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21 Data Science Projects for Beginners (with Source Code)

Looking to start a career in data science but lack experience? This is a common challenge. Many aspiring data scientists find themselves in a tricky situation: employers want experienced candidates, but how do you gain experience without a job? The answer lies in building a strong portfolio of data science projects .

A well-crafted portfolio of data science projects is more than just a collection of your work. It's a powerful tool that:

• Shows your ability to solve real-world problems
• Highlights your technical skills
• Proves you're ready for professional challenges
• Makes up for a lack of formal work experience

By creating various data science projects for your portfolio, you can effectively demonstrate your capabilities to potential employers, even if you don't have any experience . This approach helps bridge the gap between your theoretical knowledge and practical skills.

Why start a data science project?

Simply put, starting a data science project will improve your data science skills and help you start building a solid portfolio of projects. Let's explore how to begin and what tools you'll need.

Steps to start a data science project

• Define your problem : Clearly state what you want to solve .
• Gather and clean your data : Prepare it for analysis.
• Explore your data : Look for patterns and relationships .

Hands-on experience is key to becoming a data scientist. Projects help you:

• Apply what you've learned
• Develop practical skills
• Show your abilities to potential employers

Common tools for building data science projects

To get started, you might want to install:

• Programming languages : Python or R
• Data analysis tools : Jupyter Notebook and SQL
• Version control : Git
• Machine learning and deep learning libraries : Scikit-learn and TensorFlow , respectively, for more advanced data science projects

These tools will help you manage data, analyze it, and keep track of your work.

Overcoming common challenges

New data scientists often struggle with complex datasets and unfamiliar tools. Here's how to address these issues:

• Start small : Begin with simple projects and gradually increase complexity.
• Use online resources : Dataquest offers free guided projects to help you learn.
• Join a community : Online forums and local meetups can provide support and feedback.

Setting up your data science project environment

To make your setup easier :

• Use Anaconda : It includes many necessary tools, like Jupyter Notebook.
• Implement version control: Use Git to track your progress .

Skills to focus on

According to KDnuggets , employers highly value proficiency in SQL, database management, and Python libraries like TensorFlow and Scikit-learn. Including projects that showcase these skills can significantly boost your appeal in the job market.

In this post, we'll explore 21 diverse data science project ideas. These projects are designed to help you build a compelling portfolio, whether you're just starting out or looking to enhance your existing skills. By working on these projects, you'll be better prepared for a successful career in data science.

Choosing the right data science projects for your portfolio

Building a strong data science portfolio is key to showcasing your skills to potential employers. But how do you choose the right projects? Let's break it down.

Balancing personal interests, skills, and market demands

When selecting projects, aim for a mix that :

• Aligns with your interests
• Matches your current skill level
• Highlights in-demand skills
• Projects you're passionate about keep you motivated.
• Those that challenge you help you grow.
• Focusing on sought-after skills makes your portfolio relevant to employers.

For example, if machine learning and data visualization are hot in the job market, including projects that showcase these skills can give you an edge.

A step-by-step approach to selecting data science projects

• Assess your skills : What are you good at? Where can you improve?
• Identify gaps : Look for in-demand skills that interest you but aren't yet in your portfolio.
• Plan your projects : Choose 3-5 substantial projects that cover different stages of the data science workflow. Include everything from data cleaning to applying machine learning models .
• Get feedback and iterate : Regularly ask for input on your projects and make improvements.

Common data science project pitfalls and how to avoid them

Many beginners underestimate the importance of early project stages like data cleaning and exploration. To overcome data science project challeges :

• Spend enough time on data preparation
• Focus on exploratory data analysis to uncover patterns before jumping into modeling

By following these strategies, you'll build a portfolio of data science projects that shows off your range of skills. Each one is an opportunity to sharpen your abilities and demonstrate your potential as a data scientist.

Real learner, real results

Take it from Aleksey Korshuk , who leveraged Dataquest's project-based curriculum to gain practical data science skills and build an impressive portfolio of projects:

The general knowledge that Dataquest provides is easily implemented into your projects and used in practice.

Through hands-on projects, Aleksey gained real-world experience solving complex problems and applying his knowledge effectively. He encourages other learners to stay persistent and make time for consistent learning:

I suggest that everyone set a goal, find friends in communities who share your interests, and work together on cool projects. Don't give up halfway!

Aleksey's journey showcases the power of a project-based approach for anyone looking to build their data skills. By building practical projects and collaborating with others, you can develop in-demand skills and accomplish your goals, just like Aleksey did with Dataquest.

21 Data Science Project Ideas

Excited to dive into a data science project? We've put together a collection of 21 varied projects that are perfect for beginners and apply to real-world scenarios. From analyzing app market data to exploring financial trends, these projects are organized by difficulty level, making it easy for you to choose a project that matches your current skill level while also offering more challenging options to tackle as you progress.

Beginner Data Science Projects

• Profitable App Profiles for the App Store and Google Play Markets
• Exploring Hacker News Posts
• Exploring eBay Car Sales Data
• Finding Heavy Traffic Indicators on I-94
• Storytelling Data Visualization on Exchange Rates
• Clean and Analyze Employee Exit Surveys
• Star Wars Survey

Intermediate Data Science Projects

• Exploring Financial Data using Nasdaq Data Link API
• Popular Data Science Questions
• Investigating Fandango Movie Ratings
• Finding the Best Markets to Advertise In
• Mobile App for Lottery Addiction
• Building a Spam Filter with Naive Bayes
• Winning Jeopardy

Advanced Data Science Projects

• Predicting Heart Disease
• Credit Card Customer Segmentation
• Predicting Insurance Costs
• Classifying Heart Disease
• Predicting Employee Productivity Using Tree Models
• Optimizing Model Prediction
• Predicting Listing Gains in the Indian IPO Market Using TensorFlow

In the following sections, you'll find detailed instructions for each project. We'll cover the tools you'll use and the skills you'll develop. This structured approach will guide you through key data science techniques across various applications.

1. Profitable App Profiles for the App Store and Google Play Markets

Difficulty Level: Beginner

In this beginner-level data science project, you'll step into the role of a data scientist for a company that builds ad-supported mobile apps. Using Python and Jupyter Notebook, you'll analyze real datasets from the Apple App Store and Google Play Store to identify app profiles that attract the most users and generate the highest revenue. By applying data cleaning techniques, conducting exploratory data analysis, and making data-driven recommendations, you'll develop practical skills essential for entry-level data science positions.

Tools and Technologies

• Jupyter Notebook

Prerequisites

To successfully complete this project, you should be comfortable with Python fundamentals such as:

• Variables, data types, lists, and dictionaries
• Writing functions with arguments, return statements, and control flow
• Using conditional logic and loops for data manipulation
• Working with Jupyter Notebook to write, run, and document code

Step-by-Step Instructions

• Open and explore the App Store and Google Play datasets
• Clean the datasets by removing non-English apps and duplicate entries
• Analyze app genres and categories using frequency tables
• Identify app profiles that attract the most users
• Develop data-driven recommendations for the company's next app development project

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

• Cleaning and preparing real-world datasets for analysis using Python
• Conducting exploratory data analysis to identify trends in app markets
• Applying frequency analysis to derive insights from data
• Translating data findings into actionable business recommendations

Relevant Links and Resources

• Example Solution Code

2. Exploring Hacker News Posts

In this beginner-level data science project, you'll analyze a dataset of submissions to Hacker News, a popular technology-focused news aggregator. Using Python and Jupyter Notebook, you'll explore patterns in post creation times, compare engagement levels between different post types, and identify the best times to post for maximum comments. This project will strengthen your skills in data manipulation, analysis, and interpretation, providing valuable experience for aspiring data scientists.

