experience that covers all the essential analytics skills.

For this article, I've sifted through the best online course platforms to find the best classes for learning data analytics. Since many skills are involved in an analyst's job, I've provided the best course option for specific categories. Hopefully, this categorization will help you more easily decide which course is the best for you .

Go from side-hustling to earning enough to quit your job. Use code sdgd3 for 50% off. Check it out →

If you're still having trouble picking a course, feel free to check out the Learning Guide at the end of this article for information about what data analysts do, what skills they need, and recommendations on how to get started.

Course selection criteria

There're thousands of data courses these days, so to narrow down the options to only the most qualified, I considered the following data points:

• Organization and quality of content
• Student reviews
• Staff and student discussions
• Quizzes and assignments
• Inclusion of statistical concepts

These criteria helped reduce the number of potential courses to only a handful, of which I then compared and contrasted to bring you my recommendations listed below. These final few are what I found to be the best for learning data analytics online today.

TL;DR Best Data Analytics courses for 2024

Google data analytics professional certificate — google, coursera.

Complete beginners looking for a broad introduction to data analytics focused on Google products.

This highly-rated course from Google covers a wide range of topics and is designed to "have you job-ready in less than six months," according to their description. Google states that certificate holders will have access to an Employer Consortium, which comprises 150 U.S. companies committed to considering graduates for entry-level analytics positions.

Out of all the courses I could find, this series by Google is the most comprehensive. The course video content is well-organized, professional, and exciting, and with so many students enrolled, there's an active community for questions and answers. Through the course, you're exposed to the most popular analytics tools: Google Sheets, SQL, R, and Tableau. These topics cover the entire data analytics pipeline and give you the skills to develop your own projects.

Course 1: Foundations: Data, Data, Everywhere

• Intro to data analytics
• Intro to analytics tools (Sheets, SQL)

Course 2: Ask Questions to Make Data-Driven Decisions

• Problem-solving
• Asking good questions
• Spreadsheet basics
• Communicating effectively

Course 3: Prepare Data for Exploration

• Data formats, types, modeling, collection
• Data ethics
• Using spreadsheets with databases
• Intro to BigQuery
• Data security

Course 4: Process Data from Dirty to Clean

• Data integrity
• Data cleaning with spreadsheets and SQL
• Resume/career info

Course 5: Analyze Data to Answer Questions

• Organizing data for Sheets and Bigquery
• Formatting and transforming data
• Data aggregation functions in Sheets and SQL
• More formulas, functions, and pivot tables
• Intermediate SQL

Course 6: Share Data Through the Art of Visualization

• Intro to data visualization concepts
• Creating visualizations with Tableau
• Developing data stories
• Creating effective presentations

Course 7: Data Analysis with R Programming

• Intro to the R language and RStudio
• Cleaning, organizing, and transforming data with R
• Creating visualizations with R
• Making reports and docs for R analyses

Course 8: Google Data Analytics Capstone: Complete a Case Study

• Developing your own project to display in your portfolio and resume
• Info on building a compelling portfolio

The curriculum is divided into "courses," but some of the content can be completed far shorter than a typical Coursera course. Some learners report they completed the entire Specialization in under a month. So, depending on your background, you may well finish the course series quicker than advertised.

Enroll in the Google Data Analytics Professional Certificate

Become a Data Analyst — Linkedin Learning

Those looking for broad exposure to many data analytics tools, but with more of a focus on Microsoft products

The data analyst learning path from Linkedin Learning in a collection of courses organized in a way that provides you with a well-rounded education. The course path is similar in scope to that of Google's listed above, but focusing on Microsoft products, namely Excel and Power BI.

One benefit to this course series over Google's is the inclusion of statistics modules, which is excellent for learners that would like to strengthen their math for analytics.

Course 1: The Non-Technical Skills of Effective Data Scientists

• Imperative non-technical skills

Course 2: Learning Excel: Data Analysis

• Basic statistics in Excel
• Visualizing data
• Hypothesis testing
• Using distributions
• Covariance and correlation
• Bayesian analysis

Course 3: Data Fluency: Exploring and Describing Data

• Data fluency
• How to use the most common chart types
• Descriptive statistics

Course 4: Learning Data Analytics: 1 Foundations

• Importing and cleaning data
• Creating and maintaining datasets
• Intro to Power Query

Course 5: Learning Data Analytics Part 2: Extending and Applying Core Knowledge

• Working with business data
• Building datasets with queries
• Building pivot tables
• Intro to Power BI
• Presenting data in meetings

Course 6: Excel Statistics Essential Training: 1

• Types of data
• Probability
• Central tendency
• Variability
• Distributions
• Analysis of variance (ANOVA)
• Repeated measure analysis
• Correlation

Course 7: Predictive Analytics Essential Training: Data Mining

• Defining problems
• Understanding data requirements
• Problems and solutions you'll face with data
• Deploying models
• Cross-Industry Standard for Data Mining (CRISP-DM)

Course 8: Power BI Essential Training

• Getting data into Power BI
• Reports and visualizations
• Creating dashboards
• Sharing data
• Power BI mobile

Course 9: Learning Data Visualization

• Information hierarchy
• Storytelling
• Visual paradigms
• Interactivity

Course 10: Tableau Essential Training

• Managing data sources
• Tableau worksheets and workbooks
• Creating custom calculations and fields
• Analyzing data in Tableau
• Mapping geographic data
• Creating dashboards and actions

Course 11 SQL: Data Reporting and Analysis

• Using SQL to report data
• Grouping SQL results
• Merging data
• Some advanced syntax

Course 12: R Essential Training: Wrangling and Visualizing Data

• Intro to R and RStudio
• Importing data
• Visualizing data in R
• Wrangling data
• Recoding data

Course 13 Data Cleaning in Python Essential Training

• Causes of errors
• Detecting, preventing, and fixing errors

You can acquire many in-demand skills from the data analyst path on Linkedin Learning. There is some overlap with the statistics and visualization content, but for a beginner, this can only reinforce your newly acquired analytics skills as a beginner.

The one gripe I have with this path is that the Python course at the end already assumes Python experience, but nowhere in the path is there a Python syntax course. If you intend to complete this path, I'd also recommend learning Python syntax on Codecademy , the top Python course according to the data.

Enroll in the Become a Data Analyst path

Excel Skills for Data Analytics and Visualization Specialization — Coursera

Those with some Excel experience looking to analyze data as fast as possible.

This course from Macquarie University covers many of Excel's intermediate to advanced concepts, allowing you to clean, analyze, and visualize data efficiently. If you're rusty on statistics, it may be a good idea to complement this course with an appropriate statistics course since the math side of analytics isn't covered here.

Overall, the quality of instruction is fantastic, and you'll find plenty of assessments and assignments to hone your Excel and Power BI skills.

Course 1: Excel Fundamentals for Data Analysis

• Cleaning and manipulating text
• Working with numbers and dates
• Defined Names
• Tables for automating data manipulation
• Logical and lookup functions

Course 2: Data Visualization in Excel

• Conditional formatting, sparklines, and number formats
• Various charting techniques
• Specialized charts
• Interactive dashboards using pivot charts, slicers, and dynamic charts

Course 3: Excel Power Tools for Data Analysis

• Get and transform data with Power Query
• Power Pivot and data models
• Visualize data with Power BI

Once you've completed this course and are comfortable analyzing data in Excel, it may make sense to start learning SQL or R. For SQL, see the previous course; for R see the following course.

Enroll in Excel Skills for Data Analytics and Visualization Specialization

Data Analyst in R — Dataquest

Beginners who are more interested in the programming side of data analytics, as opposed to a spreadsheet software, like Excel.

Dataquest is one of the most popular interactive data science learning platforms. Despite not having videos for each topic, their teaching methodology and project-based learning style are very effective.

The Data Analyst with R path, which also has a Python version , brings learners with no programming experience through the entire analytics pipeline using R and SQL.

Python is often the first choice for working with data, but if your goal is to become proficient in analytics, R plays more nicely with data and statistics out of the box. The language's straightforwardness lets you be productive with data faster than Python in many cases.

• Intro to R syntax
• Data structures
• Loops, iterations, functions, and control flow
• Data visualization
• Data cleaning
• SQL fundamentals
• Getting data from APIs and web scraping in R
• Beginner to intermediate statistics using R
• Probability fundamentals and conditional probability
• Linear regression
• Machine learning fundamentals
• Interactive web apps with R

Dataquest has a much stronger curriculum in statistics and probability than other courses, so if you feel like your math needs work, this course path will help. Furthermore, Dataquest can also serve as a great resource for practicing your skills through their guided projects, regardless of which other course you decide to take.

Enroll in the Data Analyst in R path

Business and Data Analysis with SQL — Skillshare

Those who are more interested in learning and applying SQL to analytics, or anyone wishing to expand past spreadsheets

Geoff Devitt, the instructor for this course, is a 20+ year veteran of the Big Data industry and created this Skillshare class to share what he's learned building projects and analyzing data over his career.

Unlike other courses mentioned in this list, this course focuses solely on solving analytics problems using Structured Query Language (SQL), a programming language built to interface with databases. Since SQL runs many businesses, it was one of the most highly requested skills mentioned in job posts during my research for this article.

• Intro to databases
• Normalizing data
• Fact tables
• Aggregating data
• Entity Relationship Diagrams (ERD)
• Setting up and connecting to MySQL
• Beginner to intermediate SQL
• Data analysis example project
• Query explain plans
• MySQL administration

Once you've finished this course, you should be reasonably comfortable with SQL and can start working on your portfolio projects.

Enroll in Business and Data Analysis with SQL

Learning guide

What do data analysts do.

Data analysts' responsibilities vary widely from company to company, but most have the following primary objectives: 1) Pull data from a variety of sources, such as databases, Excel workbooks, and CSVs 2) Apply statistical analysis to the data 3) Condense information into easily digestible formats 4) Create visualizations, summary reports, and dashboards

A data analyst creates value for a company by helping them make better decisions and, ultimately, to generate more revenue.

Data Analyst vs. Data Scientist

Becoming a data scientist often starts with first becoming a data analyst. If we were to look at a Venn diagram of the skillsets of both roles, the data scientist's circle would fully encompass the data analyst's.

The Northeastern University blog sums up the differences nicely:

While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists , on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Essentially, data analysts work with established data and pipelines to draw insight, while data scientists create new data pipelines and use more advanced tools and techniques to solve more complex problems.

Data analyst tools and skills

Data analysts use various tools and processes, so the courses you take will depend on your desired position and what knowledge you already have. One company may need an analyst primarily for Excel and Power BI, while another business needs someone with SQL and Tableau experience.

If you're coming into data analytics with no programming experience, your quickest path to job-ready would be to build proficiency in a spreadsheet program like Excel or Google Sheets and visualization software, such as Tableau or Google Data Studio.

Alternatively, if you have any programming experience, starting with SQL, Python, or R would be a good choice. Most job posts mention SQL, so if that's your primary interest, feel free to check out my picks for the best SQL courses , which rank the best courses for learning SQL in general. In this article, I focus my SQL suggestions primarily on using SQL for analytics.

Which technology (Excel, SQL, Tableau, etc.) do I start with?

Learning Excel is one of the best ways to get started in data analytics since many people already have exposure to the software. You can more easily build on this familiarity through an Excel-specific course, like Excel Skills for Data Analytics and Visualization Specialization from Coursera.

Despite using Python to analyze data for years, I still reach for Excel when I need to do a quick analysis, share results with the team, and make educated decisions. Many companies still run their entire analytics pipeline through Excel, so many job opportunities are awaiting skilled Excel data analysts.

Knowing more about Excel (or Google Sheets) can only benefit you in analytics, but if you already have a decent Excel foundation, going on to SQL would be a safe bet. Many businesses store their data in a SQL database and need analysts to pull, summarize, and make sense of that data. When researching job posts for this article, I found more jobs requiring someone with solid SQL experience than any other technology.

Since SQL is a programming language, it makes sense that most SQL courses focus solely on syntax and data modeling, which is why I've included Business and Data Analysis with SQL here since it has more of an analytics approach.

Finally, if you already have some spreadsheet experience and would rather move on to programming language with more applications, then learning R is an excellent choice. Unlike SQL, which is confined to databases, R is a regular programming language with many advantages in statistics and visualization. R is more straightforward than Python for pure analytics work, but Python skills are still in high-demand. Either way, I would recommend working through the free R or Python material on Dataquest to start learning as quickly as possible.

Apply what you learn

No matter which skill, technology, or path you start, it's imperative you practice everything you learn. Solidify your knowledge by analyzing something you're interested in, and produce reports or dashboards to display your findings. Many employers consider unique, completed projects an equivalent replacement for work experience. Not only does talking through a passion project you've completed aid interviewers with gauging your abilities, but it also helps you escape imposter syndrome and build self-confidence.

Are certificates worth it?

Every course in this list offers a certificate, and they are a fantastic way to prove to yourself that you completed a course and solidified knowledge of a topic. Despite that, you should approach building your resume as if the certificates you earn don't exist. You should focus solely on building projects that demonstrate your knowledge and aptitude, especially when you lack past job experience.

Pick a niche you're interested in, whether it's sports, finance, health, or marketing, and build something that interests you. It doesn't have to be groundbreaking, but it should showcase your abilities to interviewers. If you're looking for inspiration, check out Kaggle datasets and community notebooks, or follow a few of the Dataquest or Coursera guided projects.

Good luck, have fun!

Learning data analytics can be a challenging journey, but whether for a career or hobby, I'm confident analytics will lead you down a path of exciting projects, tangents, and fortuitous findings.

Get updates in your inbox

Join over 7,500 data science learners.

Recent articles:

The 6 best python courses for 2024 – ranked by software engineer, best course deals for black friday and cyber monday 2024, sigmoid function, dot product, 7 best artificial intelligence (ai) courses.

Top courses you can take today to begin your journey into the Artificial Intelligence field.

Meet the Authors

Chief Editor at LearnDataSci and software engineer

Back to blog index

• Technical Help
• CE/CME Help
• Billing Help
• Sales Inquiries
• CE Certificates
• Billing Inquiries
• Purchase Inquiries

Qualitative Data Analysis

This course provides an applied approach to qualitative data analysis through the lens of multiple methods and methodologies.

About this Course

The analysis of qualitative research data is a fundamental yet multifaceted process that requires careful attention to the unique qualities of qualitative research design. This course provides an applied, phenomenological approach to qualitative data analysis. It is designed for an interdisciplinary audience with examples taken from the nonprofit, commercial, and government sectors in the health and social sciences.

Undergraduate/graduate students, research staff, and IRB members in particular may find this course meaningful as an introduction to qualitative research methods.