To successfully complete this project, you should be comfortable with Python concepts for data science such as:

• String manipulation and basic text processing
• Working with dates and times using the datetime module
• Using loops to iterate through data collections
• Basic data analysis techniques like calculating averages and sorting
• Creating and manipulating lists and dictionaries
• Load and explore the Hacker News dataset, focusing on post titles and creation times
• Separate and analyze 'Ask HN' and 'Show HN' posts
• Calculate and compare the average number of comments for different post types
• Determine the relationship between post creation time and comment activity
• Identify the optimal times to post for maximum engagement
• Manipulating strings and datetime objects in Python for data analysis
• Calculating and interpreting averages to compare dataset subgroups
• Identifying time-based patterns in user engagement data
• Translating data insights into practical posting strategies
• Original Hacker News Posts dataset on Kaggle

3. Exploring eBay Car Sales Data

In this beginner-level data science project, you'll analyze a dataset of used car listings from eBay Kleinanzeigen, a classifieds section of the German eBay website. Using Python and pandas, you'll clean the data, explore the included listings, and uncover insights about used car prices, popular brands, and the relationships between various car attributes. This project will strengthen your data cleaning and exploratory data analysis skills, providing valuable experience in working with real-world, messy datasets.

To successfully complete this project, you should be comfortable with pandas fundamentals and have experience with:

• Loading and inspecting data using pandas
• Cleaning column names and handling missing data
• Using pandas to filter, sort, and aggregate data
• Creating basic visualizations with pandas
• Handling data type conversions in pandas
• Load the dataset and perform initial data exploration
• Clean column names and convert data types as necessary
• Analyze the distribution of car prices and registration years
• Explore relationships between brand, price, and vehicle type
• Investigate the impact of car age on pricing
• Cleaning and preparing a real-world dataset using pandas
• Performing exploratory data analysis on a large dataset
• Creating data visualizations to communicate findings effectively
• Deriving actionable insights from used car market data
• Original eBay Kleinanzeigen Dataset on Kaggle

4. Finding Heavy Traffic Indicators on I-94

In this beginner-level data science project, you'll analyze a dataset of westbound traffic on the I-94 Interstate highway between Minneapolis and St. Paul, Minnesota. Using Python and popular data visualization libraries, you'll explore traffic volume patterns to identify indicators of heavy traffic. You'll investigate how factors such as time of day, day of the week, weather conditions, and holidays impact traffic volume. This project will enhance your skills in exploratory data analysis and data visualization, providing valuable experience in deriving actionable insights from real-world time series data.

To successfully complete this project, you should be comfortable with data visualization in Python techniques and have experience with:

• Data manipulation and analysis using pandas
• Creating various plot types (line, bar, scatter) with Matplotlib
• Enhancing visualizations using seaborn
• Interpreting time series data and identifying patterns
• Basic statistical concepts like correlation and distribution
• Load and perform initial exploration of the I-94 traffic dataset
• Visualize traffic volume patterns over time using line plots
• Analyze traffic volume distribution by day of the week and time of day
• Investigate the relationship between weather conditions and traffic volume
• Identify and visualize other factors correlated with heavy traffic
• Creating and interpreting complex data visualizations using Matplotlib and seaborn
• Analyzing time series data to uncover temporal patterns and trends
• Using visual exploration techniques to identify correlations in multivariate data
• Communicating data insights effectively through clear, informative plots
• Original Metro Interstate Traffic Volume Data Set

5. Storytelling Data Visualization on Exchange Rates

In this beginner-level data science project, you'll create a storytelling data visualization about Euro exchange rates against the US Dollar. Using Python and Matplotlib, you'll analyze historical exchange rate data from 1999 to 2021, identifying key trends and events that have shaped the Euro-Dollar relationship. You'll apply data visualization principles to clean data, develop a narrative around exchange rate fluctuations, and create an engaging and informative visual story. This project will strengthen your ability to communicate complex financial data insights effectively through visual storytelling.

To successfully complete this project, you should be familiar with storytelling through data visualization techniques and have experience with:

• Creating and customizing plots with Matplotlib
• Applying design principles to enhance data visualizations
• Working with time series data in Python
• Basic understanding of exchange rates and economic indicators
• Load and explore the Euro-Dollar exchange rate dataset
• Clean the data and calculate rolling averages to smooth out fluctuations
• Identify significant trends and events in the exchange rate history
• Develop a narrative that explains key patterns in the data
• Create a polished line plot that tells your exchange rate story
• Crafting a compelling narrative around complex financial data
• Designing clear, informative visualizations that support your story
• Using Matplotlib to create publication-quality line plots with annotations
• Applying color theory and typography to enhance visual communication
• ECB Euro reference exchange rate: US dollar

6. Clean and Analyze Employee Exit Surveys

In this beginner-level data science project, you'll analyze employee exit surveys from the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. Using Python and pandas, you'll clean messy data, combine datasets, and uncover insights into resignation patterns. You'll investigate factors such as years of service, age groups, and job dissatisfaction to understand why employees leave. This project offers hands-on experience in data cleaning and exploratory analysis, essential skills for aspiring data analysts.

To successfully complete this project, you should be familiar with data cleaning techniques in Python and have experience with:

• Basic pandas operations for data manipulation
• Handling missing data and data type conversions
• Merging and concatenating DataFrames
• Using string methods in pandas for text data cleaning
• Basic data analysis and aggregation techniques
• Load and explore the DETE and TAFE exit survey datasets
• Clean column names and handle missing values in both datasets
• Standardize and combine the "resignation reasons" columns
• Merge the DETE and TAFE datasets for unified analysis
• Analyze resignation reasons and their correlation with employee characteristics
• Applying data cleaning techniques to prepare messy, real-world datasets
• Combining data from multiple sources using pandas merge and concatenate functions
• Creating new categories from existing data to facilitate analysis
• Conducting exploratory data analysis to uncover trends in employee resignations
• DETE Exit Survey Dataset

7. Star Wars Survey

In this beginner-level data science project, you'll analyze survey data about the Star Wars film franchise. Using Python and pandas, you'll clean and explore data collected by FiveThirtyEight to uncover insights about fans' favorite characters, film rankings, and how opinions vary across different demographic groups. You'll practice essential data cleaning techniques like handling missing values and converting data types, while also conducting basic statistical analysis to reveal trends in Star Wars fandom.

To successfully complete this project, you should be familiar with combining, analyzing, and visualizing data while having experience with:

• Converting data types in pandas DataFrames
• Filtering and sorting data
• Basic data aggregation and analysis techniques
• Load the Star Wars survey data and explore its structure
• Analyze the rankings of Star Wars films among respondents
• Explore viewership and character popularity across different demographics
• Investigate the relationship between fan characteristics and their opinions
• Applying data cleaning techniques to prepare survey data for analysis
• Using pandas to explore and manipulate structured data
• Performing basic statistical analysis on categorical and numerical data
• Interpreting survey results to draw meaningful conclusions about fan preferences
• Original Star Wars Survey Data on GitHub

8. Exploring Financial Data using Nasdaq Data Link API

Difficulty Level: Intermediate

In this beginner-friendly data science project, you'll analyze real-world economic data to uncover market trends. Using Python, you'll interact with the Nasdaq Data Link API to retrieve financial datasets, including stock prices and economic indicators. You'll apply data wrangling techniques to clean and structure the data, then use pandas and Matplotlib to analyze and visualize trends in stock performance and economic metrics. This project provides hands-on experience in working with financial APIs and analyzing market data, skills that are highly valuable in data-driven finance roles.

• requests (for API calls)

To successfully complete this project, you should be familiar with working with APIs and web scraping in Python , and have experience with:

• Making HTTP requests and handling responses using the requests library
• Parsing JSON data in Python
• Data manipulation and analysis using pandas DataFrames
• Creating line plots and other basic visualizations with Matplotlib
• Basic understanding of financial terms and concepts
• Set up authentication for the Nasdaq Data Link API
• Retrieve historical stock price data for a chosen company
• Clean and structure the API response data using pandas
• Analyze stock price trends and calculate key statistics
• Fetch and analyze additional economic indicators
• Create visualizations to illustrate relationships between different financial metrics
• Interacting with financial APIs to retrieve real-time and historical market data
• Cleaning and structuring JSON data for analysis using pandas
• Calculating financial metrics such as returns and moving averages
• Creating informative visualizations of stock performance and economic trends
• Nasdaq Data Link API Documentation

9. Popular Data Science Questions

In this beginner-friendly data science project, you'll analyze data from Data Science Stack Exchange to uncover trends in the data science field. You'll identify the most frequently asked questions, popular technologies, and emerging topics. Using SQL and Python, you'll query a database to extract post data, then use pandas to clean and analyze it. You'll visualize trends over time and across different subject areas, gaining insights into the evolving landscape of data science. This project offers hands-on experience in combining SQL, data analysis, and visualization skills to derive actionable insights from a real-world dataset.

To successfully complete this project, you should be familiar with querying databases with SQL and Python and have experience with:

• Writing SQL queries to extract data from relational databases
• Data cleaning and manipulation using pandas DataFrames
• Basic data analysis techniques like grouping and aggregation
• Creating line plots and bar charts with Matplotlib
• Interpreting trends and patterns in data
• Connect to the Data Science Stack Exchange database and explore its structure
• Write SQL queries to extract data on questions, tags, and view counts
• Use pandas to clean the extracted data and prepare it for analysis
• Analyze the distribution of questions across different tags and topics
• Investigate trends in question popularity and topic relevance over time
• Visualize key findings using Matplotlib to illustrate data science trends
• Extracting specific data from a relational database using SQL queries
• Cleaning and preprocessing text data for analysis using pandas
• Identifying trends and patterns in data science topics over time
• Creating meaningful visualizations to communicate insights about the data science field
• Data Science Stack Exchange Data Explorer

10. Investigating Fandango Movie Ratings

In this beginner-friendly data science project, you'll investigate potential bias in Fandango's movie rating system. Following up on a 2015 analysis that found evidence of inflated ratings, you'll compare 2015 and 2016 movie ratings data to determine if Fandango's system has changed. Using Python, you'll perform statistical analysis to compare rating distributions, calculate summary statistics, and visualize changes in rating patterns. This project will strengthen your skills in data manipulation, statistical analysis, and data visualization while addressing a real-world question of rating integrity.

To successfully complete this project, you should be familiar with fundamental statistics concepts and have experience with:

• Data manipulation using pandas (e.g., loading data, filtering, sorting)
• Calculating and interpreting summary statistics in Python
• Creating and customizing plots with matplotlib
• Comparing distributions using statistical methods
• Interpreting results in the context of the research question
• Load the 2015 and 2016 Fandango movie ratings datasets using pandas
• Clean the data and isolate the samples needed for analysis
• Compare the distribution shapes of 2015 and 2016 ratings using kernel density plots
• Calculate and compare summary statistics for both years
• Analyze the frequency of each rating class (e.g., 4.5 stars, 5 stars) for both years
• Determine if there's evidence of a change in Fandango's rating system
• Conducting a comparative analysis of rating distributions using Python
• Applying statistical techniques to investigate potential bias in ratings
• Creating informative visualizations to illustrate changes in rating patterns
• Drawing and communicating data-driven conclusions about rating system integrity
• Original FiveThirtyEight Article on Fandango Ratings

11. Finding the Best Markets to Advertise In

In this beginner-friendly data science project, you'll analyze survey data from freeCodeCamp to determine the best markets for an e-learning company to advertise its programming courses. Using Python and pandas, you'll explore the demographics of new coders, their locations, and their willingness to pay for courses. You'll clean the data, handle outliers, and use frequency analysis to identify countries with the most potential customers. By the end, you'll provide data-driven recommendations on where the company should focus its advertising efforts to maximize its return on investment.

To successfully complete this project, you should have a solid grasp on how to summarize distributions using measures of central tendency, interpret variance using z-scores , and have experience with:

• Filtering and sorting DataFrames
• Handling missing data and outliers
• Calculating summary statistics (mean, median, mode)
• Creating and manipulating new columns based on existing data
• Load the freeCodeCamp 2017 New Coder Survey data
• Identify and handle missing values in the dataset
• Analyze the distribution of participants across different countries
• Calculate the average amount students are willing to pay for courses by country
• Identify and handle outliers in the monthly spending data
• Determine the top countries based on number of potential customers and their spending power
• Cleaning and preprocessing survey data for analysis using pandas
• Applying frequency analysis to identify key markets
• Handling outliers to ensure accurate calculations of spending potential
• Combining multiple factors to make data-driven business recommendations
• freeCodeCamp 2017 New Coder Survey Results

12. Mobile App for Lottery Addiction

In this beginner-friendly data science project, you'll develop the core logic for a mobile app aimed at helping lottery addicts better understand their chances of winning. Using Python, you'll create functions to calculate probabilities for the 6/49 lottery game, including the chances of winning the big prize, any prize, and the expected return on buying a ticket. You'll also compare lottery odds to real-life situations to provide context. This project will strengthen your skills in probability theory, Python programming, and applying mathematical concepts to real-world problems.

To successfully complete this project, you should be familiar with probability fundamentals and have experience with:

• Writing functions in Python with multiple parameters
• Implementing combinatorics calculations (factorials, combinations)
• Working with control structures (if statements, for loops)
• Performing mathematical operations in Python
• Basic set theory and probability concepts
• Implement the factorial and combinations functions for probability calculations
• Create a function to calculate the probability of winning the big prize in a 6/49 lottery
• Develop a function to calculate the probability of winning any prize
• Design a function to compare lottery odds with real-life event probabilities
• Implement a function to calculate the expected return on buying a lottery ticket
• Implementing complex probability calculations using Python functions
• Translating mathematical concepts into practical programming solutions
• Creating user-friendly outputs to effectively communicate probability concepts
• Applying programming skills to address a real-world social issue

13. Building a Spam Filter with Naive Bayes

In this beginner-friendly data science project, you'll build a spam filter using the multinomial Naive Bayes algorithm. Working with the SMS Spam Collection dataset, you'll implement the algorithm from scratch to classify messages as spam or ham (non-spam). You'll calculate word frequencies, prior probabilities, and conditional probabilities to make predictions. This project will deepen your understanding of probabilistic machine learning algorithms, text classification, and the practical application of Bayesian methods in natural language processing.

To successfully complete this project, you should be familiar with conditional probability and have experience with:

• Python programming, including working with dictionaries and lists
• Understand probability concepts like conditional probability and Bayes' theorem
• Text processing techniques (tokenization, lowercasing)
• Pandas for data manipulation
• Understanding of the Naive Bayes algorithm and its assumptions
• Load and explore the SMS Spam Collection dataset
• Preprocess the text data by tokenizing and cleaning the messages
• Calculate the prior probabilities for spam and ham messages
• Compute word frequencies and conditional probabilities
• Implement the Naive Bayes algorithm to classify messages
• Test the model and evaluate its accuracy on unseen data
• Implementing the multinomial Naive Bayes algorithm from scratch
• Applying Bayesian probability calculations in a real-world context
• Preprocessing text data for machine learning applications
• Evaluating a text classification model's performance
• SMS Spam Collection Dataset

14. Winning Jeopardy

In this beginner-friendly data science project, you'll analyze a dataset of Jeopardy questions to uncover patterns that could give you an edge in the game. Using Python and pandas, you'll explore over 200,000 Jeopardy questions and answers, focusing on identifying terms that appear more often in high-value questions. You'll apply text processing techniques, use the chi-squared test to validate your findings, and develop strategies for maximizing your chances of winning. This project will strengthen your data manipulation skills and introduce you to practical applications of natural language processing and statistical testing.