Course Preview:

Language Availability: English

Suggested Audiences: Faculty, IRB Chairs, IRB Members, Research Staff, Undergraduate and Graduate Students

Organizational Subscription Price: $675 per year/per site for government and non-profit organizations; $750 per year/per site for for-profit organizations Independent Learner Price: $99 per person

Course Content

" role="button"> introduction to qualitative data analysis.

This module discusses the data analysis considerations shared by all qualitative methods and approaches this course covers. This includes the basic qualitative data analysis process and tools and the rigorous and ethical approaches to qualitative data analysis that apply across methods.

Recommended Use: Required ID (Language): 20971 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> In-Depth Interview Method

This module begins with an overview of the basic in-depth interview method and its variations. This provides the foundation for the core discussions concerning the distinctive aspects of the in-depth interview method that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20972 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Focus Group Discussion Method

To provide a basis for the core discussions, this module begins with an overview of the fundamentals of the focus group method and its variations. This provides an understanding of the distinctive aspects of the focus group method that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20973 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Ethnography

Understanding the ethnographic approach and its variations is important to the discussion of data analysis. For this reason, the module begins with an overview of ethnographic research and the distinctive aspects of ethnography that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20974 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Narrative Research

This module provides an overview of narrative research and its variations. It provides an overview of narrative research, which serves as a foundation for the core discussions concerning the distinctive aspects of the narrative research approach that affect qualitative data analysis. The module concludes with a discussion of quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20975 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Case Study Research

Case study research and its variations are examined at the start of this module. Then, distinctive aspects of case study research that affect qualitative data analysis are explored, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20976 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Qualitative Content Analysis Method

This module reviews the basic Qualitative Content Analysis (QCA) method and its variations. It also discusses the distinctive aspects of the QCA method that affect qualitative data analysis and the quality and ethical considerations that QCA presents.

Recommended Use: Supplemental ID (Language): 20977 (English) Author(s): Margaret R. Roller, MA - Roller Research

Who should take the Qualitative Data Analysis course?

The suggested audience includes students, faculty, and staff that want to learn more about the basics of qualitative data analysis and one or more of the discussed methods.

How long does it take to complete the Qualitative Data Analysis course?

This course consists of one required module and six supplemental modules. All learners should complete module 1 and then complete the supplemental modules as needed (20-30 minutes each).

" role="button"> Why should an organization subscribe to this course?

Organizational subscriptions provide access to the organization's affiliated members. This allows organizations to train individuals across the organization on how to properly conduct qualitative data analysis.

" role="button"> What are the standard recommendations for learner groups?

This course is designed such that learners should complete the first module and then any following method modules as needed.

" role="button"> Is this course eligible for continuing medical education credits?

This course does not currently have CE/CME credits available.

Related Content

This course provides learners with an understanding of how to improve study design, collect and analyze data, and promote reproducible research.

Essentials of observational research protocol design and development.

Foundational course that orients and prepares learners to engage with the scholarly publication process in an informed way.

An in-depth review of the development and execution of protocols.

Privacy Overview

Special Discount

Get 10% - 40% off all our courses, view terms & conditions.

2 participants: 10% discount 3 to 5 participants: 15% discount 6 or more participants: 20% discount Online distance learning: 20% discount

Valid till 31 st October, 2023

Research and Data Analysis Courses Training Courses & Seminars -->

• Course Categories
• Training Courses

Research and Data Analysis Courses

Unleash the power of research and data analysis with our comprehensive course category. Dive into the world of data-driven insights and develop the skills to collect, analyze, and interpret data effectively. Our specialized courses cover a wide range of research methodologies, statistical techniques, data analysis and interpretation, and tools used in various industries. Whether you're a student, researcher, or professional seeking to enhance your analytical abilities, our expert instructors will guide you through hands-on training and real-world projects. Build your statistical capacity and Stay at the forefront of data-driven decision-making and gain a competitive edge in your field.

Enroll now to become a proficient data analyst and drive meaningful results through evidence-based research

Explore our Full Portfolio of Training Courses

Our portfolio of more than 200 training courses are currently designed to address the current training needs of our clients incorporating latest trends and internationally accepted best practices, in each distinct subject area.

• Explore All Courses

9 Free Data Analytics Courses for Beginners

Want to try your hand at data analytics? Here’s our hand-picked selection of the best free online data analytics courses available right now.  

Ask any employer and they’ll tell you the same thing: there’s a huge data skills shortage . While traditional data scientists with their in-depth expertise are still much-valued, there’s now a growing need for employees with a broad understanding of data analytics principles, no matter what role they work in.

In short, data analytics is becoming a must-have, 21st-century skill. If you’re hoping to specialize in data analytics or simply future-proof your career, a free online short course is a great way to get started.

This post covers everything you need to consider when choosing a free data analytics course , and rounds up the best options available:

• What is data analytics?
• CareerFoundry
• FreeCodeCamp
• Great Learning Academy
• What’s the difference between a free data analytics course and a paid program?
• What should I look for in a free data analytics course?
• Wrap-up and further reading
• Free data analytics courses FAQ

1. What is data analytics?

Data analytics. It seems like everybody’s talking about it, right? But what exactly is it?

Put simply, data analytics is a scientific approach to decision-making. Sounds simple enough! Truth is, “scientific approach” means understanding a great many nuances relating to data collection, cleaning, and analysis.

Meanwhile, “decision-making” covers many areas, too, from predicting health outcomes to assigning budgets. All this requires careful planning.  

Learn more: Data Analytics for Beginners

What does data analytics actually involve?

Typically, the data analytics process involves many tasks. These usually include:

• defining a problem
• deciding which data to collect to solve that problem
• collecting and cleaning that data
• transforming and modeling it, and finally
• using all this to extract useful information to support making a decision

We’ll grant you, this is a longer definition than our first one, but it’s an honest one! 

Learn more about the data analytics process in this article .  

Why is data analytics so hot right now?

Data analytics has been around in one form or another since humans first started scratching figures onto wax tablets. However, data analytics has only really gained traction with the dawn of digital.

In large part, this is thanks to the huge amounts of data that we now produce every day—whether we’re catching an Uber, making a bank transfer, or simply sending an email.

This explosion of data has coincided with strides forward in technologies that automate much of the data analytics process. This includes machine learning algorithms and complex data storage architectures that allow us to manipulate big data with (relatively speaking) incredible ease.

Now, in the age of AI , data analysis skills and techniques are even more prized than they were before.

2. The best free data analytics courses available right now

OK, time to choose a course! We’ve selected a broad cross-section of options to give you an idea of the various types of free data analytics courses available:

1. Data Analytics Short Course (CareerFoundry)

CareerFoundry’s free, five-tutorial data analytics short course is ideal if you want a light-touch introduction to data analytics. Sign up and you’ll get access to five hands-on, 15-minute lessons from Dr. Humera Noor , Director of Engineering at Eyeo.

The course provides a broad view of data analytics, setting you up to explore the topic further if you choose. Check out the following video to get an idea of how the short course fares:

Unlike many of the courses on our list, there are no hidden costs , making this an ideal, no-pressure option for complete beginners.

The course covers everything from the different types of data analytics roles to a summary of tools and skills you’ll need to develop if you pursue a career in the field, and you can expect to get hands-on with the basics of data analysis.

If you enjoy the short course, CareerFoundry also offers a comprehensive paid Data Analytics Program that will take you from beginner to job-ready data analyst, all backed by the CareerFoundry Job Guarantee . 

2. Data Science for Everyone (Datacamp)

Specifically offering courses for data analytics, DataCamp is a paid course provider.

However, the first module (or ‘chapter’) of their Data Science for Everyone course is completely free. It doesn’t get into heavy technical detail and is perfect if you’re new to the topic.

The course defines what data science is, covering a typical data science workflow. This includes some great interactive exercises to contextualize how data analytics is applied to real-world problem-solving.

Be warned though, once you’ve completed the first chapter, you’ll have to subscribe to access additional content.  

3. Learn to Code for Data Analysis (OpenLearn)

Provided by the UK’s Open University, the OpenLearn platform is jam-packed with content covering everything from astronomy to cybersecurity and, of course, data analytics.

OpenLearn’s courses are renowned for being high quality and many are also free. Once you’ve got the basics down, why not learn to code?

OpenLearn’s free, eight-week coding course, Learn to Code for Data Analysis, provides a solid grasp of basic programming and data analytics concepts and you’ll even be able to write simple analytical algorithms in a programming environment. All this with interactive exercises and a free participation certificate at the end. Bonus!

If you’d like to view more options more towards the developer route, check out our guide to the best free coding courses .

4. Online Data Science Courses (Harvard University)

Ever wanted to say that you studied at Harvard? Now’s your chance!

Harvard University has many data analytics courses that are free to access via EdX. Dive deep into topics from data wrangling to linear regression and machine learning .

While these courses may be better suited to those with a bit of prior knowledge, they cover numerous specialized topics and go into far more detail than most free courses do.

The only downside is that many of them require a considerable time investment, i.e. a few hours a week for several weeks, rather than a crash course in a few hours or days.

You’ll also have to pay if you want a completion certificate. But if you simply want to expand your skills, this remains a great option.

5. Introduction to Data Analytics , offered by IBM (Coursera)

Primarily offering paid courses, Coursera partners with various universities, companies, and training providers.

Because the courses don’t come from a single source, quality and depth do vary, but you can sign up for a free seven-day trial. This is more than long enough to complete one of Coursera’s beginner courses, such as IBM’s Introduction to Data Analytics (an estimated 13 hours).

The course covers what a data analyst does and the tools they use to perform common tasks. And if you want something more detailed, there are thousands of other options available, ranging from data analysis with Python to probability and statistics .

Do be aware, though, once your seven-day trial expires, you’ll need to subscribe. So choose that free course wisely!  

6. Free data science and data analytics courses (Udemy)

Like Coursera, Udemy offers thousands of data analytics and data science courses from various uploaders.

As ever, with these large platforms, some courses are free and some are not, but Udemy’s paid courses tend to be on the more affordable end of the spectrum. While course quality can be a bit hit or miss, you can weed out the less popular free ones by checking user ratings.

Whether you want a broad introduction, such as an introduction to data science using Python or something more specific like how to use the KNIME data analytics platform , then you won’t run short of free options here. Many of the courses are just a couple of hours long, too, meaning you can upskill fast.

7. Introductory Data Science Courses (Dataquest)

Another data-specific course provider, Dataquest offer a wide selection of hands-on data science courses. While Dataquest has a monthly subscription model, you can access some of their content for free, including practice problems.

Helpfully, courses are categorized by career and skill path (and by programming language), helping you target your training. However, you will need to pay the subscription for ad-free access or a completion certificate.

Their catalog offers training in areas like Python for data science to introduction to SQL and databases and data structures in R . There should be plenty to get you started.

8. Data Analysis with Python (FreeCodeCamp)

For those worrying if coding is hard to learn , this site has been incredibly helpful over the years. Simple, accessible and full of hundreds of tutorials, FreeCodeCamp also offers a free online data analytics course too.

It focusses on the Python language, helping you learn about the data analysis process—from reading data, to processing it using the tools Pandas and NumPy, to data visualization using Matplotlib.

All-in-all there are 28 sections, with further parts that add up to a data analysis with Python certification.

9. Python for Machine Learning (Great Learning Academy)

Here’s one for those with 2024 vision. This free data analytics course is not necessarily for beginners, but it’ll give you a relatively gentle welcome into the world of machine learning and AI.

Like CareerFoundry, GreatLearning have been in the online learning space for over a decade, so their online learning platform is pretty smooth. They also have an impressive credibility score on ratings site Trustpilot . You’ll learn mostly through video instruction, with documentation and a Telegram channel for support.

In this Python for ML course you’ll learn about the major packages, what NumPy and Pandas, as well as the usage and functions of machine learning applications. It even comes with a certification as well, should you complete everything.

3. What’s the difference between a free data analytics course and a paid program?

As you can see from the options, trying out a free data analytics course is a great way of dipping a toe in the water to see if you want to dive deeper.

However, there are several important differences between free courses and paid programs that you should know about.

Depth of detail

The intention of a free course is usually to provide a high-level introduction to see if a full program is worth forking out for. Short courses are perfect for getting an all-around taste of the subject.

Meanwhile, a full program (at least, a good one!) will equip you with all the tools you need to pursue a data analytics career.

Course length

Again, being designed as more of a “teaser trailer,” free online data analytics courses are (generally, though not always) much shorter. They usually last anywhere from a few hours to a few days of learning time.

Anything longer than that and you’re moving into paid program territory. In this case, courses may take anywhere from a week to several months to complete, depending on the content’s complexity.

Level of support

Free courses rely on self-guided learning. Meanwhile, full data analytics programs will generally offer guided support, usually in the form of a tutor or mentor, plus help with the job search.

For example, they’ll give you help writing your data analyst resume and building your data portfolio . Some paid courses and bootcamps even provide a job guarantee.

Knowledge level

Free data analytics courses are usually aimed at those with absolutely no experience. This is ideal for learning the basics. Once you’re ready to upskill, though, you’ll need to do a bit more homework!

Paid programs can be more challenging, but once you’ve completed one you’ll have all the skills (and certifications) you need to call yourself a qualified data analyst…and that’s not something you can get from a free course.

4. What should you look for in a free data analytics course?

Many paid data analytics programs tend to provide similar incentives and benefits, as well as covering much of the same core material. That’s because they’re competing with other similar programs.

Free data analytics courses, on the other hand, can differ much more widely. Because they’re not competing for your cash, they can cater to learners’ different requirements.

What you look for in a free online course, then, will depend on what you’re hoping to get out of it. Here are a few things worth thinking about:

• Does it cover the subject matter you want to learn about? Short courses are sometimes focused on very specific topics. Try to find one that interests you.
• Does it combine theory with practice? Hands-on activities always stick in the mind better than simply reading theory. Find a course with rich, engaging material.
• Does it balance simplicity with useful content? While you don’t want a course that’s too complex for beginners, nor should it be so generic that it’s no use to you.
• Does it set you up to progress? Ultimately, a short or free data analytics course should build your confidence to take your learning further.

Before getting started, why not find out what a typical day in the life of a data analyst looks like ?

5. Wrap-up and further reading

So there we have it! Eight free data analytics courses for beginners.

Whether you want to pursue a career as a data analyst or scientist, or simply keep your skills relevant, we hope our list keeps you busy. If you’d like to get more advice on whether a free or paid course is for you, talk to one of our program advisors to get a better idea.

No matter what career path you’re on, adding data analysis to your skillset can only be a good thing . As the labor market adapts to 21 st century needs, data analytics is fast becoming a workplace skill that nobody can do without. Don’t get left behind!