To successfully complete this project, you should be familiar with intermediate statistics concepts like significance and hypothesis testing with experience in:

• String operations and basic regular expressions in Python
• Implementing the chi-squared test for statistical analysis
• Working with CSV files and handling data type conversions
• Basic natural language processing concepts (e.g., tokenization)
• Load the Jeopardy dataset and perform initial data exploration
• Clean and preprocess the data, including normalizing text and converting dollar values
• Implement a function to find the number of times a term appears in questions
• Create a function to compare the frequency of terms in low-value vs. high-value questions
• Apply the chi-squared test to determine if certain terms are statistically significant
• Analyze the results to develop strategies for Jeopardy success
• Processing and analyzing large text datasets using pandas
• Applying statistical tests to validate hypotheses in data analysis
• Implementing custom functions for text analysis and frequency comparisons
• Deriving actionable insights from complex datasets to inform game strategy
• J! Archive - Fan-created archive of Jeopardy! games and players

15. Predicting Heart Disease

Difficulty Level: Advanced

In this challenging but guided data science project, you'll build a K-Nearest Neighbors (KNN) classifier to predict the risk of heart disease. Using a dataset from the UCI Machine Learning Repository, you'll work with patient features such as age, sex, chest pain type, and cholesterol levels to classify patients as having a high or low risk of heart disease. You'll explore the impact of different features on the prediction, optimize the model's performance, and interpret the results to identify key risk factors. This project will strengthen your skills in data preprocessing, exploratory data analysis, and implementing classification algorithms for healthcare applications.

• scikit-learn

To successfully complete this project, you should be familiar with supervised machine learning in Python and have experience with:

• Implementing machine learning workflows with scikit-learn
• Understanding and interpreting classification metrics (accuracy, precision, recall)
• Feature scaling and preprocessing techniques
• Basic data visualization with Matplotlib
• Load and explore the heart disease dataset from the UCI Machine Learning Repository
• Preprocess the data, including handling missing values and scaling features
• Split the data into training and testing sets
• Implement a KNN classifier and evaluate its initial performance
• Optimize the model by tuning the number of neighbors (k)
• Analyze feature importance and their impact on heart disease prediction
• Interpret the results and summarize key findings for healthcare professionals
• Implementing and optimizing a KNN classifier for medical diagnosis
• Evaluating model performance using various metrics in a healthcare context
• Analyzing feature importance in predicting heart disease risk
• Translating machine learning results into actionable healthcare insights
• UCI Machine Learning Repository: Heart Disease Dataset

16. Credit Card Customer Segmentation

In this challenging but guided data science project, you'll perform customer segmentation for a credit card company using unsupervised learning techniques. You'll analyze customer attributes such as credit limit, purchases, cash advances, and payment behaviors to identify distinct groups of credit card users. Using the K-means clustering algorithm, you'll segment customers based on their spending habits and credit usage patterns. This project will strengthen your skills in data preprocessing, exploratory data analysis, and applying machine learning for deriving actionable business insights in the financial sector.

To successfully complete this project, you should be familiar with unsupervised machine learning in Python and have experience with:

• Implementing K-means clustering with scikit-learn
• Feature scaling and dimensionality reduction techniques
• Creating scatter plots and pair plots with Matplotlib and seaborn
• Interpreting clustering results in a business context
• Load and explore the credit card customer dataset
• Perform exploratory data analysis to understand relationships between customer attributes
• Apply principal component analysis (PCA) for dimensionality reduction
• Implement K-means clustering on the transformed data
• Visualize the clusters using scatter plots of the principal components
• Analyze cluster characteristics to develop customer profiles
• Propose targeted strategies for each customer segment
• Applying K-means clustering to segment customers in the financial sector
• Using PCA for dimensionality reduction in high-dimensional datasets
• Interpreting clustering results to derive meaningful customer profiles
• Translating data-driven insights into actionable marketing strategies
• Credit Card Dataset for Clustering on Kaggle

17. Predicting Insurance Costs

In this challenging but guided data science project, you'll predict patient medical insurance costs using linear regression. Working with a dataset containing features such as age, BMI, number of children, smoking status, and region, you'll develop a model to estimate insurance charges. You'll explore the relationships between these factors and insurance costs, handle categorical variables, and interpret the model's coefficients to understand the impact of each feature. This project will strengthen your skills in regression analysis, feature engineering, and deriving actionable insights in the healthcare insurance domain.

To successfully complete this project, you should be familiar with linear regression modeling in Python and have experience with:

• Implementing linear regression models with scikit-learn
• Handling categorical variables (e.g., one-hot encoding)
• Evaluating regression models using metrics like R-squared and RMSE
• Creating scatter plots and correlation heatmaps with seaborn
• Load and explore the insurance cost dataset
• Perform data preprocessing, including handling categorical variables
• Conduct exploratory data analysis to visualize relationships between features and insurance costs
• Create training/testing sets to build and train a linear regression model using scikit-learn
• Make predictions on the test set and evaluate the model's performance
• Visualize the actual vs. predicted values and residuals
• Implementing end-to-end linear regression analysis for cost prediction
• Handling categorical variables in regression models
• Interpreting regression coefficients to derive business insights
• Evaluating model performance and understanding its limitations in healthcare cost prediction
• Medical Cost Personal Datasets on Kaggle

18. Classifying Heart Disease

In this challenging but guided data science project, you'll work with the Cleveland Clinic Foundation heart disease dataset to develop a logistic regression model for predicting heart disease. You'll analyze features such as age, sex, chest pain type, blood pressure, and cholesterol levels to classify patients as having or not having heart disease. Through this project, you'll gain hands-on experience in data preprocessing, model building, and interpretation of results in a medical context, strengthening your skills in classification techniques and feature analysis.

To successfully complete this project, you should be familiar with logistic regression modeling in Python and have experience with:

• Implementing logistic regression models with scikit-learn
• Evaluating classification models using metrics like accuracy, precision, and recall
• Interpreting model coefficients and odds ratios
• Creating confusion matrices and ROC curves with seaborn and Matplotlib
• Load and explore the Cleveland Clinic Foundation heart disease dataset
• Perform data preprocessing, including handling missing values and encoding categorical variables
• Conduct exploratory data analysis to visualize relationships between features and heart disease presence
• Create training/testing sets to build and train a logistic regression model using scikit-learn
• Visualize the ROC curve and calculate the AUC score
• Summarize findings and discuss the model's potential use in medical diagnosis
• Implementing end-to-end logistic regression analysis for medical diagnosis
• Interpreting odds ratios to understand risk factors for heart disease
• Evaluating classification model performance using various metrics
• Communicating the potential and limitations of machine learning in healthcare

19. Predicting Employee Productivity Using Tree Models

In this challenging but guided data science project, you'll analyze employee productivity in a garment factory using tree-based models. You'll work with a dataset containing factors such as team, targeted productivity, style changes, and working hours to predict actual productivity. By implementing both decision trees and random forests, you'll compare their performance and interpret the results to provide actionable insights for improving workforce efficiency. This project will strengthen your skills in tree-based modeling, feature importance analysis, and applying machine learning to solve real-world business problems in manufacturing.

To successfully complete this project, you should be familiar with decision trees and random forest modeling and have experience with:

• Implementing decision trees and random forests with scikit-learn
• Evaluating regression models using metrics like MSE and R-squared
• Interpreting feature importance in tree-based models
• Creating visualizations of tree structures and feature importance with Matplotlib
• Load and explore the employee productivity dataset
• Perform data preprocessing, including handling categorical variables and scaling numerical features
• Create training/testing sets to build and train a decision tree regressor using scikit-learn
• Visualize the decision tree structure and interpret the rules
• Implement a random forest regressor and compare its performance to the decision tree
• Analyze feature importance to identify key factors affecting productivity
• Fine-tune the random forest model using grid search
• Summarize findings and provide recommendations for improving employee productivity
• Implementing and comparing decision trees and random forests for regression tasks
• Interpreting tree structures to understand decision-making processes in productivity prediction
• Analyzing feature importance to identify key drivers of employee productivity
• Applying hyperparameter tuning techniques to optimize model performance
• UCI Machine Learning Repository: Garment Employee Productivity Dataset

20. Optimizing Model Prediction

In this challenging but guided data science project, you'll work on predicting the extent of damage caused by forest fires using the UCI Machine Learning Repository's Forest Fires dataset. You'll analyze features such as temperature, relative humidity, wind speed, and various fire weather indices to estimate the burned area. Using Python and scikit-learn, you'll apply advanced regression techniques, including feature engineering, cross-validation, and regularization, to build and optimize linear regression models. This project will strengthen your skills in model selection, hyperparameter tuning, and interpreting complex model results in an environmental context.