If you’ve already tried some free data analytics courses and are ready for a full program, check out this round-up of the best data analytics courses on the market right now . Meanwhile, to learn more about data analytics, check out the following posts:

• Data bootcamp vs data degree: Which is best for you?
• What are the key skills every data analyst needs?
• How to become a data analyst: The ultimate guide

6. Free data analytics courses FAQ

Now we’ve learned about the options out there, it’s time to answer some of the common questions asked on this subject:

How can I learn data analysis skills for free?

The internet is filled with free data analysis skills courses, with the CareerFoundry Free Data Short Course just one example. Compare options and decide which skills you’d like to pick up, as well as which learning style is best for you.

Is Google data analyst certification free?

You can access the material for the Google Data Analytics Certification freely, but you won’t have access to the course features and will need to pay the Coursera subscription ($39 per month) to take assessments. Learn how it compares to the CareerFoundry Data Analytics Program in this guide .

Can I teach myself data analytics?

If you’re self-motivated and organized enough, it’s possible to teach yourself data analytics using the wide array of free material available online. However, be aware that it will take time, dedication, and hard work to get a data analyst with no experience .

Our websites may use cookies to personalize and enhance your experience. By continuing without changing your cookie settings, you agree to this collection. For more information, please see our University Websites Privacy Notice .

College of Liberal Arts and Sciences

School of Public Policy

Master of arts in survey research and data analysis, sharpen your skills as a researcher and gain experience necessary for a career in the rapidly-growing field of data analysis..

UConn’s online Master of Arts (MA) in Survey Research and Data Analysis is a 30-credit, online program designed to meet the needs of today’s survey researchers from corporate, government, and nonprofit sectors.

The program provides students with extensive training in all stages of the survey research process, including project design and management, questionnaire construction, sampling, methods of data collection, data analysis, and reporting. Our students learn from professors who are leading survey research practitioners that bring real-life examples to the student learning experience.

Through our curriculum, you’ll learn traditional and cutting-edge methodologies that you can use to gather data about attitudes, opinions, behaviors, and demographics. You also gain the training to analyze data and answer complex questions that impact our world today.

Whether you’re performing a market study or analyzing public policy, this flexible degree program will prepare you for career success in the rapidly-growing field of data science and analysis.

Financial Aid

Request Info

Why Choose UConn

Job placements 6-months after graduation.

Learn from Anywhere

Online, flexible, and asynchronous.

Flat-Rate Tuition

All-inclusive program fee: cost is the same regardless of Connecticut residency.

Support Team

Students have access to an “on the ground” support team.

Flexible Schedule

Complete in as few as three (full-time) or five (part-time) semesters.

Outcomes and Careers

Alumni of the MA in Survey Research and Data Analysis program go on to careers in public opinion polling, management, market research, health care, and public policy. Their successes contribute to our school’s national reputation and our ability to attract high-caliber students from around the world.

By earning your MA through the UConn School of Public Policy, you'll join our network of talented alumni – many of whom engage as mentors to current students.

Career Development Resources

Degree Outcomes

Students in the MA in Survey Research and Data Analysis gain the following competencies:

Statistical Techniques and Analysis

• Advanced regression and machine learning.
• Utilizing statistical application packages (SPSS, Stata, and R).
• Quantitative data analysis, analytics, and data visualization.
• Analysis of qualitative data.

Research Planning and Design

• Developing a research plan to address specific questions.
• Constructing qualitative research instruments (cognitive interviews, focus group moderating guides, and ethnographic interviewing guides).
• Constructing standardized survey questions.
• Identifying threats to survey question reliability and validity.
• Designing self-administered and interviewer-administered surveys.
• Sampling methodology, complex samples, and developing sampling plans.
• Reducing error in survey research.
• Understanding models of survey response and cognitive processes.
• Experimental design and survey experiments.

Data Collection and Management

• Data collection through surveys and web scraping.
• Project management, including quality assurance and control.
• Data management, storage, ethics, Institutional Review Board (IRB), and Collaborative Institutional Training Initiative (CITI) training.

Reporting and Communication

• Report on research findings.
• Cultural competence in communicating with diverse groups of survey respondents and consumers of survey research.
• Ability to work and thrive in a diverse workplace.

UConn’s program offers the perfect amount of flexibility for a working student. The curriculum was exactly aligned with my survey research interests while still being comprehensive enough to teach me about other areas within the field.

Shauna Robinson '20 MA Healthcare Writer, Q-Centrix

Graduate placements

Program Requirements

Effective fall 2023, the 30-credit MA program is organized around:

• Core courses (24 credits).
• Elective courses (6 credits).

Students who do not have at least two years of relevant professional experience must complete a three-credit supervised internship as one of their elective courses.

Core Courses

Students must take the following courses:

• PP 5332. Advanced Quantitative Methods
• PP 5376. Applied Quantitative Methods
• PP 5377. Qualitative Methods in Public Policy
• PP 5379. Principles and Methods of Survey Research I
• PP 5383. Principles and Methods of Survey Research II
• PP 5385. Attitude Formation
• PP 5386. Survey Research Analysis and Reporting
• PP 5389. Capstone on the Future of Survey Research

Elective Courses

Students can choose from the following course options:

• PP 5341. Public Opinion and Democratic Processes
• PP 5382 Project Management in Survey Research
• PP 5384. Political Polling
• PP 5387. Surveys for Market Research
• PP 5388. Introduction to Multipopulation Survey Research Methods
• PP 5390. Supervised Internship

Media and Communication Campaigns Track

Students can tailor their degree toward an interest in media, culture, and creative industries through coursework offered by the Department of Communication (COMM). Interested students can choose from the following COMM courses as electives.

COMM 5003. Advanced Communication Research Methods

Research techniques and procedures for the study of communication. Research design, multivariate statistics, and structural modeling.

COMM 5120. Communication Campaigns: Campaign Theory and Planning

Students learn how to conduct interviews and focus groups with members of a target audience, and work with non-profit organizations to design a campaign.

COMM 5150. Crisis Risk and Communication

Research, theory, and best practices in crisis and risk communication.

COMM 5640. Social Media Use and Effects

Research and theory on the social and psychological predictors and effects of social media use as well as social media platforms: their technology, functions, and analysis of collected data.

Sample Course Sequences

The MA in Survey Research and Data Analysis may be completed full time or part time. Below are examples of how students typically complete the program. You can learn more on our course descriptions page .

• PP 5379. Principles and Methods of Survey Research

Total Credits = 9

• PP 5377. Qualitative Methods

Total Credits = 12

• Elective (internship)

Total Program Credits = 30

Total Credits = 6

Additional Options

Joint master’s degree in public administration or public policy and survey research and data analysis.

The School of Public Policy offers students in the survey research and data analysis program the opportunity to couple their MA degree with a Master of Public Administration (MPA) or a Master of Public Policy (MPP) degree.

The joint programs prepare students with functional skills and knowledge in public administration and public policy and at the same time engage them in interdisciplinary study and research related to survey design, data collection, and data analysis techniques. Students must apply to and be admitted by both programs.

Online Individual Graduate Courses in Survey Research

Individuals with a bachelor’s degree or higher can take UConn’s online survey research courses as non-degree students. The "non-degree" designation allows students to take courses for credit without being formally admitted to the University. A maximum of six credits can be taken this way. These courses may be used toward a Graduate Certificate in Survey Research or a MA in Survey Research and Data Analysis. Students must earn a B or better and the courses must be taken within six years in order to be counted.

If you are interested in registering for a course but are not a current UConn student, please submit our online Information Request Form and a member of our team will contact you.

Students apply to the UConn MA in Survey Research and Data Analysis via the UConn Graduate School’s online application.  

Full admissions requirements  

Application Deadlines

Priority decision: Feb. 15 Final Deadline: May 15

Final Deadline: Nov. 1

For more information about the UConn MA in Survey Research and Data Analysis, please contact:

Bryan Callender

Outreach Coordinator, School of Public Policy

Request Information

Frequently asked questions, do i need to have an undergraduate degree in order to take survey research courses.

Yes. Proof of a bachelor’s degree from an accredited four-year institution is required to register for classes. Graduates from all fields and majors are eligible to take courses in our program.

Do I need to be admitted to a degree or certificate program to take courses?

No. As a non-matriculated student, you may take up to six credits of coursework. After this limit is reached, you may apply for admission to the MA program to continue your studies. Non-matriculated students must have an earned bachelor’s degree.

Do I need to take the GRE?

No. The MA and certificate programs do not require any standardized test for admission.

What programs in survey research does UConn offer?

The School of Public Policy offers two online survey research programs: a 30-credit Master of Arts in Survey Research and Data Analysis and a 12-credit Graduate Certificate in Survey Research .

Can I start in the certificate program and switch to the master’s program later?

Yes. As long as you are accepted to the master’s program before your Graduate Certificate in Survey Research is conferred, the credits you have earned can be applied toward the MA in Survey Research and Data Analysis program. You cannot earn both a certificate and a master’s degree.

Who should write my letters of recommendation and how should they be submitted?

Your letters of recommendation should be written by people who know you and your work very well. Your writers may be current or past supervisors, college professors, or professional colleagues. For current and recent undergraduates, we recommend that at least two letters are from recent professors.

How do I submit letters of recommendation and transcripts?

Recommending individuals can upload their letters when prompted via email by the application system. Individuals can also elect to email [email protected] directly to submit their letter. Applicants may not directly submit their letters of recommendation.

What are the admissions requirements for the Master of Arts in Survey Research and Data Analysis?

See the full list of application requirements on our Apply Now page .

What are the application deadlines?

For fall admission, the priority deadline is Feb. 15. The final deadline for fall is May 15. For spring admission, the deadline is Nov. 1.

How are admissions decisions made for your programs?

The admissions committee takes a holistic approach when reviewing your application. We will evaluate all aspects of the application including types of undergraduate courses taken, letters of recommendation, your personal statement and résumé (professional experience and work choices; volunteer work and honors received). It is very important to us that there is a right “fit” with your career goals and our curriculum and program.

After the School of Public Policy reviews applications, the Graduate School then audits the student’s application to verify the transcript information. Once both reviews are complete, decisions are sent to applicants by the Graduate School.

How much do survey research courses cost?

The cost is the same for residents, non-residents, and international students. The cost for 2023-2024 is $1,000 per credit, all-inclusive for tuition and fees. One 3-credit class totals $3,000.

• Total cost for the 12-credit certificate: $12,000
• Total cost for the 30-credit Master's degree: $30,000

Does the MA in Survey Research and Data Analysis program offer financial assistance or graduate assistantships?

No. The program does not offer financial assistance in the form of graduate assistantships.

Is financial aid (such as student loans) available?

Please note that financial aid may not be available for all students. Graduate students who are enrolled in a graduate certificate program (only) are not eligible to receive federal financial aid (Federal Stafford Loan and Graduate PLUS Loan funds). Students enrolled in these programs may wish to consider UConn’s payment plan , or Alternative (Private) Loan financing . Financial aid is administered through the University of Connecticut Student Financial Aid Office. They can be reached at 860-486-2819 or at the Office of Student Financial Aid Services website .

International Students

Can international students obtain a student visa if admitted to uconn’s ma in survey research and data analysis.

No, student visas cannot be issued for online education. International students can only participate if doing so from their home country.

Are tests of English proficiency required?

Yes. It is very important to our program that international students have mastered the English language before attending our program. All of our classes are taught in English and we expect strong English skills from international students. Applicants from non-English speaking countries should submit their Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) score with their application materials. A strong applicant will have a TOEFL score of at least 600 (paper test), 250 (computer test), or 100 (internet test); or an average overall band IELTS score of at least seven.

Online Learning

Are courses offered in a traditional face-to-face classroom environment as well as online.

No, all survey research courses are offered online. The School of Public Policy offers traditional face-to-face courses through many of our other programs .

How are online courses different from on-site courses?

Our online courses meet the same academic standards as on-site courses at UConn. The primary difference is that all of the assignments, communication, and class participation are completed using various technologies. All of the traits required to be successful in graduate school—effective time management, the ability to work independently and collaboratively, and possessing the abilities to write clearly and think critically are equally important for the online courses offered by the School of Public Policy.

Does online mean that I will not have contact with professors and classmates?

No. Each course is an integral part of our online community, which means that there is an opportunity for significant interaction among students and faculty. Our faculty contact students by email or telephone; participate actively in discussion boards and chat rooms; provide extensive feedback on assignments; and even conduct online office hours.

Is the MA in Survey Research and Data Analysis conferred by the School of Public Policy identified as being online?

No. The transcript identifies the degree as a Master of Arts in Survey Research and Data Analysis. Neither the transcript nor the credential refers to it as being an online course of study.

When do classes start and when are they held during the year?

Classes are held in the fall and spring as specified by the University of Connecticut academic calendar . Summer classes may also be offered.

Can I study full-time or part-time?

The majority of our students are professionals who take one or two three-credit courses each semester, though a few choose to study full time and take three or four classes each semester. Because the courses are billed at a flat fee-per-credit rate, the part-time/full-time designation does not have an impact on your bill beyond the cost-per-credit calculation.

Once you have matriculated as a master’s student, you must take at least one course in each consecutive fall and spring semester in order to stay active in your program and avoid reinstatement fees and the need to reapply.

How are the courses delivered?

The online courses are delivered asynchronously. Asynchronous refers to fulfilling course objectives through activities that do not need to take place at the same time for all students. An example of an asynchronous activity is the discussion board or threaded discussion, where students post to the board when it is convenient to them as long as the activity is completed by a predetermined date.

Rawls College of Business

Master of science in data science.

Ranked the No. 3 best online, non-MBA program in the nation in 2022 , the Rawls College Master's in Data Science (MSDS) program provides graduates with the technical expertise needed to lead in the digital frontier. Through our 36-hour, STEM-designated program, learn how to manage, analyze and understand complex data to make strategic decisions. Upon graduation, you will have the skills and knowledge needed to be an agile data scientist capable of making impactful decisions across a variety of business settings and industries.

program highlights

Flexible format, on-campus or online classes.

Options to complete coursework on campus or online allow you to choose the course modality that best fits your personal needs.

One or Two-Year Options

Complete your degree in as little as a year, or take fewer classes per semester by selecting the online, two-year option.

Optional Practical Training (OPT) Eligible

International students may qualify to work in the U.S. for up to three years after receiving their degrees.

Cutting-Edge Curriculum

We prioritize the real-world application of knowledge and skills to best support students who want to accelerate their careers in data science, business analytics, business intelligence and big data fields. Through our comprehensive curriculum, you will learn how to use advanced technologies and statistical methods to manipulate data and translate findings into actionable organizational strategies.

Classes include foundational building blocks for today's data scientists:

Statistics for Data Science

Scripting Languages

Database Concepts

Data Technology Environments

Big Data Strategy

Business Intelligence

Multivariate Analysis

Time Series Analysis

Simulation & Optimization

Machine Learning

Decision Theory and Business Analytics

Big Data Security

program format

The MSDS program requires 36-credit hours, consisting of specialized data science courses. This is a lock-step program, requiring students to take classes in a specific order, as concepts build on each other. The program begins in the summer, and summer courses are four to five weeks in length. Fall and spring courses are seven to eight weeks in length. Coursework for the one-year program can be completed on campus or online. The two-year program is available online only.

Working professionals experience their core courses together, creating a stimulating cohort-based learning environment. During your tenure in the program, you will build relationships with peers, emanating from diverse backgrounds and industries, resulting in a larger professional network upon graduation.

View sample degree programs »

Our MSDS faculty members include engaged technology practitioners who utilize real-world experience to inspire their instruction. Their areas of expertise include computer-aided decision making, information requirements determination, operations management, health care analytics, information economics and more.

Meet our faculty »

class profile

At Rawls College, we believe diversity drives opportunities for collaboration and learning. Together, we benefit from the many perspectives, skills and experiences our students from all over the world bring to our learning environment.

supporting your success

Rawls Career Management Center

Whether you are looking to switch careers or advance on your current path, the Rawls Career Management Center (CMC) is dedicated to supporting your success. The staff in the CMC can help you explore professions and industries, learn strategic career advancement techniques, and connect you with top employers.

Techsan Connection

The Techsan Connection is a free, online platform for Texas Tech alumni. Through the platform, alumni can apply to jobs, reconnect with fellow classmates, network with industry professionals and volunteer to mentor current students.

The admission process is the first step toward earning your degree. We will work closely with you to ensure your application process is personal, simple and successful.

Application Requirements

While no prior work experience is required, applicants must have a bachelor's degree. Most applicants have an education or work background in computer science, management information systems, science, engineering, or similar fields. Basic knowledge of computer programming software such as R, SQL, and Python will be beneficial throughout the program's coursework. Additionally, applicants will benefit from prior completion of coursework in calculus, statistics and probability.

Unofficial Transcripts

Applicants must submit unofficial transcripts from any degree-awarding college or university, as well as any post-secondary institution attended. 

Applicants must submit a detailed current resume, indicating professional work experience—including start and end dates (month and year) for each position held. Provide accomplishments and skills acquired, including managerial experience.

GMAT Scores

The summer 2023 intake will not require a GRE or GMAT for application, but submission of scores are encouraged. 

We don't have a set minimum or maximum requirement for test scores. We review students holistically taking the application in its entirety into consideration.

English Proficiency for International Students

All international applicants must provide proof of English proficiency before their applications can be considered for admission. Only your most recent measure of English proficiency is considered for admission purposes. This test is waived only for graduates of U.S. universities or universities in English proficiency-exempt countries. Applicants who have completed at least two consecutive years at a college or university in the U.S. or in an English proficiency-exempt country are also exempt from the English proficiency requirement.

Application Deadlines

Summer Entry: May 1

International students are encouraged to apply at least six months in advance when possible.

student resources

• Prospective Students
• Current Students

Program Questions

[email protected] 806.742.3184

Cy Cawthron 806.834.1069

Events@Rawls

Professional mba weekend classes.

Saturday, June 1, 2024 - Sun , June 2, 2024 (all day)

Where: Rawls College of Business

Saturday, June 29, 2024 - Sun , June 30, 2024 (all day)

Where: Center for Business Communications Room 139

Contact TTU

• Like Rawls College of Business on Facebook Like Rawls College of Business on Facebook
• Follow Rawls College of Business on X (twitter) Follow Rawls College of Business on X (twitter)
• Subscribe to Rawls College of Business on YouTube Subscribe to Rawls College of Business on YouTube
• Follow Rawls College of Business on Flickr Follow Rawls College of Business on Flickr
• Follow Rawls College of Business on Instagram Follow Rawls College of Business on Instagram
• Connect with Rawls College of Business on LinkedIn Connect with Rawls College of Business on LinkedIn

More From Forbes

5 free online data analysis courses in 2024.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

Data analysis is one of the most in-demand skills of 2024, and this trend is predicted to remain ... [+] until at least 2032

For the next four years, big data analytics is expected to be one of the core drivers of economic growth, according to projections by the World Economic Forum's Future Of Jobs report 2023. Analytical thinking leads the way in being the number one skill on the rise, increasing in demand this year, as per the findings of the same report, while data analysis appears sixth in LinkedIn top 10 Most In-Demand Skills of 2024 report .

Organizations have many priorities which make data analytics a worthwhile skill to learn, with AI (artificial intelligence), big data, and a strong focus on data-driven decision-making and business strategy being a few of them.

This is further evidenced by the fact that the job demand for data scientists is predicted to continue soaring until 2032 at least, to as much as 35%, according to the U.S. Bureau of Labor Statistics—that's much faster than the average job growth rate in North America.

Thanks to the demand, data analysis careers pay exceptionally well, leading to six figure incomes, with data scientists making as much as over $119,000 per year on average , as evidenced by current average salary figures on Salary.com

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024, why work in data analysis.

As if all of the above are not enough, here are two more excellent reasons to pursue data analytics as a career:

First, data analysis is a highly transferable skill, and is a function that is needed in every industry globally, similar to careers such as project management, which also shares the same characteristic of global demand and transferability across industries. You could work in a wide range of sectors, from healthcare, to finance, to e-commerce, to marketing, and of course, technology.

Second, now is a great time to learn data analysis, because of the acceleration and advancement of AI (artificial intelligence), which provides a helpful boost in augmenting your work and ensuring accuracy. Your work within data can come at a critical time when an organization is seeking to prepare its data for use in its internal AI tools and processes.

Free Online Courses On Data Analytics

Whether you're seeking to reskill or upskill, you may not have much spare cash to invest in a course or to pursue a degree within this field. However, there may be some free courses that can help:

1. MIT's How To Process, Analyze And Visualize Data

OpenCourseWare is a an online platform that hosts courses from many of the world's leading universities, including Ivy League schools, for free, using a Creative Commons Licence. One such course, from the world-renowned MIT (Massachusetts Institute of Technology) has lectures and course materials on How To Process, Analyze, And Visualize Data.

Honing in on an in-demand skill leads to a lucrative salary and diverse career opportunities

2. Google Advanced Data Analytics Professional Certificate

This free online course, provided through Coursera, comes with an extra advantage—a professional certificate. Google career certificates are widely recognized by employers around the world, and undertaking one is an excellent way to fast-track your career development and be invited to job interviews. The advanced version teaches Python, machine learning, Jupyter Notebook, and Tableau software, amongst other skills, all of which you can add to your resume's skills section, thus improving your chances of being hired.

3. Google Data Analytics Professional Certificate

This course is aimed at guiding you through data analysis at the beginner level, and is a great introduction and starting point for further studies, such as the advanced course mentioned above. As with all Google Career certificates offered via Coursera, you have the option to apply for financial aid to cover the cost of your course, subject to eligibility.

4. HarvardX's Data Science Professional Certificate

Harvard University has partnered with edX to provide free courses to learners on the edX platform. Although you need to pay for the entire training with the certificate, if you click into each module (course) within the certificate program on edX, it does provide you with the option to learn the course materials for free. To get the certificate, you would need to upgrade.

5. AWS Data Analytics

Amazon Web Services (AWS) provides a range of online certifications and ongoing professional development training, tailored to specific technical career paths. Once of these learning paths is their data analytics training . They provide this through free virtual training webinars, and individual courses that you can study on their own or as part of their data analytics learning plan, such as Data Analytics Fundamentals, or Best Practices for Data Warehousing.

You can learn from the best Ivy League schools—without ever attending Ivy League in person or paying ... [+] enormous student fees

Overall, choosing to pursue a career in data analysis is a decision you will not regret. If you commit to ongoing professional development and exploring new training opportunities, you too, can experience the lucrative salaries, remote work opportunities, flexibility, and diverse opportunities that others within this profession are already enjoying.

• Editorial Standards
• Reprints & Permissions

Post Graduate Program in Data Analytics, Nairobi

Our Data Analytics courses in Nairobi will expand your skill set and increase your career potential. Offered in partnership with Purdue University and collaboration with IBM, our data analytics training in Nairobi uses expert faculty and real-world projects to bring concepts to life.

EMI Starting at

• Admission closes on 29 Apr, 2024
• Program Duration 8 months
• Learning Format Online Bootcamp

World’s #1 Online Bootcamp

Awarded best data analytics program by career karma.

• 4.5 Reviews 433
• 4.4 Reviews 843

Why Join this Program

Purdue’s academic excellence.

Joint certificate from Simplilearn and Purdue University.

IBM’s Industry Prowess

Obtain IBM certificates for IBM courses and get access to masterclasses by IBM

Career Assistance

Build your Resume and highlight your profile to recruiters with the career assistance services.

Hands-on Experience

14+ industry-relevant projects from the likes of Google,Zomato and IBM and many more

FOR ENTERPRISE

Data analytics course overview.

With our Data Analytics courses in Nairobi, students gain a thorough understanding of critical data analytics and data science technologies. The course covers Tableau, statistics, Python, R, Power BI, and SQL. This Data Analytics training in Nairobi gives graduates the skills needed to market themselves as data analytics professionals.

Key Features

• Post Graduate Program certificate and Alumni Association membership
• Exclusive hackathons and Ask me Anything sessions by IBM
• Live sessions on the latest AI trends, such as generative AI, prompt engineering, explainable AI, and more
• Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
• Master Classes delivered by Purdue faculty and IBM experts

Data Analytics Certification Advantage

The Data Analytics Course in partnership with Purdue University leverages Purdue’s academic excellence in Data Analytics & Simplilearn’s collaboration with IBM, providing a comprehensive view of the domain.

Partnering with Purdue University

• Receive a joint Purdue-Simplilearn certificate
• Masterclasses by Purdue faculty
• Purdue University Alumni Association membership

Program in Collaboration with IBM

• Industry-recognized certificates from IBM
• Industry masterclasses conducted by IBM
• Exclusive hackathons and Ask Me Anything (AMA) Sessions with IBM leadership

Data Analytics Course Details

Fast track your career with this comprehensive Data Analytics Course curriculum, which covers the concepts of Statistics foundation, analyzing data using Python and R languages, interacting with databases using SQL, and visualizing the data using Tableau and Power BI.

Learning Path

Get started with this Data Analytics Program in partnership with Purdue University and explore everything about this Data Analytics certification. Start your journey with the preparatory courses on Statistics and an Introduction to Data Analytics along with SQL training.

Make the Data Analytics foundation strong with the basics of statistics fundamentals, and techniques as the first step in the Data Analytics Program.

This course gives you the information you need to successfully start working with SQL databases and make use of the database in your applications. Learn the concepts of fundamental SQL statements, conditional statements, commands, joins, sub-queries, and various functions to manage your SQL database for scalable growth

With this Data Analytics Program with the Python Bootcamp program, you will learn programming fundamentals, how to analyze data in Python, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.

Discover R programming with this Data Analytics Program. Learn how to write R code, utilize R data structures, and create your own functions.

The next step to becoming a Data Analyst is learning R—the most in-demand open-source technology. R is a powerful Data Science and analytics language, which has a steep learning curve and a very vibrant community. This is why it is quickly becoming the technology of choice for organizations who are adopting the power of analytics for competitive advantage

This Data Analytics Program covers Tableau Desktop 10 training that will help you develop various skills in the powerful data platform, including building visualizations, organizing data, and designing dashboards.

At the end of the Data Analytics Program, bring your newly acquired Data Analytics skills together with a hands-on, industry-relevant capstone project that compiles every course into one portfolio-worthy capstone.

• Aligned with PL-300: Microsoft Power BI Data Analyst certification
• Simplilearn's certification training explores Microsoft Power BI concepts.
• Topics include Power BI Desktop layouts, BI reports, dashboards, DAX commands, and functions.
• Learn to experiment, refine, prepare, and present data with ease.
• Explore comprehensive Power BI training for hands-on applied learning.
• The course adopts a practical approach to help you gain expertise.

Attend an online interactive masterclass and get insights about advancements in technology/techniques in Data Science, AI, and Machine Learning.

Attend this interactive, online industry master class to gain insights about cutting edge Data Analytics advancements and techniques.

Attend this live online immersive masterclass on Generative AI designed to empower participants with the knowledge and skills to harness its incredible potential. These cutting-edge masterclasses are conducted by industry experts and delve deep into the world of AI-powered creativity, helping you to understand various concepts & topics related to generative AI.

+65 31 585082

Skills covered.

• Data Analytics
• Statistical Analysis using Excel
• Data Analysis Python and R
• Data Visualization Tableau and Power BI
• Linear and logistic regression modules
• Clustering using kmeans
• Supervised Learning

Tools Covered

Industry Projects

Rating prediction for apps on google play store.

Make a model to predict the app rating, with other information about the app provided to boost its visibility.

Demand Forecast for Walmart

Predict store sales and demand, factoring in economic conditions for the retail giant Walmart’s stores across the United States.

Designing a Sales dashboard in Excel

Explore Excel to analyze sales based on various product categories.

Online Car Rental Platform

Build an online car rental platform where customers should be able to view the available cars that can be rented based on categories

Comparison of Regions Based on Sales

Build a dashboard to visualize the region-wise sales performance and suggest the necessary improvements.

Identify the causes and develop a system to predict heart attacks in an effective manner using the datasets on the factors that might have an impact on cardiovascular health.

Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Program Advisors and Trainers

Program advisors.

Patrick J. Wolfe

Patrick J. Wolfe, an award-winning researcher in the mathematical foundations of data science, is the Frederick L. Hovde Dean of the College of Science at Purdue University and was named the 2018 Distinguished Lecturer in Data Science by the IEEE.

Program Trainers

Christopher Hemmel

Business data analyst.

Sonal Ghanshani

Consultant and corporate trainer.

Shubham Pandey

Strategy consultant.

Data Scientist | Corporate Trainer

Join the data analytics industry.

Data science and analytics jobs are predicted to increase 28% by 2020, according to an IBM report. The global analytics market is expected to grow by $132.9 billion during the period of 2016 to 2026 (Source: Market research future report)

Expected New Jobs For Data Science And Analytics

Annual Job Growth By 2026

Average Annual Salary

Companies hiring Data Analysts

Batch Profile

This Data Analytics Course caters to working professionals across industries. Learner diversity adds richness to class discussions and interactions.

Alumni Review

I had a great learning experience, and the faculty was very encouraging. The projects were vital in helping me understand whatever I learned during the course. I have also gained a lot of great professional contacts through this course. The course was very well structured, too.