To successfully complete this project, you should be familiar with optimizing machine learning models and have experience with:

• Implementing and evaluating linear regression models using scikit-learn
• Applying cross-validation techniques to assess model performance
• Understanding and implementing regularization methods (Ridge, Lasso)
• Performing hyperparameter tuning using grid search
• Interpreting model coefficients and performance metrics
• Load and explore the Forest Fires dataset, understanding the features and target variable
• Preprocess the data, handling any missing values and encoding categorical variables
• Perform feature engineering, creating interaction terms and polynomial features
• Implement a baseline linear regression model and evaluate its performance
• Apply k-fold cross-validation to get a more robust estimate of model performance
• Implement Ridge and Lasso regression models to address overfitting
• Use grid search with cross-validation to optimize regularization hyperparameters
• Compare the performance of different models using appropriate metrics (e.g., RMSE, R-squared)
• Interpret the final model, identifying the most important features for predicting fire damage
• Visualize the results and discuss the model's limitations and potential improvements
• Implementing advanced regression techniques to optimize model performance
• Applying cross-validation and regularization to prevent overfitting
• Conducting hyperparameter tuning to find the best model configuration
• Interpreting complex model results in the context of environmental science
• UCI Machine Learning Repository: Forest Fires Dataset

21. Predicting Listing Gains in the Indian IPO Market Using TensorFlow

In this challenging but guided data science project, you'll develop a deep learning model using TensorFlow to predict listing gains in the Indian Initial Public Offering (IPO) market. You'll analyze historical IPO data, including features such as issue price, issue size, subscription rates, and market conditions, to forecast the percentage increase in share price on the day of listing. By implementing a neural network classifier, you'll categorize IPOs into different ranges of listing gains. This project will strengthen your skills in deep learning, financial data analysis, and using TensorFlow for real-world predictive modeling tasks in the finance sector.

To successfully complete this project, you should be familiar with deep learning in TensorFlow and have experience with:

• Building and training neural networks using TensorFlow and Keras
• Preprocessing financial data for machine learning tasks
• Implementing classification models and interpreting their results
• Evaluating model performance using metrics like accuracy and confusion matrices
• Basic understanding of IPOs and stock market dynamics
• Load and explore the Indian IPO dataset using pandas
• Preprocess the data, including handling missing values and encoding categorical variables
• Engineer features relevant to IPO performance prediction
• Split the data into training/testing sets then design a neural network architecture using Keras
• Compile and train the model on the training data
• Evaluate the model's performance on the test set
• Fine-tune the model by adjusting hyperparameters and network architecture
• Analyze feature importance using the trained model
• Visualize the results and interpret the model's predictions in the context of IPO investing
• Implementing deep learning models for financial market prediction using TensorFlow
• Preprocessing and engineering features for IPO performance analysis
• Evaluating and interpreting classification results in the context of IPO investments
• Applying deep learning techniques to solve real-world financial forecasting problems
• Securities and Exchange Board of India (SEBI) IPO Statistics

How to Prepare for a Data Science Job

Landing a data science job requires strategic preparation. Here's what you need to know to stand out in this competitive field:

• Research job postings to understand employer expectations
• Develop relevant skills through structured learning
• Build a portfolio of hands-on projects
• Prepare for interviews and optimize your resume
• Commit to continuous learning

Research Job Postings

Start by understanding what employers are looking for. Check out data science job listings on these platforms:

Steps to Get Job-Ready

Focus on these key areas:

• Skill Development: Enhance your programming, data analysis, and machine learning skills. Consider a structured program like Dataquest's Data Scientist in Python path .
• Hands-On Projects: Apply your skills to real projects. This builds your portfolio of data science projects and demonstrates your abilities to potential employers.
• Put Your Portfolio Online: Showcase your projects online. GitHub is an excellent platform for hosting and sharing your work.

Pick Your Top 3 Data Science Projects

Your projects are concrete evidence of your skills. In applications and interviews, highlight your top 3 data science projects that demonstrate:

• Critical thinking
• Technical proficiency
• Problem-solving abilities

We have a ton of great tips on how to create a project portfolio for data science job applications .

Resume and Interview Preparation

Your resume should clearly outline your project experiences and skills . When getting ready for data science interviews , be prepared to discuss your projects in great detail. Practice explaining your work concisely and clearly.

Job Preparation Advice

Preparing for a data science job can be daunting. If you're feeling overwhelmed:

• Remember that everyone starts somewhere
• Connect with mentors for guidance
• Join the Dataquest community for support and feedback on your data science projects

Continuous Learning

Data science is an evolving field. To stay relevant:

• Keep up with industry trends
• Stay curious and open to new technologies
• Look for ways to apply your skills to real-world problems

Preparing for a data science job involves understanding employer expectations, building relevant skills, creating a strong portfolio, refining your resume, preparing for interviews, addressing challenges, and committing to ongoing learning. With dedication and the right approach, you can position yourself for success in this dynamic field.

Data science projects are key to developing your skills and advancing your data science career. Here's why they matter:

• They provide hands-on experience with real-world problems
• They help you build a portfolio to showcase your abilities
• They boost your confidence in handling complex data challenges

In this post, we've explored 21 beginner-friendly data science project ideas ranging from easier to harder. These projects go beyond just technical skills. They're designed to give you practical experience in solving real-world data problems – a crucial asset for any data science professional.

We encourage you to start with any of these beginner data science projects that interests you. Each one is structured to help you apply your skills to realistic scenarios, preparing you for professional data challenges. While some of these projects use SQL, you'll want to check out our post on 10 Exciting SQL Project Ideas for Beginners for dedicated SQL project ideas to add to your data science portfolio of projects.

Hands-on projects are valuable whether you're new to the field or looking to advance your career. Start building your project portfolio today by selecting from the diverse range of ideas we've shared. It's an important step towards achieving your data science career goals.

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12 Data Science Projects for Beginners and Experts

Data science is a booming industry. Try your hand at these projects to develop your skills and keep up with the latest trends.

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills to comprehend and manage the upcoming challenges facing the profession. The purpose behind this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills .

12 Data Science Projects to Experiment With

• Building chatbots.
• Credit card fraud detection.
• Fake news detection.
• Forest fire prediction.
• Classifying breast cancer.
• Driver drowsiness detection.
• Recommender systems.
• Sentiment analysis.
• Exploratory data analysis.
• Gender detection and age detection.
• Recognizing speech emotion.
• Customer segmentation.

Top Data Science Projects

Understanding data science can be quite confusing at first, but with consistent practice, you’ll start to grasp the various notions and terminologies in the subject. The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive.

In this section, we’ll share a handful of fun and interesting project ideas with you spread across all skill levels ranging from beginners to intermediate to veterans.

More on Data Science: How to Build Optical Character Recognition (OCR) in Python

1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code: Build Your First Python Chatbot Project

Chatbots play a pivotal role for businesses as they can effortlessly   without any slowdown. They automate a majority of the customer service process,  single-handedly reducing the customer service workload. The chatbots utilize a variety of techniques backed with artificial intelligence, machine learning and data science.

Chatbots analyze the input from the customer and reply with an appropriate mapped response. To train the chatbot, you can use recurrent neural networks with the intents JSON dataset , while the implementation can be handled using Python . Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase.

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code: Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think, and lately, they’ve been on the rise. We’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees , artificial neural networks , and logistic regression . As you feed more data to your system, you should be able to increase its overall accuracy.

3. Fake News Detection

• Data set/Packages: news.csv
• Source code: Detecting Fake News

Fake news needs no introduction. In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn . For the data set, you can use News.csv.

4. Forest Fire Prediction

Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to  nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

More on Data Science: K-Nearest Neighbor Algorithm: An Introduction

5. Classifying Breast Cancer

• Data set: IDC (Invasive Ductal Carcinoma)
• Source code: Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can try building a breast cancer detection system using Python. Breast cancer cases have been on the rise, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use NumPy , OpenCV , TensorFlow , Keras, scikit-learn and Matplotlib .

6. Driver Drowsiness Detection

• Source code: Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers. One of the best ways to prevent this is to implement a drowsiness detection system.

A driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives .

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a deep learning model and libraries such as OpenCV , TensorFlow , Pygame , and Keras .

More on Data Science: 8 Data Visualization Tools That Every Data Scientist Should Know

7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code: Movie Recommendation System Project in R

Have you ever wondered how media platforms like YouTube, Netflix and others recommend what to watch next? They use a tool called the recommender/recommendation system . It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that then generates what the user might like to watch next.

Based on your preferences and input data, you can try to build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use recommenderlab , ggplot2 , reshap2 and data.table.

8. Sentiment Analysis

• Data set: janeaustenR
• Source code: Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is a tool backed by artificial intelligence, which essentially allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews or survey responses, and could span a range of emotions such as happy, angry, positive, love, negative, excitement and more.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them the critical insight into the people’s reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s data set along with the tidytext package .

9. Exploratory Data Analysis

• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code: Exploratory data analysis in Python

Data analysis starts with exploratory data analysis (EDA). It plays a key role in the data analysis process as it helps you make sense of your data and often involves visualizing them for better exploration. For visualization , you can pick from a range of options, including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified the patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn and matplotlib.

A great source for EDA data sets is the IBM Analytics Community .

10. Gender Detection and Age Prediction

• Data set: Adience
• Packages: OpenCV
• Source code: OpenCV Age Detection with Deep Learning

Identified as a classification problem, this gender detection and age prediction project will put both your machine learning and computer vision skills to the test. The goal is to build a system that takes a person’s image and tries to identify their age and gender.

For this project, you can implement convolutional neural networks and use Python with the OpenCV package . You can grab the Adience dataset for this project. Factors such as makeup, lighting and facial expressions will make this challenging and try to throw your model off, so keep that in mind.

11. Recognizing Speech Emotions

• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code: Speech Emotion Recognition with librosa

Speech is one of the most fundamental ways of expressing ourselves, and it contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By analyzing the emotions behind speech, it’s possible to use this information to restructure our actions,  services and even products, to offer a more personalized service to specific individuals.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa , SoundFile , NumPy, Scikit-learn, and PyAaudio packages. For the data set, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) , which contains over 7300 files.

12. Customer Segmentation

• Source code: Customer Segmentation using Machine Learning

Modern businesses strive by delivering highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region, interests, and so on. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology. As one of the hottest, in-demand professions in the industry, the future of data science holds many promises. But to make the most out of the upcoming opportunities, you need to be prepared to take on the challenges it brings.

Frequently Asked Questions

What projects can be done in data science.

• Build a chatbot using Python.
• Create a movie recommendation system using R.
• Detect credit card fraud using R or Python.

How do I start a data science project?

To start a data science project, first decide what sort of data science project you want to undertake, such as data cleaning, data analysis or data visualization. Then, find a good dataset on a website like data.world or data.gov. From there, you can analyze the data and communicate your results.

How long does a data science project take to complete?

Data science projects vary in length and depend on several variables like the data source, the complexity of the problem you’re trying to solve and your skill level. It could take a few hours or several months.

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Data Science Principles

Are you prepared for our data-driven world.

Data Science Principles is a Harvard Online course that gives you an overview of data science with a code- and math-free introduction to prediction, causality, data wrangling, privacy, and ethics.

What You'll Learn

What is data science, and how can it help you make sense of the infinite data, metrics, and tools that are available today? 

Data science is at the core of any growing modern business, from health care to government to advertising and more. Insights gathered from data science collection and analysis practices have the potential to increase quality, effectiveness, and efficiency of work output in professional and personal situations. 

Data Science Principles makes the foundational topics in data science approachable and relevant by using real-world examples that prompt you to think critically about applying these understandings to your workplace. Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics. 

Data Science Principles is an introduction to data science course for anyone who wants to positively impact outcomes and understand insights from their company’s data collection and analysis efforts. This online certificate course will prepare you to speak the language of data science and contribute to data-oriented discussions within your company and daily life. This is a course for beginners and managers to better understand what data science is and how to work with data scientists.

Data Science Principles is part of our Harvard on Digital Learning Path.

The Harvard on Digital course series provides the frameworks and methodologies to turn data into insight, technologies into strategy, and opportunities into value and responsibility to lead with data-driven decision making.

Explore More Courses in this Learning Path

The course is part of the Harvard on Digital Learning Path and will be delivered via  HBS Online’s course platform .  Learners will be immersed in real-world examples from experts at industry-leading organizations.  By the end of the course, participants will be able to:

• Understand the modern data science landscape and technical terminology for a data-driven world
• Recognize major concepts and tools in the field of data science and determine where they can be appropriately applied
• Appreciate the importance of curating, organizing, and wrangling data
• Explain uncertainty, causality, and data quality—and the ways they relate to each other
• Predict the consequences of data use and misuse and know when more data may be needed or when to change approaches

Your Instructor

Dustin Tingley  is a data scientist at Harvard University. He is Professor of Government and Deputy Vice Provost for Advances in Learning and helps to direct Harvard's education focused data science and technology team. Professor Tingley has helped a variety of organizations use the tools of data science and he has helped to develop machine learning algorithms and accompanying software for the social sciences. He has written on a variety of topics using data science techniques, including education, politics, and economics.

Real World Case Studies

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Mauricio Santillana

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Learn how Burning Glass Technologies uses text analysis to recommend job openings, skill development, and labor market trends.

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Learners who have enrolled in at least one qualifying Harvard Online program hosted on the HBS Online platform are eligible to receive a 30% discount on this course, regardless of completion or certificate status in the first purchased program. Past Participant Discounts are automatically applied to the Program Fee upon time of payment.  Learn more here .

Learners who have earned a verified certificate for a HarvardX course hosted on the  edX platform  are eligible to receive a 30% discount on this course using a discount code. Discounts are not available after you've submitted payment, so if you think you are eligible for a discount on a registration, please check your email for a code or contact us .

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For this course we offer a 30% discount for learners who work in the nonprofit, government, military, or education fields. 

Eligibility is determined by a prospective learner’s email address, ending in .org, .gov, .mil, or .edu. Interested learners can apply below for the discount and, if eligible, will receive a promo code to enter when completing payment information to enroll in a Harvard Online program. Click here to apply for these discounts.

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Learn more and enroll your team ! 

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Students and Recent Graduates

Prepare for your career by building a foundation of the essential concepts, vocabulary, skills, and intuition necessary for business.

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Recognize how data is changing industries and think critically about how to develop a data-driven mindset to prepare you for your next opportunity.

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Learn how data science techniques can be essential to your industry and how to contribute to cross-functional, data-oriented discussions.

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"This is a topic that people in any industry should have at least basic knowledge of in order to create more efficient and competitive businesses, tools, and resources."

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"I found value in the real-world examples in Data Science Principles. With complicated topics and new terms, it's especially beneficial for learnings to be able to tie back new or abstract concepts to ideas that we understand. This course helped me understand data in this context and what algorithms are actually trying to solve."

Alejandro D. Financial Services Analyst

"Data Science Principles applies to many aspects of our daily lives. The course helps guide people in everyday life through decision making and process thinking."

Jared D. Senior Director of Sales

"The way this complicated topic was presented and the reflection it caused was impressive. I enjoyed the way I could dive into a whole new world of expertise in such an engaging way with all these various tools such as videos, peer discussions, polls, and quizzes."

Sonja Schwetje Managing Director/Editor-in-Chief, ntv

Data Science Principles makes the fundamental topics in data science approachable and relevant by using real-world examples and prompts learners to think critically about applying these new understandings to their own workplace. Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics.