Rose Ashford

What other learners are saying

Admission details, eligibility criteria.

For admission to this Data Analytics Certification Course, candidates:

Admission Fee & Financing

The admission fee for this Data Analytics Course is $ 2,790, which covers applicable certification charges & Alumni Association membership fee.

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay in Installments

You can pay monthly installments for Post Graduate Programs using Splitit payment option with 0% interest and no hidden fees.

We provide the following options for one-time payment

• Credit Card

Program Cohorts

Next cohort.

6 May, 2024

11 May, 2024 - 17 Nov, 2024

16:30 - 20:30 EAT

Weekend ( Sat - Sun )

Data Analytics Certification Course FAQs

What is data analytics.

Just about everything is data-driven these days, from market research and sales figures to expenses and logistics. To most people, this information can be overwhelming and daunting. It can be difficult and time-consuming to sort through it all and know what’s important, what isn’t, and what it all means. This is where Data Analysts come into the picture: they take this information and do thorough data analysis and  turn it into useful information for businesses, which allows them to make more informed decisions in the future.

Why is there a demand for Data Analytics courses in Kenya?

Data analytics has spread its wings across sectors and industries, including, healthcare, finance, retail, education, eCommerce, and more, making it a lucrative field. And in order to grab the best opportunities in the field, a world-renowned Data Analytics course in Kenya (especially from a prestigious university) acts as a career catalyst, making this course one of the most in-demand certification courses.

What should I expect from this Data Analytics Course?

As a part of this Data Analytics Course, in collaboration with IBM, you will receive the following:

• Simplilearn-Purdue University Joint Certificate.
• Industry recognized certificates from IBM (for IBM Data Analytics modules) and Simplilearn
• Purdue Alumni Association membership eligibility.
• Lifetime access to all core eLearning content created by Simplilearn

How long does it take to learn Data Analysis?

The time taken to learn data analytics varies from person to person. It depends on your dedication to studying, prior knowledge of the field, and work experience in data analytics. While some of the concepts may take a few days, others may take a couple of months to grasp. When you take our Data Analytics Course, you should apply the concepts you learned to real-world use cases to gain practical exposure and reinforce your learning.

What is the salary potential of a Data Analytics Professional?

The average annual Data Analyst job salary is over $61,000 per year.

How do I know if the Data Analytics is right for me?

Learning new skills and expanding your knowledge is always a plus point. This Data Analytics Course is developed in collaboration with Purdue University, a perfect blend of world-renowned curriculum and industry-aligned training, which makes the Data Analytics Course just the right one for you!

What are the eligibility criteria for this Data Analytics Course in partnership with Purdue University?

For admission to this Data Analytics Course , candidates:

• Should have a bachelor's degree in any discipline with an average of 50% or higher marks
• With a non-programming background can also apply
• Having prior work experience is not mandatory

Is there any minimum education qualification required to apply for this Data Analytics Course?

Yes, you are supposed to have a bachelor’s degree with an average of 50% (or higher) if you wish to enroll in this Data Analytics Course.

What is the admission process for this Data Analytics Course in partnership with Purdue University?

The admission process for this Data Analytics Course consists of three simple steps:

• All interested candidates are required to apply through the online application form
• An admission panel will shortlist the candidates based on their application
• An offer of admission will be made to the selected candidates and is accepted by the candidates by paying the fee

Will I become an alumni of Purdue University after completion of the Data Analytics Course?

You will get eligibility for Purdue Alumni Association Membership after completing the Data Analytics Course.

How do I earn the Post Graduate Program certificate in Data Analytics?

Upon completion of the following minimum requirements, you will be eligible to receive the certificate that will testify to your skills as an expert in Data Analytics.

What are the top modules included in this Data Analytics Course?

You’ll find the best-in-class modules covered in this Data Analytics Course. The list includes:

• Analytics and Programming Foundation
• Data Analytics with Python
• R Programming for Data Science
• Data Science with R
• Data Analyst Capstone

Is there any financial aid provided for this Data Analytics Course?

To ensure money is not a barrier in the path of learning, we offer various financing options to help make this Data Analytics Course more financially manageable. Please refer to our “Admissions Fee and Financing” section for more details.

Will any preparation material be provided to get started in this Data Analytics Course?

Once you make the first installment of the fee, you will be given access to a preparatory program with eight to 10 hours of self-paced learning content in the form of videos. You will have to go through the assigned program before attending the first class.

What tools and languages do we learn in this Data Analytics Course?

With the growing popularity of data analytics, several tools have been thought in this Data Analytics Course. Some of the important Data Analytics tools that provide various advanced features include Tableau, Power BI, SAS, QlikView, RapidMiner, and MS Excel.

Is this Data Analytics Course taught online? Do I need to attend any physical classroom sessions?

This Data Analytics Course is completely online. You can access the Data Analytics Course material anytime and anywhere with a computer or smartphone connected to the internet.

How will my doubts/questions be addressed in this Data Analytics Course?

We have a team of dedicated admissions counselors who can guide you as you apply for this Data Analytics Course.

I don't have any prior knowledge in coding, can I make a career in Data Analytics?

Yes. This Data Analytics Course will teach you the fundamentals of programming languages, statistics, and industry-standard techniques from scratch to build up your foundational knowledge and enhance your analytics career journey. These concepts will make you a master in data analytics.

What is Global Teaching Assistance?

Our teaching assistants are a dedicated team of subject matter experts here to help you get Post Graduate Program Certificate in Data Analytics on your first attempt. They engage students proactively to assure the course path is followed and to help you enrich the learning experience, from class onboarding to project mentoring and job assistance.

Do I need to follow the mentioned learning path for this Data Analytics Course?

We highly recommend that you follow the Data Analytics Course curriculum in the same order as listed in the learning path as the initial concepts are used in lessons that follow it.

Who are the instructors for this Data Analytics Course and how are they selected?

All of our highly qualified Data Analytics instructors are Business Intelligence experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain part of our faculty.

Will I be able to access the content after completion of the Data Analytics Course?

Yes, you can access the course content even after the completion of the Data Analytics Course.

I am not able to access the Data Analytics Course. Who can help me?

Contact us using the form on the right side of any page on the Simplilearn website, select the Live Chat link, or contact help and support .

My current role does not include exposure to data. Does it make sense for me to opt for this Data Analytics Course?

Data is ruling businesses around the world. The more data-driven you’re, the more beneficial it is for your organization. By taking insights from data, you can make meaningful decisions, plan strategies, and help your business achieve its goal faster. Enrolling in this extensive Data Analytics Course is definitely going to be an advantage, and nothing less.

I am not from a technical background. Can I still join this Data Analytics Course?

Yes, you can join Data Analytics Course even if you do not belong to a technical background. However, having a basic knowledge of programming languages and mathematics will be beneficial.

Can I enroll in a Data Analytics Course if I don't have any prior knowledge in Data Analysis?

Yes, you can enroll in the Data Analytics Course even if you don’t have any prior knowledge since this course will take you through the fundamentals to the top of the ladder, where you learn all the advanced critical Data Analytics skills.

What is covered under the 24/7 Support promise?

We offer 24/7 support through email, chat, and calls. We have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Data Analytics Course.

If I want to cancel my enrollment from this Data Analytics Course, can I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the program price after deducting an administration fee. To learn more, you can view our Refund Policy .

This Data Analytics Course is offered on a non-credit basis and is not transferable to a degree.

Can I get a sealed transcript for World Education Services (WES) at the end of the program?

These do not include any transcripts for WES, this is reserved only for degree. We do not offer sealed transcripts and hence, our certificates are not applicable for WES or similar services.

What are the benefits of this Generative AI Masterclass?

These masterclasses are delivered in the form of live virtual sessions by experienced industry experts. This delves deep into the world of AI-powered creativity, helping you understand multiple concepts & topics related to generative AI such as effective prompt engineering, ethical considerations in GenAI, and much more. 

You will gain exposure to the world of Gen AI, some of its practical applications, some of the latest advancements in the field and much more - thus setting you apart from your competitors and helping you stay ahead in your career.

Related Programs

Post Graduate Program in AI and Machine Learning

Post Graduate Program in Data Science

Find data science & business analytics programs in nairobi.

• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

Top 11 Data Analytics Courses In India In 2024 (Updated)

Data analytics is the course where we learn the compilation of various sets of data. in easy language, it is said to collect, store, and analyze the data set for perfect business goals. we will cover the top data analytics courses in india here to give you a clear idea of what to expect from training programs on data analysis. .

In India, We have all been facing employment issues much from a year back. People are moving towards trying out something new with their skills for getting a new and best job opportunity for themselves. We all are having a certain career goal; we try to fix certain goals for ourselves. So, that we can try hard to achieve it.

Here we see that various types of courses in the market going around and within all the types of Courses. I found the perfect course which has set its mark and left behind all the courses that are Data Analytics courses. This course is a masterpiece that you will get to know when you will learn it and get all the knowledge around it.

The main and most important purpose of each and everyone is to get a nice future opportunity, which you will grab by doing this course. The opportunities you will get in corporate sectors, government sectors, and businesses also. In every field, we need a smart data analytics master, who can help to achieve the future goals of the companies, Businesses, or wherever it is needed.

• Content Writing Courses in India
• Digital Marketing Courses in India
• GST Certification Courses in India
• Technical Writing Courses in India
• SEO Courses in India
• Business Accounting and Taxation Courses in India
• Financial Modeling Courses in India
• Creative Writing Courses in India
• Business Analytics Courses in India
• Tally Courses in India
• Investment Banking Courses in India

The below mentioned the Top 11 Data Analytics Courses provided by the different institutes, mentioning both classroom and Online learning classes, which one is favorable for the candidates who want to learn the Data analytics courses. The list is as follows:

1. iim skills.

IIM SKILLS is one of the top listed ed-tech companies in the world. They have a wide range of skill development & finance related courses. Data analytics master course is one of their most preferred course. The course curriculum is best designed & is fully practical-oriented.

Course Name & Details:

Data Analytics Course

The course is a 6 months course with additional 2 months of optional internship program which is unpaid for professional experience.

Fees INR 49990 Rs. + 18% taxes.

Course Modules:

The course fees is inclusive of exam & certification cost.

Live training online sessions on weekdays/weekend.

Tools Knowlege You Gain:

Why iim skills is the best choice:.

• All the courses are online which offers flexibility in learning.
• IIM SKILLS offers flexible schedule, which means you can learn on weekends or weekdays sessions as per your availability without compromising on your professional or personal commitments.
• They have the best mentors who have subject matter expertise.
• The course is fully practical & skill oriented.
• Master certification plus assured internship of 2 months for professional exposure.
• Fress demo sessions.
• 100% money back after your 1st session if you are not convinced with the training.
• Lifetime access to the learning materials which are timely updated.
• 24*7 support and career guidance.
• 10% of group discount if you register with your friends.

Course Eligibility Criteria:

The data analytics course with IIM SKILLS is a customized course and is best for applicants irrespective of their backgrounds. Which means any fresher, graduate, UG, post-graduate, IT professionals, Banking, Sales or network managers can enroll for the course. The course is also best designed for mid-level & senior-level professionals in the industry.

Professional Courses from IIM SKILLS

• Investment Banking Course
• Financial Modeling Course
• Digital Marketing Course
• Technical Writing Course
• Content Writing Course
• Business Accounting And Taxation Course

Contact Details For More Information:

+919580740740,  [email protected]

Dig in here and find the advanced courses:

• Online Data Analytics Courses
• Data Analytics Courses in Thane
• Data Analytics Courses in Delhi
• Data Analytics Courses in Kolkata
• Data Analytics Courses in Bangal0re
• Data Analytics Courses in Pune
• Data Analytics Courses in Hyderabad

2. Jigsaw Academy

Jigsaw Academy has made its excellent remark by skilling the people by teaching courses like Data analytics, Data Science, Cybersecurity, Cloud Computing, and Software development. This academy is certified by IIM Indore and it provides all the required courses under the certification by IIM Indore, Manipal University, Hacker U, and Jigsaw Academy.

The Data Analytics course is provided by the Jigsaw academy with the name of Full-stack Data science program. The Course covers up important modules like Data interpretation, Data manipulation, Data integration, Descriptive Analytics, Predictive analytics, statistical analysis, and Data visualization.

The tools which are covered while teaching the program are python, Tableau, Anaconda, SQL, Tensor flow, Kesar.

The mode of class is online by the institute. Get the hands-on learning experience, Learn from the experts, Assignment based classes.

Dual Certificate awarded by the NASSCOM Future skills and Jigsaw academy at the end of the completion of the course.

Duration : 4 months

Eligibility:

• Basics in programming and statistics
• Data science enthusiastic
• Preferably Graduates

Fees : INR 48000

Full-stack Data scientist.

Learn proactive Social Media Marketing with the  Top  Digital Marketing Course

• Data Analytics Courses in Mumbai
• Data Analytics Courses in Gurgaon
• Data Analytics Courses in Noida
• Data Analytics Courses in Ahmedabad
• Data Analytics Courses in Agra
• Data Analytics Courses in Bhopal
• Data Analytics Courses in Chennai

3. AnalytixLabs

Analytixlabs is an institute that offers a wide range of Data Analytics Courses. This institute ranked number 1 and is also awarded as India’s best institute for Data Analytics Courses.

The institutes provide job-oriented attractive learning to candidates who do not have prior knowledge about Data analytics.

The Course name is Business Analytics 360 which covers all the modules of the Data Analytics and the add-on modules also.

The Course covers in their curriculum taught respective tools like Data Visualization and analytics, Data Science with R, Python, Tableau, SQL, Predictive Modeling, and intro to ML and Data analytics with VBA (e-learning).

The skills are covered in this course MIS Reporting analytics, Statistical analytics, and modeling, Predictive modeling, Reporting Analytics, Data blending, and manipulation.

The Institute provides self-paced e-learning, classroom & Boot camp, and Interactive online class. It also covers up the course by giving the assignment to the candidate and projects. The course comes under certification by the side of the institute after completion of the full course.

Duration : 99 Hours+66 hours of learning.

Beginners with a qualitative background in business management, Maths, and Finance.

Fees: INR 32000 ONWARDS.

Analytics Consultant, Business Analyst, Data Scientist, Data Analyst, MIS Analyst, Statistical Analyst.

Find out here the advanced courses for faster learning:

• Data Analytics Courses in Chandigarh
• Data Analytics Courses in Coimbatore
• Data Analytics Courses in Kochi
• Data Analytics Courses in Indore
• Data Analytics Courses in Jaipur
• Data Analytics Courses in Lucknow
• Data Analytics Courses in Kota

4. Simplilearn

The Simplilearn conducts the Data analytics courses and Data sciences courses with the Joint partnership with IBM. The main role of IBM is to make students ready for the corporate industries in the field of Data analytics. IBM is a well-recognized university that makes most of the investment in Research and development.

The Course name is the Data analyst Master Program. This course will teach you to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extracts, and forecasting. The learners will get to learn Data visualization with Tableau and Power BI. The tools covered in this program are python, pandas, NumPy, Scipy, Power BI, R, and Tableau.

The addition to the course is the Access to the IBM Cloud Lite account and Industry recognized Certification of Master program on Data analyst from Simplilearn. It has the job assist also which will prepare you for the job means make you job-ready.

Duration: 120 plus hours.

Candidates must have some qualitative background like Maths, Engineering, and Business management.

Fees: 44999 INR

Data analyst, Data analyst consultant, Data scientist.

Also, read here the excellent and most-opted:

• Data Analytics Courses in Navi Mumbai
• Data Analytics Courses in Goa
• Data Analytics Courses in Nashik
• Data Analytics Courses in Guwahati
• Data Analytics Courses in Patna
• Data Analytics Courses in Vadodara
• Data Analytics Courses in Tricky
• Data Analytics Courses in Mangalore

Udacity is an online platform that provides a world-class learning environment on its platform. It is different because they provide Customized solutions, unique approaches, and managed experiences for the seekers.

The program name is Data analysis with python and SQL. With is program students get experience to work with up with the messy dataset. It also includes the related program to this course is Programming of Data Science with Python and Business Analyst.

The syllabus includes the modules like Introduction to Data Analysis, Practical statistical, Data wrangling, and Data visualization with python. The relevant tools which are mentioned in the course are SQL and python .

They also have a list of things that they offer within the program is Real-world projects from industry experts, a Flexible learning program, a Technical support mentor, and career services.

Duration: 4 months

Graduates preferably.

Fees: 77676 INR

Data analyst, Data scientist.

• Data Analytics Courses in Trivandrum
• Data Analytics Courses in Varanasi
• Data Analytics Courses in Udaipur
• Data Analytics Courses in Dehradun
• Data Analytics Courses in Visakhapatnam
• Data Analytics Courses in Surat
• Data Analytics Courses in Gujarat
• Data Analytics Courses in Nagpur

6. IMS Preschool

IMS Proschool is the institute working since 1977. It is building and skilling the youth. By giving the youth proper guidance, motivation, and support youth in their career building and getting a shinning future. It is a top-ranked institute by Analytics India magazine for the last 4 years.

The Course name is Business Analyst the related term to it like Big data, Data mining, and Data science they all are terms which relative called to the Business analyst program.

The course includes Statistical techniques, Predictive analysis, Regression techniques, and Machine learning modules. This includes the techniques like Predictive analysis, Data exploration, Clustering, and classification. The relevant tools used are Python, SQL, R, and Tableau.

The institutes have some brilliant features that will call up to enroll yourself with this institute is that you will get active learning methodology and get trained by industry experts.

The program includes joint certification from IMS Preschool and NSDC, one more interesting thing you will get an optional certificate from the NSE Academy.

Duration: 15 weeks.

Fees: 50000 INR (Classroom learning) & 32000 INR (live virtual classroom).

Data analyst, Data scientist, Business analyst, Data Architect, Data administrator.

Check out the Top Professional Courses for Skill Development

7. Edvancer

Edvancer is the platform that is opened by the IIT & IIM Alumni. The motive of the institute is to provide the best education possibilities to the seekers who want to build up their careers in the field of technology.

The Program’s name is Advanced Certification in Data Analytics-IIT Kanpur. The Course modules include Predictive Analysis, Data Visualization, and Data analysis. It includes the tools like R, Tableau, and SQL.

The Program is delivered by the experts of IIT Kanpur and Industry experts of Edvancer. The most interesting feature you will get with this institute is all-time access to the online course content post-completion of the course

More features are it makes you job-ready, practical content, and 100% placement assistance with proper career assistance.

Duration: 180 hours.

Highly suitable for nontech background also.

Fees: 69990 INR (Live online) 44990 INR (Self-paced).

Data scientist, Data analyst, Data architect.

8. Imarticus Learning

Imarticus learning builds you in such a way that you will get proper career growth after completing the program. Experienced learning, Valuable approach, guidance, and proper support to enhance your skill to become an industry expert.

The Course name is Post Graduate Program in Data Analytics. You will be trained in this course by Case study, Inclass projects Capstone projects, Boot camps & Hackathon.

The Curriculum highlights are SQL, Probability & Statistics, Data science with python and R, Big data Hadoop & Spark, and Data visualization with Tableau & Power BI.

The Data analytics course will provide 360-degree learning, Tech-enabled learning, Career Services, and Industry connect. It also has Industry oriented case studies, Real business problems for effective learning, and Business problems from Different industries it’s an add-on skill for learners.

The Institute will prepare you accordingly Job relevant skills, learn life, Get mentored, and learn job-relevant skills. It has the certification by the side of the Skill India program NSDC after the completion of the post-graduate program.

Duration: 24 weeks.

Fresh graduates and Professionals with up to 3 years of experience.

Fees: 2, 10,000 INR (In Classroom)

Data scientist, Data analyst, Data Science Consultant, Business analytics specialist, Web, and social media analyst, Data mining specialist, Business analytics tech consultant, Business intelligence analyst, CRM analyst.

9. Ivy Professional School

Ivy Professional schools are providing all types of Comprehensive courses since 2004. The institute is ranked under the top 10 for Big Data & Analytics Schools in 2015. The institute is also having official training partners Capgemini, HSBC, Genpact, Cognizant, Pay pal.

Among the different data analytics courses, their course name is Business analytics Certification. There is one relevant course also like Predictive analytics with R certification.

The Course outline the modules with relevant tools are Analytics Essentials, Industry Applicable Core Analytics, Advanced analytics using SAS, and Predictive analysis using R.

The Institute provides Hands-on learning through guaranteed Industry projects and Internships, the best placement support and is taught by Elite faculty from IIIT, IIM, ISI, US Universities.

They also include Classroom training across India, Live online training, and join self-paced Training. The Certification is provided in collaboration with NASSCOM.

Duration: 189 hours.

Graduates-Post graduates with Math/Statistics/Economics, MBA Graduates any stream, Banking/ Finance/IT/KPOworking professional.

Fees: 37400 INR

Data scientist, Data analyst, Big data scientist, Business analyst.

10. Inventateq

The institute is known to provide Top Data analytics Courses because it is a reputed institute for Data Analytics programs which is mentioned by many top-class magazines.

The Course name is Business Data Analytics. The best thing about this course is that it is a placement oriented course for the learners. You will be taught by the 9+ years certified trainers.

The Course modules with the relevant tools are Business Data analytics with python specialization, Business Data Analytics with R programming and SAS, Artificial Intelligence Course, Machine learning course, Neural Networks, and Deep Learning, Tensor flow classes, Data analytics expert, Probability and statistics courses.

You will be given the Industry-driven comprehensive curriculum; live interaction with machines, Real-world projects & case studies, and the most important thing is all-time access to study materials and videos.

The mode of training is online classrooms and offline training is available.

The Training methodology of the institute is Theory, Practical, Assessments, Certification, Resume preparation, Attend Interview and you are hired by the top industry.

Duration: 2-3 months.

Students/Freshers and Working professionals.

Business analyst, Data scientist, Data analyst, Google Business Data analyst.

NIIT is an online institution that is working for a very long period providing us the several job-oriented courses from a technical background. This Institute has made its reach in the market due to its outstanding courses provided by them. It also runs the best data analytics courses for learners.

The Course name Data analysis and Visualization in Excel and Power BI.

The Course module outline is Learning the course while using excel in Data Analysis and Visualization. By using excel’s inbuilt functions and user-defined formulas for Data analysis. Excel Data Analysis Tool pack for Data analysis, Creating and Customizing charts in excel for Data visualization.

Data Visualization in Power BI Desktop and Web. By using Excel Data in Power BI for Visualization. Reporting/Dashboarding using Power BI, Publishing Power BI Dashboards, Data relationships and queries in Power BI, and Data Transformations in Power BI.

The NIIT provides a unique curriculum and study material by the institute. The classroom plus app-based learning for their courses. The Courses are taught by the Industry Experience Faculty. The Faculty guidance through the app you will get in the online learning. Hands-on application of tools by the side of the course which you get a chance to make your learning better and more influential.

The Institute has a Knowledge Centre in which you can get a chance to see Industry trends by the side of them.

The Data Analysis Course is the Certification program that the individual get after the completion of the course.

Duration: 38 Hours (Classroom-based) 36 Hours (Online-based learning).

Undergraduates and graduates from Math’s/Statistics backgrounds.

Fees: 30000 – 1,00,000 INR

Data analyst, Business analyst, Data Scientist, Business Intelligence, Business analyst, Research analyst.

The Data analytics Courses are booming day by day. The Data says that the Industry will have billions of turnovers by 2025. The Data analysis is for Undergraduates, Graduates, a Working professional who wants to enhance their analytics skills and be ready for a career in the analytics field. The Course is also famous because it’s a course which is job oriented course the seekers who want to start a new they can do this course. This is assured that by learning Data analytics you will get the best job options it was the wider scope in different industries and Multinational Companies also. The Industry examples that have scope for Data, and analytics are IT, Retail, Banking, Pharma, Hospitality, Healthcare, FMCG, Media, and Sports, etc.

Q. What if I miss my live session, how can I cope up with my missed class?

IIM SKILLS shares recordings of live sessions after every class to all its participants. Which means you can refer the video if you miss your live class.

Q. How can I be sure if the course is worth taking?

If you are not sure, you can register for the free demo session with IIM SKILLS & decide later.

Q. I am a fresher with no experience, am I eligible to enroll for the course?

Absolutely yes, data analytics course is suitable for everyone who aspires to become a data analyst. The course with IIM SKILLS begins with introduction to data analysis and gradually progresses to expert knowledge.

Author: Anusha Bajpai

Hello I am a SEO Manager in a Marketing firm. Today’s customer experience has changed exponentially with the availability of so many digital tools. The same person searching for a product or service can end up at the same website that someone else was also searching in an entirely different way. Since customers have multiple avenues in which to locate a product or service, it can be challenging to know what the best methods are for your business to ensure that yours ranks high enough. Want to acquire knowledge and expertise in Data analytics to gain deep insight and to help make results more effective, and devise appropriate methods. Thanks for providing list of available data analytics courses in India. It is of much help Thanks.

I’m Bcom students , i am not from science and math background can i able to to do this course and from where should i can learn data analyst course completely

you can learn from IIM SKILLS

Hi, My name is pankaj and i want to learn data analyst course, but i am not science and mathematics background. Can i able to do this course nd if i do then which type of difficulties i have see

I am a bcom graduate can i will do the data analytics course

Yes you can.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Join Free Data Analytics Master Course Demo Class

• Comments This field is for validation purposes and should be left unchanged.

Weekend Batch - 21st April 2024

Every Sat & Sun - 10:00 AM - 1:00 PM (IST)

5 Seats Left

You May Also Like To Read

Top 9 excellent creative writing courses in kerala, top 30 content writing interview questions (updated) list, top 7 certificate course in financial accounting and taxation, top 10 digital marketing courses in cuba with placements, 12 best forensic science courses in india, top 12 technical writing courses in patna with placements, 7 best python courses in pune: curriculum and details, top 8 data analytics courses in algeria with placements, top 7 digital marketing courses in vivek vihar with placements.

• 100% assured internships
• Placement Assured Program
• 500+ Hiring Partners
• 100% Money Return Policy

Sunday Batch - 28th April 2024

Sunday 10:00 AM - 2:00 PM (IST)

Share Your Contact Details

• Name This field is for validation purposes and should be left unchanged.
• Phone This field is for validation purposes and should be left unchanged.

Weekdays Batch - 16th April 2024

Tues & Thur - 8:00 PM - 9:30 PM (IST)

Saturday Batch - 13th April 2024

Saturday 10:00 AM - 1:00 PM (IST)

Download Course Brochure

• Email This field is for validation purposes and should be left unchanged.

Download Hiring Partners List

Download tools list, weekend batch - 04th may 2024.

Every Sat & Sun - 10:00 AM - 12:00 PM

Request for Online DEMO

Every Sat & Sun - 10:00 AM - 12:00 PM (IST)

• Learn From An Expert
• Steroids To Crack CAT Exam
• Flip The Classroom Concept
• Technology Driven

Request to Speak with MBA ADVISOR

• Select Course * * Select Course Advanced Search Engine Optimization Business Accounting & Taxation Course Business Analytics Master Course Content Writing Master Course Digital Marketing Master Course Data Analytics Master Course Data Science Master Course Financial Modeling Course Investment Banking Course GST Practitioner Certification Course Technical Writing Master Course Tally Advanced Course Other Course
• ADDITIONAL COMMENT

Weekday Batch - 23rd April 2024

Every Tue, Wed & Thur - 8 PM - 10 PM

Talk To An Agent

Talk to our agent, download student's success report, weekday batch - 30th april 2024.

Every Tue, Wed & Thur - 8:00 PM - 10:00 PM (IST)

Request For a Callback

Start hiring.

• Company Name *
• Hiring for * Select Program Content Writer Digital Marketer Data Analyst Financial Modellers Technical Writer Business Accounting & Taxation Search Engine Optimization Investment Banking
• Attach Document * Max. file size: 256 MB.
• Company Name * First
• Select Program Select Program Business Accounting & Taxation Course Content Writing Master Course Digital Marketing Master Course Data Analytics Master Course Financial Modeling Course Search Engine Optimization Technical Writing Master Course
• Select Members Select Mumbers 1-5 6-20 21-50 51-100 100+
• Additional Comments

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

Partner Center

Maternal and Infant Research Electronic Data Analysis (MIREDA): A protocol for creating a common data model for federated analysis of UK birth cohorts and the life course

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for MJ Seaborne
• For correspondence: [email protected]
• ORCID record for HE Jones
• ORCID record for N Cockburn
• ORCID record for S Durbaba
• ORCID record for TC Giles
• ORCID record for A González-Izquierdo
• ORCID record for A Hough
• ORCID record for D Mason
• ORCID record for A Mendez-Villalon
• ORCID record for C. Sanchez-Soriano
• ORCID record for C. Orton
• ORCID record for D Ford
• ORCID record for P Quinlan
• ORCID record for K Nirantharakumar
• ORCID record for L. Poston
• ORCID record for RM Reynolds
• ORCID record for G Santorelli
• ORCID record for S Brophy
• Info/History
• Preview PDF

Introduction Birth cohorts are valuable resources for studying early life, the determinants of health, disease, and development. They are essential for studying life course. Electronic cohorts are live, dynamic longitudinal cohorts using anonymised, routinely collected data. There is no selection bias through direct recruitment, but they are limited to health and administrative system data and may lack contextual information.