Download Full Syllabus

• Study a flu detection case study alongside Professor Dustin Tingley and Mauricio Santillana , Assistant Professor at Harvard’s T.H. Chan School of Public Health.
• Explain why data collection is important.
• Identify factors that may affect data quality.
• Recognize that not all data is numerical.
• Explain how the organization of data can affect the information you are able to extract from it.
• Study a predicting sepsis case alongside Craig Umscheid , Vice President and Chief Quality and Innovation Office, University of Chicago Medicine.
• Understand the basic structure of a predictive algorithm.
• Identify where human decisions shape predictive systems.
• Evaluate the success of a predictive system.  
• Study The Google Tax Case. 
• Explain why it is important to establish causal relationships.
• Identify barriers to establishing causal relationships in a variety of settings.
• Identify why randomization can help establish a causal relationship but also create other problems.  
• Explore a privacy and facial recognition case study with Latanya Sweeney , Professor of the Practice of Government and Technology at the Harvard Kennedy School and Sciences, director and founder of the Public Interest Tech Lab , and director and founder of the Data Privacy Lab .
• Explain why data privacy is important.
• Describe what can constitute a violation of privacy.
• Critique existing privacy policies.
• Create a set of ethical tenets to guide data work at their own organizations.  
• Study the Burning Glass and Text Data case.
• Identify sources of non-numerical data.
• Explain why it would be useful to use non-numerical data.
• Describe the differences in approach for supervised and unsupervised learning.
• Identify use cases for neural networks.  
• Explore a case study on reducing food waste with Shelf Engine.
• Describe some algorithms commonly used in data science.
• Understand basic workhorse algorithms in data science such as regression.
• Explain why and how such tools are made substantially more complex.
• Explain the crucial role humans have in overseeing and maintaining algorithms.
• Explain some of the trade-offs between more sophisticated algorithms, including the costs of running and evaluating their success.
• Learn about the Harvard Link case study.
• Explain the importance of data transformation and wrangling.
• List the common technologies used within data science ecosystems.
• Describe the connection between data science tasks, software tools, and hardware tools.
• Identify potential sources of bottlenecks in the data science process.  
• Work on a health care prioritization case study.
• Recognize a problem that an algorithm might be able to solve.
• Recognize the challenges created by using data science tools in ways outside their intended use.
• Identify steps within the data science process that need auditing.  

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Data Science for Business

Designed for managers, this course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.

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Top 65+ Data Science Projects in 2024 [with Source Code]

Data Science Projects involve using data to solve real-world problems and find new solutions. They are great for beginners who want to add work to their resume , especially if you’re a final-year student . Data Science is a hot career in 2024, and by building data science projects you can start to gain industry insights.

Think about predicting movie ratings or analyzing trends in social media posts. For example, you could guess how people will rate movies or see what’s popular on social media. Data Science Projects are a great way to learn and show your skills, setting you up for success in the future.

Explore cutting-edge data science projects with complete source code for 2024. These top Data Science Projects cover a range of applications, from machine learning and predictive analytics to natural language processing and computer vision . Dive into real-world examples to enhance your skills and understanding of data science.

Table of Content

What is Data Science?

Why build data science projects, best data science projects with source code, top data science projects – faqs.

Data Science is all about making sense of big piles of data . It’s like finding patterns and predicting future outcomes based on data. Data scientists use special tools and tricks to turn huge data into helpful information that can solve problems or make predictions.

Data Science is like being a detective for numbers. It’s about digging into huge piles of data to find hidden treasures of insights. Just like Sherlock Holmes uses clues to solve mysteries, data scientists use algorithms and techniques to uncover valuable information that helps businesses make better decisions.

Data Science Projects are important because they help us make better decisions using data . Whether it’s predicting trends in finance , understanding customer behavior in marketing, or diagnosing diseases in healthcare, data science projects enable us to uncover insights that lead to smarter choices and more efficient processes.

Data Science projects are like powerful tools that help us understand the world around us. They let us see patterns in data that we wouldn’t notice otherwise. By using these patterns, we can make smarter decisions in everything from business to healthcare, making our lives better and more efficient.

Let us look at some fun and exciting data science projects with source codes, that you can build.

Here are the best Data Science Projects with source code for beginners and experts to give a great learning experience. These projects help you understand the applications of data science by providing real world problems and solutions.

These projects use various technologies like Pandas , Matplotlib , Scikit-learn , TensorFlow , and many more. Deep learning projects commonly use TensorFlow and PyTorch, while NLP projects leverage NLTK, SpaCy, and TensorFlow.

We have categorized these projects into 6 categories. This will help you understand data science and it’s uses in different field. You can specialize in a particular field or build a diverse portfolio for job hunting.

Top Data Science Project Categories

Web scraping projects.

• Data Analysis and Visualization Projects

Machine Learning Projects

• Time Series Forecasting Projects

Deep Learning Projects

Opencv projects, nlp projects.

Explore the fascinating world of web scraping by building these data science projects with these exciting examples.

• Quote Scraping
• Wikipedia Text Scraping and cleaning
• Movies Review Scraping And Analysis
• Product Price Scraping and Analysis
• News Scraping and Analysis
• Real Estate Property Scraping and visualization
• Geeksforgeeks Job Portal Web Scraping for Job Search
• YouTube Channel Videos Web Scrapping
• Real-time Share Price scrapping and analysis

Data Analysis & Visualizations

Go through on a data-driven journey with these captivating exploratory data analysis and visualization projects.

• Zomato Data Analysis Using Python
• IPL Data Analysis
• Airbnb Data Analysis
• Global Covid-19 Data Analysis and Visualizations
• Housing Price Analysis & Predictions
• Market Basket Analysis
• Titanic Dataset Analysis and Survival Predictions
• Iris Flower Dataset Analysis and Predictions
• Customer Churn Analysis
• Car Price Prediction Analysis
• Indian Election Data Analysis
• HR Analytics to Track Employee Performance
• Product Recommendation Analysis
• Credit Card Approvals Analysis & Predictions
• Uber Trips Data Analysis
• iPhone Sales Analysis
• Google Search Analysis
• World Happiness Report Analysis & Visualization
• Apple Smart Watch Data Analysis
• Analyze International Debt Statistics

Dive into the world of machine learning with these real world data science practical projects.

• Wine Quality Prediction
• Credit Card Fraud Detection
• Disease Prediction Using Machine Learning
• Loan Approval Prediction using Machine Learning
• Loan Eligibility prediction using Machine Learning Models in Python
• Recommendation System in Python
• ML | Heart Disease Prediction Using Logistic Regression
• House Price Prediction using Machine Learning in Python
• ML | Boston Housing Kaggle Challenge with Linear Regression
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
• ML | Cancer cell classification using Scikit-learn
• Stock Price Prediction using Machine Learning in Python
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross-Validation
• Box Office Revenue Prediction Using Linear Regression in ML
• Online Payment Fraud Detection using Machine Learning in Python
• Customer Segmentation using Unsupervised Machine Learning in Python
• Bitcoin Price Prediction using Machine Learning in Python
• Recognizing HandWritten Digits in Scikit Learn
• Zillow Home Value (Zestimate) Prediction in ML
• Calories Burnt Prediction using Machine Learning

Time Series & Forecasting

Data Sceince Projects on time series and forecasting-

• Time Series Analysis with Stock Price Data
• Weather Data Analysis
• Time Series Analysis with Cryptocurrency Data
• Climate Change Data Analysis
• Anomaly Detection in Time Series Data
• Sales Forecast Prediction – Python
• Predictive Modeling for Sales or Demand Forecasting
• Air Quality Data Analysis and Dynamic Visualizations
• Gold Price Analysis and Forcasting Over Time
• Food Price Forecasting
• Time wise Unemployement Data Analysis
• Dogecoin Price Prediction with Machine Learning

Dive into these Data Science projects on Deep Learning to see how smart computers can get!

• Prediction of Wine type using Deep Learning
• IPL Score Prediction Using Deep Learning
• Handwritten Digit Recognition using Neural Network
• Predict Fuel Efficiency Using Tensorflow in Python
• Identifying handwritten digits using Logistic Regression in PyTorch

Explore fascinating Data Science projects with OpenCV, a cool tool for playing with images and videos. You can do fun tasks like recognizing faces , tracking objects , and even creating your own Snapchat-like filters . Let’s unleash the power of computer vision together!