The MIREDA (Maternal and Infant Research Electronic Data Analysis) partnership creates a UK-wide birth cohort by aligning existing electronic birth cohorts to have the same structure, content, and vocabularies, enabling UK-wide federated analyses.

Create a core dynamic, live UK-wide electronic birth cohort with approximately 100,000 new births per year using a common data model (CDM).

Provide data linkage and automation for long-term follow up of births from MuM-PreDiCT and the ‘Born in’ initiatives of Bradford, Wales, Scotland, and South London for comparable analyses.

Methods We will establish core data content and collate linkable data. Use a suite of extraction, transformation, and load (ETL) tools will be used to transform the data for each birth cohort into the CDM. Transformed datasets will remain within each cohort’s trusted research environment (TRE). Metadata will be uploaded for the public to the Health Data Research (HDRUK) Innovation Gateway . We will develop a single online data access request for researchers. A cohort profile will be developed for researchers to reference the resource.

Ethics Each cohort has approval from their TRE through compliance with their project application processes and information governance.

Dissemination We will engage with researchers in the field to promote our resource through partnership networking, publication, research collaborations, conferences, social media, and marketing communications strategies.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by an MRC Partnership Grant [MR/X02055X/1], MatCHNet pump-priming [U20005/302873] and an MRC Programme Grant [MR/X009742/1].

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Access to data is granted according to the information governance requirements of each TRE. The Data Protection Act 2018 is not applicable to anonymised data and the OMOP CDM will be anonymised and provide aggregated data and statistics only. Each TRE has ethical approval for its operation and use, thus no additional ethical approval was required beyond the standard project approval by official channels.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

There was an error in the order of authorship and a missing punctuation mark after the title abstract. Also needed to update the authors order in the file I uploaded as it did not match that of the paper.

Data availability statement

Data will be available upon reasonable request through the Health Data Research (HDRUK) Innovation Gateway .

Abbreviations

View the discussion thread.

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager
• Tweet Widget
• Facebook Like
• Google Plus One

Subject Area

• Public and Global Health
• Addiction Medicine (316)
• Allergy and Immunology (621)
• Anesthesia (162)
• Cardiovascular Medicine (2294)
• Dentistry and Oral Medicine (280)
• Dermatology (202)
• Emergency Medicine (370)
• Endocrinology (including Diabetes Mellitus and Metabolic Disease) (815)
• Epidemiology (11611)
• Forensic Medicine (10)
• Gastroenterology (683)
• Genetic and Genomic Medicine (3611)
• Geriatric Medicine (338)
• Health Economics (620)
• Health Informatics (2324)
• Health Policy (917)
• Health Systems and Quality Improvement (869)
• Hematology (336)
• HIV/AIDS (758)
• Infectious Diseases (except HIV/AIDS) (13193)
• Intensive Care and Critical Care Medicine (760)
• Medical Education (360)
• Medical Ethics (101)
• Nephrology (392)
• Neurology (3388)
• Nursing (193)
• Nutrition (511)
• Obstetrics and Gynecology (653)
• Occupational and Environmental Health (653)
• Oncology (1775)
• Ophthalmology (526)
• Orthopedics (210)
• Otolaryngology (284)
• Pain Medicine (226)
• Palliative Medicine (66)
• Pathology (441)
• Pediatrics (1010)
• Pharmacology and Therapeutics (423)
• Primary Care Research (409)
• Psychiatry and Clinical Psychology (3098)
• Public and Global Health (6017)
• Radiology and Imaging (1235)
• Rehabilitation Medicine and Physical Therapy (718)
• Respiratory Medicine (813)
• Rheumatology (370)
• Sexual and Reproductive Health (359)
• Sports Medicine (319)
• Surgery (390)
• Toxicology (50)
• Transplantation (171)
• Urology (142)

• Open access
• Published: 09 April 2024

National norms for the obstetric nurses’ and midwives’ health education competence, and its influencing factors: a nationwide cross-sectional study

• Jingjing Zou 1   na1 ,
• Jingling Wu 2   na1 &
• Xiumin Jiang 3  

BMC Medical Education volume  24 , Article number:  389 ( 2024 ) Cite this article

122 Accesses

Metrics details

Strengthening obstetric nurses’ and midwives’ health education competence is the investment and guarantee for the population’s future health. The purpose of study is to establish national norms for their health education competence, and explore possible influencing factors for providing an uniform criterion identifying levels and weaknesses.

An online questionnaire with a standard process was used to collect data. Three normative models were constructed, and multiple linear regression analysis analyzed possible influencing factors.

The sample respondents ( n  = 3027) represented obstetric nurses and midwives nationally. Three health education competency normative norms (mean, percentile and demarcation norm) were constructed separately. Locations, hospital grade, department, marital status, training times and satisfaction with health education training influenced obstetrical nurses’ and midwives’ health education competence ( P <0.05).

This study constructed the first national standard for assessing obstetric nurses’ and midwives’ health education competence, providing a scientific reference to evaluate the degree of health education competence directly. These known factors could help clinical and policy managers designate practice improvement measures. In future research, Grade I hospitals should be studied with larger sample sizes, and indicators need to improve to reflect health education’s effect better.

Peer Review reports

The World Health Organization has long recommended [ 1 ] that pregnant women need more health education, life guidance, and follow-up visits. “Outline of the Healthy China 2030 Plan” [ 2 , 3 ] also proposed that health services would be strengthened to improve the health of women and children, and it is essential to provide health education covering the prenatal, perinatal and postnatal periods. Comprehensive and practical health education can significantly enhance maternal and infant safety, promote spontaneous delivery, and increase exclusive breastfeeding rates [ 4 ]. Authorities such as the International Confederation of Midwives [ 5 ] and the American College of Nurse-Midwives [ 6 ] emphasise the critical role of midwives and obstetric nurses in providing comprehensive care, assisting in labour and delivey, and managing complications [ 7 ]. Obstetric nurses and midwives should possess extensive health knowledge and excellent education competence to ensure women and their families can make informed decisions, and safely manage maternal health and well-being [ 8 ].

Strengthening obstetric nurses’ and midwives’ health education competence is the investment and guarantee for the population’s future health [ 9 ]. Given its importance, researchers have conducted in-depth discussions on health education quality, goals, strategy and evaluation. However, no study has built a uniform criterion for assessing the performance of obstetric nurses’ and midwives’ health education competency. A norm, a reference standard for the scores obtained using a scale, is usually the average score and standard deviation of many testers. A norm could compare the differences between different groups and assess individual performance [ 10 ]. Meanwhile, based on the normal analysis, a more scientific and reliable scale promotion strategy can be formed to popularize and promote relevant scientific theories and methods [ 11 ].

The rating scale of health education competence for nurses (RSHECN) was developed and verified its reliability and validity (Tong and Li, 2010). The scale determined that good performance in health education requires nurses to have adequate expertise, sound assessment, planning and implementation and the ability to evaluate the effectiveness of health education, which calcified the connotation of health education competence for nurses and provided a way for evaluation. Therefore, a nationwide cross-sectional survey of multi-stage stratified cluster sampling was conducted to establish norms for RSHECN and explore their influencing factors of health education competence, providing a reference for clinical and policy managers to identify weaknesses and formulate practice improvement plans.

Study design

A cross-sectional study of multi-stage stratified cluster sampling was carried out [ 12 , 13 ]. The nationwide obstetric nurses and midwives were selected as participants from April to May 2021 to establish the mean norm, percentile norm and demarcation norm of RSHEC and explore possible influencing factors of obstetric nurses’ and midwives’ health education competence.

Participants

The participants were recruited using a stratified multistage cluster sampling method with three steps: (1) Selected representative regions. Three regions (Eastern China, Central China and Western China) were selected, divided by the National Bureau of Statistics of China according to geographic location and economic level. (2) Selected provincial administrative unit (from now it was referred to as the “unit”). The convenient sampling method was used to decide the final units. Seven out of eleven in the eastern region were selected: Tianjin, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian and Guangdong. Four out of eight units in the central region were selected: Shanxi, Heilongjiang, Jiangxi and Hunan. Seven out of twelve units in the western region were selected: Sichuan, Chongqing, Gansu, Qinghai, Xinjiang, Guangxi and Inner Mongolia. (3) Selected included hospitals. The selection of hospitals adopted a convenient sampling method and ensured the diversity of garde I, II and III hospitals as much as possible. After that, with the consent of the hospital nursing department, a whole-group sampling method was used to include all obstetric nurses who met the inclusion criteria in the included hospitals. All active registered obstetrical nurses or midwives who voluntarily participated were included in this study. Moreover, interns, visiting nurses, and nurses who were absent during the survey or could not attend for personal reasons were excluded. The ethical committee of the principal researcher’s hospital approved the study (No 2018 − 206). Before the survey, written consent was obtained from all nursing departments. The questionnaire does not collect the personal information of the participants, and the database can only be accessed by the members of the research group. Participants were informed consent, and the returning online questionnaire was considered consent of participation.

Data collection

An introduction letter stating the study aim and process was issued to the department of the selected hospital to obtain survey permission. Then the online training was held to conduct a unified training for the hospital responsible person for the project. The standard data collection process was introduced to the responsible person with a standard language, and the contact information of the research group was provided to communicate the problem during the survey. The standard data collection process is the following: (1) Screen potential participants according to inclusion and exclusion criteria; (2) Seek the consent of potential participants. (3) Emphasize anonymity and confidentiality and sign the informed consent; (4) Invite participants to complete the questionnaire. Considering workforce and material resources, this survey adopts electronic questionnaires by the software “Questionnaire Star”, which helps to distribute questionnaires more scientifically in such an extensive national survey. The procedure was set so that each electronic device could only fill in the questionnaire once and submit the questionnaire after completing all items within 30 min. At the end of the survey, 5% of the questionnaires were randomly selected for quality check.

Measurements

The health education competence assessment questionnaire involves two parts: (1) general information questionnaire: The questionnaire was designed by reviewing relevant literature research and discussing with obstetric nursing experts, which covered the potential factors that might affect the health education competence of obstetrical nurses and midwives, including the type of hospital, age, educational level, current work department, additional training in health education, working years and other basic social demographic information. (2) Rating Scale of Health Education Competence for Obstetric Nurses and Midwives. The scale was used to evaluate the competence of health education of nurses and midwives, which had been authorised by the developer of Tong [ 14 ]. The self-evaluation scale includes four dimensions: assessment, plan, implementation and evaluation. Thirty-eight items on a five-point Likert-type scale (1 to 5, “completely disagree” to “completely agree”) and all items are positive. The score ranges from 37 to 185, and higher scores indicate better health education competence. The psychological verification was completed among various departments, including the obstetric nurse and midwife. The scale’s Cronbach α and half-fraction reliability were 0.949 and 0.953, the content validity index was 0.90, and it was verified with good construction validity and distinguish validity [ 14 ]. In this sample, 500 questionnaires were randomly selected in proportion to the number of obstetric nurses and midwives for reliability testing, and its Cronbach α was 0.987. Moreover, to facilitate understanding and comparison, the results of this study were analysed using conversion score, and the formula is as follows: conversion score = (original score theoretical minimum score of this aspect) / (theoretical maximum score theoretical minimum score of this aspect) ×100.

Data analysis

All calculations were performed using IBM SPSS Statistics software (version 26.0). Continuous variables were reported as mean ( \(\stackrel{-}{\text{x}}\) ) ± standard (S), and categorical variables were presented as frequencies and proportions. Three types of norms were developed in this study to establish normative values for health education competence among obstetric nurses and midwives. The mean norm was determined using the results of one-way ANOVA to calculate the mean and standard deviation of conversion scores and each dimension score. Percentile norm was established using the percentile method, with 5% percentile intervals, resulting in normative values at the 5th, 25th, 50th, 75th, and 95th percentiles. The demarcation norm was established through the distribution method with different demarcation schemes calculated at a spacing of 0.5 S within the total scale score ( \(\stackrel{-}{x}\)  ± 2.5 S). After that, we performed correlation analysis and selected the scheme with the highest correlation as the demarcation constant for the study [ 15 ]. Differences in assessment, plan, implementation, evaluation and conversion scores were analysed using an independent two-sample t-test and one-way analysis of variance, with demographic characteristics as independent variables. Statistically significant variables from the ANOVA analysis were included as independent variables in a stepwise multiple linear regression analysis to evaluate their contributions to conversion scores. In this study, covariance diagnosis of independent variables is based on tolerance (TOL) and variance inflation factor (VIF), and if TOL < 0.1 or VIF ≥ 10, it means that there is serious covariance between independent variables.

Three thousand three hundred questionnaires were received, 97 were excluded due to logical self-contradiction of data and abnormal distribution of values, and 3207 questionnaires were available with an effective recovery of 97.18%. All participants were female between eighteen and sixty-four years (33.20 ± 7.51 years). They had one to forty-five working years with an average of (11.00 ± 8.15) years covering the general population for job title, education, department and health education training conditions. Detailed demographic characteristics of the sample are shown in Table  1 .

Mean norms could be established for groups with different characteristics in the tested population. Considering the different economic and medical levels, five categorical mean norms were determined, including grade III hospitals, grade II hospitals, eastern China, central China and western China (Table  2 ). There is no specification Grade I for hospitals because of the insufficient sample size of primary hospitals (only 41 nurses from Grade I hospitals). The percentile norm was calculated based on scale scores and each dimension score at an interval of 5%, as shown in Table  3 . The distribution method was used to establish the demarcation norm, and plan 4, with the highest correlation coefficient ( r  = 0.970), was selected as the final scheme, as shown in Table  4 . The final demarcation grade was extremely poor [0, 70.32), poor [70.32, 76.5), medium [76.5, 88.86), good [88.86, 95.04), and excellent [95.04, 100].

The results of one-way ANOVA showed statistically significant differences ( P  < 0.05) in the health education competency conversion scores comparing hospital type, hospital grade, department, locations, marital status, satisfaction with health education training, and training times of health education. The multiple linear regression analysis showed that hospital grade ( P  = 0.002), locations ( P  = 0.032), department ( P  = 0.001), marital status( P  = 0.003), satisfaction with health education training ( P  < 0.001), and training times of health education ( P  = 0.006) were independent influencing factors of obstetric nurses’ and midwives’ health education competency scores. In this study, the TOL values were 0.956–0.993 and VIF values were 1.007–1.046, which cannot be considered as the existence of multiple covariance between independent variables, and all independent variables can be analysed by multiple regression.

This study established the first national norms for obstetric nurses’ and midwives’ health education competency and explored possible influencing factors. The mean norm can be used to determine whether obstetric nurses’ and midwives’ health education competency is within the reference range [ 15 ]. The result showed that the health education competency was highest in Central areas, followed by Eastern areas, and the lowest in Western areas. The central and east areas have superior medical resources, attracting more medical and nursing talents, while the western region has more mountainous areas with less developed medical resources. Central region scores higher than East region, probably because Central region contains fewer cities. The sample size of this survey is smaller, which makes its average score higher. The mean norm describes the overall level, and the percentile norm was formed to compare the individual score within the corresponding percentile norm to identify individual positions in the group [ 16 , 17 ]. The higher the score, the higher the percentile norm position, which means the health education competency level is better. The result of showed that the best division scheme was extremely poor [0, 70.32), poor [70.32, 76.5), medium [76.5, 88.86), good [88.86, 95.04) and excellent [95.04, 100], which make the scores for different indicators can be compared easily, reducing the difficulty of interpreting and comparing data, while also allowing for a more intuitive and accurate assessment of individual performance.

In this study, the mean scale score was (82.68 ± 12.36), which is intermediate compared to the norm [ 18 , 19 ]. The conversion scores from highest to lowest were implementation, evaluation, assessment and planning, consistent with clinical practice. In the clinical environment, each pregnant woman has different educational needs. However, nurses, as mainly part of implementer of health education, only teach fixed content but do not individualise health education on a case-by-case basis. Although there are often many research materials, such as guidelines, to guide obstetric nurses and midwives on what to do, they often copy and use indoctrination again, lacking individualised assessment of pregnant women [ 20 ]. Thus, the result prompts us to form a practical health education model in line with national conditions, strengthing the status of evaluation, assessment and planning to provide individualised health education and play the role of health education better.

The study identified that locations, hospital grade, department, marital status, satisfaction with health education training and training times were influencing factors for obstetric nurses’ and midwives’ health education competence. Among different locations, the disparity in medical conditions may lead to managers with different perceptions on the role of nurses’ and midwives’ in health education. Within health care teams, obstetric nurses and midwives are vital health education providers throughout the pregnancy and delivery. The government could introduce more policies and supportive steps to improve the attention of hospitals in underdeveloped areas to the health education capacity of nurses.

The score of tertiary hospitals was higher than secondary, and the possible reason is that tertiary hospitals absorbs higher qualified nursing talents [ 21 , 22 ], and they have more robust medical resources, research and teaching capabilities to provide more professional training and education and are more excellent regarding professional qualifications and skills [ 23 ]. Meanwhile, the regression analysis showed that the times and satisfaction of health education training were influencing factors. Long-term participation in health education training could enhance the professional confidence, stability and self-confidence of obstetrical nurses and midwives [ 23 , 24 ]. Satisfactory training can encourage applying knowledge and skills in practical work, promoting health education competency and work continuity [ 25 ]. Each training is a process of knowledge accumulation, and the increasing knowledge reserve in reproductive health, prenatal, intrapartum and postpartum care can better guide maternal health management and improve the life quality of birthing mothers and their infants [ 26 , 27 ]. Therefore, for hospitals managers, the organization of comprehensive, professional and satisfactory health education knowledge training should be regarded as an important part of management, especially for grassroots hospitals.

Another interesting result is that the health education competence of married and fertile nurses was better, who can better feel the actual needs of pregnant women and combine their own experience to provide more detailed and thoughtful health education in dealing with various real situations [ 28 , 29 ]. Future research can explore more health education methods from the perspective of maternity, so as to help unmarried and infertile nurses and midwives. Our result also showed that midwives scored lower than obstetric nurses, which may be due to the different work nature. Generally, obstetric nurses provide health education in the ward, while midwives in the delivery room. The unique physiological conditions for childbirth can make it challenging to provide health education. And the demand for health education after delivery is more significant, as the mother and her family require more information about puerperal rehabilitation and neonatal care. When providing health education, midwives and obstetric nurses could promote strengths and avoid weaknesses. Obstetric nurses can provide comprehensive health education for mothers and their families after delivery, and midwives can try to move forward their own health education opportunities and provide health education in midwives’ outpatient clinics.

A normative standardised reference will serve as a reference to help obstetric nurses and midwives identify strengths and weaknesses in health education competence and help management establish a more reasonable nursing echelon for enhancing maternal health [ 30 , 31 ]. The nationwide cross-sectional survey could help clinical and policy managers understand the current health education situation and formulate corresponding management plans for practice improvement [ 32 , 33 ]. Although the results reported here are of interest, it is necessary to acknowledge certain limitations of the study. Firstly, due to time and human constraints, the small sample size of the Grade I hospitals in this study affected the completeness of the norm. Also, the convenience sampling method used for hospital selection might introduce bias, as it does not ensure a randomized and comprehensive representation of all hospital grades, particularly Grade I hospitals. Future studies should aim for a more extensive and diverse sample, including a better representation of all hospital grades. Secondly, the study is limited to a specific time frame, which may not adequately represent changes over time. A longitudinal approach could offer insights into how health education competence evolves over time and its long-term impact on patient care and outcomes. Thirdly, the scale is a self-assessment scale, which is subjective in evaluating health education competence and lacks objective evaluation indicators. Obstetric nurses and midwives with higher scores indicate a certain level of health education competence. However, the effect of health education is not reflected by objective indicators, which need to be improved in future studies. Finally, implementing and evaluating training interventions could provide practical insights into effective strategies for improving health education competence among obstetric nurses and midwives.

A nationwide cross-sectional study of multi-stage stratified cluster sampling was conducted to establish the first national norms for obstetric nurses’ and midwives’ health education competency. Locations, hospital grade, department, marital status, satisfaction with health education training and training times were independent influencing factors for obstetric nurses’ and midwives’ health education competence. The study provides a valid way to assess obstetric nurses’ and midwives’ health education competency comprehensively and comparatively. It helps practitioners make more informed choices when developing relevant programs or decisions. In future research, Grade I hospitals should be studied with larger sample sizes, and indicators need to improve to reflect health education’s effect better.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Herval ÁM, Oliveira DPD, Gomes VE, Vargas AMD. Health education strategies targeting maternal and child health: a scoping review of educational methodologies. Medicine. 2019;98(26):e16174.

Article   Google Scholar  

The outline of the healthy China. 2030 plan and the health China initiative.  https://www.gov.cn/zhengce/zhengceku/2022-04/09/content_5684258.htm . Accessed 10 Nov 2023.

Opinions of the State Council on the implementation of the Healthy China initiative. http://www.gov.cn/zhengce/content/2019-07/15/content_5409492.htm .

Lau JD, Zhu Y, Vora S. An evaluation of a perinatal education and support program to increase breastfeeding in a Chinese American Community. Matern Child Health J. 2021;25(2):214–20.

Renfrew MJ, McFadden A, Bastos MH, Campbell J, Channon AA, Cheung NF, Silva DR, Downe S, Kennedy HP, Malata A, et al. Midwifery and quality care: findings from a new evidence-informed framework for maternal and newborn care. Lancet (London England). 2014;384(9948):1129–45.

Avery MD, Jennings JC, Germano E, Andrighetti T, Autry AM, Dau KQ, Krause SA, Montgomery OC, Nicholson TB, Perry A, et al. Interprofessional education between midwifery students and obstetrics and gynecology residents: an American college of Nurse-Midwives and American College of Obstetricians and gynecologists collaboration. J Midwifery Womens Health. 2020;65(2):257–64.

Izudi J, Akwang DG, McCoy SI, Bajunirwe F, Kadengye DT. Effect of health education on birth preparedness and complication readiness on the use of maternal health services: a propensity score-matched analysis. Midwifery. 2019;78:78–84.

Martinez Galiano JM, Delgado-Rodríguez M. Attendance to a health education program for pregnant women and outcomes of the newborn: health education of pregnant women and newborn. Minerva Pediatr. 2016;68(3):177–81.

Google Scholar  

Nutbeam D, Lloyd JE. Understanding and responding to health literacy as a social determinant of health. Annu Rev Public Health. 2021;42:159–73.

Kunzler AM, Chmitorz A, Bagusat C, Kaluza AJ, Hoffmann I, Schäfer M, Quiring O, Rigotti T, Kalisch R, Tüscher O, et al. Construct validity and population-based norms of the German brief resilience scale (BRS). Eur J Health Psychol. 2018;25(3):107–17.

Sedlander E, Bingenheimer JB, Long MW, Swain M, Rimal RN. The G-NORM scale: development and validation of a theory-based gender norms Scale. Sex Roles. 2022;87(5–6):350–63.

Geng M, Yin X, Li G, Liu R, Chang H. Chinese norms for the caring behavior lnventory in grade Ill tertiary general hospitals. J Nurs Sci. 2023;38(12):67–71.

Huang C, Liu Q, Qiu H, Jiang L, Zhang J, Wu W, Xu J. Establishment of the norms of healthy fitness measurement Scale Version 1.0(HFMS V1.0)for Chinese urban elderly. J South Med Univ. 2021;41(02):223–9.

Tong H, Li X. Development of the rating scale of health education competence for nurses. J Nurs Sci. 2010;25(23):17–8.

Xu J, Xue Y, Liu G, Feng Y, Xu M, Xie J, Wang X, Chen X, Jiang L. Establishment of the norms of sub-health measurement scale version 1.0 for Chinese urban residents. J South Med Univ. 2019;39(03):271–8.

Liu P, Lin H, Xiao Z, Zhu H, Ji H, Yao M, Qian J, Tong M, Chi X, Hong Q. The development, validity, reliability, and norm of a preschool auditory processing assessment scale in China. Res Dev Disabil. 2022;128:104272.

Wicke FS, Krakau L, Löwe B, Beutel ME, Brähler E. Update of the standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord. 2022;312:310–4.

Chen Y, Chen J, Wang Q, Wang X. Analysis of health education ability of obstetrical nurses and its influencing factors. Chin J Hosp Stat. 2022;29(03):223–7.

Li J, Zhu W. Survey on health education for nurses in tertiary hospitals and analysis of influencing factors. Anhui Med J. 2023;44(06):726–30.

Yang X, Liang P, Wang Y. Health education competence of nurses working in military hospitals and its influencingfactors: a survey study. J Nurs Sci. 2013;28(10):81–3.

Liu J, Zhang X, Wei S. Measurement and analysis of health education competence of clinical nursing staff in selected secondary hospitals in Beijing. Military Nurs. 2013;30(21):68–70.

Xu Y, Hou X, Yang Y, Qiu C, Zhang Y, Wei L. Study on health education capacity of health professionals in secondary and above levelspublic general hospitals in Liuzhou. Chin J Health Educ. 2022;38(02):187–91.

Christie D, Thompson R, Sawtell M, Allen E, Cairns J, Smith F, Jamieson E, Hargreaves K, Ingold A, Brooks L, et al. Structured, intensive education maximising engagement, motivation and long-term change for children and young people with diabetes: a cluster randomised controlled trial with integral process and economic evaluation - the CASCADE study. Health Technol Assess (Winchester Eng). 2014;18(20):1–202.

Chen F, Liu Y, Wang X, Dong H. Transition shock, preceptor support and nursing competency among newly graduated registered nurses: a cross-sectional study. Nurse Educ Today. 2021;102:104891.

Hakvoort L, Dikken J, Cramer-Kruit J, Nieuwenhuyzen KM, van der Schaaf M, Schuurmans M. Factors that influence continuing professional development over a nursing career: a scoping review. Nurse Educ Pract. 2022;65:103481.

Khazhymurat A, Paiyzkhan M, Khriyenko S, Seilova S, Baisanova S, Kuntuganova A, Almazan JU, Cruz JP. Health education competence: an investigation of the health education knowledge, skills and attitudes of nurses in Kazakhstan. Nurse Educ Pract. 2023;68:103586.

Pueyo-Garrigues M, Pardavila-Belio MI, Canga-Armayor A, Esandi N, Alfaro-Díaz C, Canga-Armayor N. NURSES’ knowledge, skills and personal attributes for providing competent health education practice, and its influencing factors: a cross-sectional study. Nurse Educ Pract. 2022;58:103277.

Cruz JP. Quality of life and its influence on clinical competence among nurses: a self-reported study. J Clin Nurs. 2017;26(3–4):388–99.

Wang R, Chen S, Cong S, Sun X, Sha L, Zhu Z, Zhou H, Ren Z, Zhang J, Gu P, et al. Status and influencing factors of nursing and midwifery professionals’ core competence- a cross sectional study. J Nurs Adm Manag. 2022;30(8):3891–9.

Dang W, Xu Y, Ji J, Wang K, Zhao S, Yu B, Liu J, Feng C, Yu H, Wang W, et al. Study of the SCL-90 scale and changes in the Chinese norms. Front Psychiatry. 2020;11:524395.

Pettker CM, Grobman WA. Obstetric safety and quality. Obstet Gynecol. 2015;126(1):196–206.

Hollis G, Westbury C. When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms. Behav Res Methods. 2018;50(1):115–33.

Marten O, Greiner W. EQ-5D-5L reference values for the German general elderly population. Health Qual Life Outcomes. 2021;19(1):76.

Download references

Acknowledgements

Thanks to all participants for their valuable contribution to this study.

Author information

Jingjing Zou and Jingling Wu contributed equally to this work.

Authors and Affiliations

School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China

Jingjing Zou

Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China

Jingling Wu

Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No.18 Daoshan, Fuzhou, Fujian Province, China

Xiumin Jiang

You can also search for this author in PubMed   Google Scholar

Contributions

JJ.Z was responsible for data analysis, data interpretation and drafted the work.JL.W was responsible for conception, design and substantively revised work.XM.J was responsible for data acquisition and project administration.

Corresponding author

Correspondence to Xiumin Jiang .

Ethics declarations

Ethics approval and consent to participate.

All active registered obstetrical nurses or midwives who voluntarily participated were included in this study. The ethical committee of the principal researcher’s hospital approved the study (No 2018 − 206). Before the survey, written consent was obtained from all nursing departments. Participants were informed consent, and the returning online questionnaire was considered consent of participation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zou, J., Wu, J. & Jiang, X. National norms for the obstetric nurses’ and midwives’ health education competence, and its influencing factors: a nationwide cross-sectional study. BMC Med Educ 24 , 389 (2024). https://doi.org/10.1186/s12909-024-05249-w

Download citation

Received : 30 December 2023

Accepted : 01 March 2024

Published : 09 April 2024

DOI : https://doi.org/10.1186/s12909-024-05249-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health education
• Obstetric nurse

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]