• OCR of Handwritten digits | OpenCV
• Cartooning an Image using OpenCV – Python
• Count number of Object using Python-OpenCV
• Count number of Faces using Python – OpenCV
• Text Detection and Extraction using OpenCV and OCR

Discover the magic of NLP (Natural Language Processing) projects , where computers learn to understand human language. Dive into exciting tasks like sentiment analysis, chatbots, and language translation. Join the adventure of teaching computers to speak our language through these exciting projects.

• Detecting Spam Emails Using Tensorflow in Python
• SMS Spam Detection using TensorFlow in Python
• Flipkart Reviews Sentiment Analysis using Python
• Fake News Detection using Machine Learning
• Fake News Detection Model using TensorFlow in Python
• Twitter Sentiment Analysis using Python
• Facebook Sentiment Analysis using python
• Hate Speech Detection using Deep Learning

In this journey through data science projects, we’ve explored a vast array of fascinating topics and applications. From uncovering insights in web scraping and exploratory data analysis to solving real-world problems with machine learning, deep learning, OpenCV, and NLP, we’ve witnessed the power of data-driven insights.

Whether it’s predicting wine quality or detecting fraud, analyzing sentiments or forecasting sales, each project showcases how data science transforms raw data into actionable knowledge. With these projects, we’ve unlocked the potential of technology to make smarter decisions, improve processes, and enrich our understanding of the world around us.

What projects can be done in data science?

Data science projects can include web scraping, exploratory data analysis, machine learning, deep learning, computer vision, natural language processing, and more.

Which project is good for data science?

One of the most basic yet popular data science project is customer segmentation . Product based or service based, all companies need to work such that they can capture maximum users. This makes customer segmentation an important project.

How do I choose a data science project?

Choose a data science project based on your interests, available data, relevance to your goals, and potential impact on solving real-world problems.

What are the 10 main components of a data science project?

The 10 main components of a data science project include problem definition, data collection, data cleaning, exploratory data analysis, feature engineering, model selection, model training, model evaluation, results interpretation, and communication.

Are ML projects good for resume?

ML projects are excellent additions to a resume, showcasing practical skills, problem-solving abilities, and the ability to derive insights from data.

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19 Data Science Project Ideas for Beginners

This article will offer 19 data science project ideas for beginners. Pick one or all of them - whatever looks like the most fun to you.

Data science projects are a great way for beginners to get to grips with some of the very basic data science skills and languages that you'll need to pursue data science as a hobby or a career. Tutorials, lessons, and videos are all great, but projects really act as a stepping stone to getting involved with data science and getting your hands dirty.

Data science projects for beginners are better for learning languages and skills because they're stickier. I can watch a video about learning Python 10,000 times, but I only really start to understand Python when I take a project and do it myself. Data science projects are great because you’ve got much more personal vested interest than just watching an online tutorial. You’re motivated to see something through when you have a stake in the matter.

A good project can be anything from learning how to import a dataset all the way to creating your own website or something even more complex. Projects can be personal, just help you learn; they can serve as a portfolio to prove you know what you're talking about.

This article will offer 19 data science project ideas for beginners. Pick one or all of them - whatever looks like the most fun to you. Let’s jump in.

7 Data Science Project Tutorials for Beginners

These seven data science projects are a mix of videos and articles. They cover various different languages depending on what you're interested in learning. You'll learn how to use APIs, how to run predictions, touch on deep learning, and look at regression.

These seven project tutorials for beginners are hands-on and specific, so they’re perfect if you want to get started but you don't really know where. Pick one you like, see where you’re struggling, and use that to start building a list of other data science skills you can learn.

Project 1: House Prices Regression

During the pandemic, I found myself spending a lot of time on Zillow. I loved looking at all the different houses because they were so rich in data. There are so many different aspects for me to investigate and lose myself in. That strange interest led me to this tutorial which allows you to predict the final price of homes in Ames, Iowa.

Sounds weird, but it's fun.

You can use either R or Python to run through this project. Honestly, it's an ambitious project especially if you're brand-new to coding. But I'm starting with it because I think it speaks to a question a lot of people have – how much are houses worth? Humans are fundamentally curious, and the best data science projects exploit that curiosity to teach you skills.

What I love about this tutorial on Kaggle is that it has a ton of different options to complete it, and these different solutions are shared with the community. Anybody can upload their own code to this, so it's a really good place to learn and copy from other people (which is really one of the best ways of learning how to code).

Get stuck in with predictions, a bit of machine learning, and some regression.

Project 2: Titanic Classification

One of the world’s best-known tragedies is the sinking of the Titanic. There weren't enough lifeboats for everyone on board causing the death of over 1,500 people. If you look at the data though, it seems that some groups of people were more likely to survive than others.

The same website as in the project above, Kaggle, runs this competition. They tried to figure out what factors were most likely to lead to success - socio-economic status, age, gender, and more. Similar to the house prices project, this project has access to the code of many other programmers that you can learn from. They also have their very own tutorial they offer for total beginners. This is really useful for people who are new to Kaggle as well as coding.

At the end, you'll have built a predictive model that answers that question. I recommend Python for this one.

Whether or not you actually join the competition, this is still one of the great data science projects for beginners to investigate.

Project 3: Deep Learning Number Recognition

Did you know computers can see? A lot of the latest interesting data science projects have to do with computer vision. This tutorial is great to teach you the basics of neural networks and classification methods. During the tutorial, your job is to correctly identify digits from a data set of tens of thousands of handwritten images.

This competition/tutorial is also hosted by Kaggle - you can check out some of their own tutorials, or you can just get stuck in with user-submitted code .

In my opinion, this project isn't as interesting as the Titanic or the house prices tutorial, but it'll teach you some of the basics of a very complex subject. Plus, it’s pretty wild that you can teach a computer to see.

Project 4: YouTube comment sentiment analysis

Don't read the comments! ...Unless you're doing a YouTube comment sentiment analysis data science project for beginners.

This tutorial of YouTube comment sentiment analysis is great because it's truly for beginners. The creator of the video tutorial is a beginner at natural language processing, which is the skill you'll be learning in this tutorial. It's a really cool video that's about 14 minutes long, perfect for getting started with NLP. It’s also a great representation that shows how data science projects can run away with you, in a good way.

The video is really funny, and she links to the code in her GitHub . Feel free to get into it yourself!

Project 5: Covid-19 Data Analysis Project

During the pandemic, it felt like things were out of my control. It sounds silly, but one of the ways I grounded myself was just by keeping track of daily numbers. Sometimes it stressed me out, but I found myself looking to data as a way to understand the unimaginable.

The Python Programmer channel had a similar idea. In this tutorial, he teaches you to do Covid-19 data analysis using Python.

This video tutorial is a bit more serious than the previous one, and it goes a little bit more in-depth about how it's done. He also covers the basics of some pretty key Python packages like pandas. It’s a really clear introduction to pandas and Python.

Project 6: Scrape IG comments

There’s so much information on the internet. Most of the tutorials above give you datasets to play with, but sometimes it’s useful to know how to find and use your own data. That’s where knowing how to scrape comes in handy. Plus, maybe you don’t particularly care about YouTube comments or Covid-19 data, but Instagram is really your jam.

The official Instagram API allows you to programmatically access your own comments. But it doesn't like you do that for other people. If you're like me, and you wanted to have a look at posts made by the people get a list of posts with a particular #or scrape the comments of other people's posts. You need something else - a scraper.

This article isn't really a tutorial so much as instructions for your own project, but I love Apify as an Instagram scraping tool. With this, you can grab the data and investigate your own questions. Do certain hashtags get more likes? Do captions elicit more comments? The sky's the limit.

Project 7: YouTube APIs with Python

Speaking of APIs, working with APIs is a necessary skill set for all of the other scientists. When you're choosing a project, make sure at least one of them teaches you to work with APIs to ensure you've covered this critical skill.

This tutorial uses Python to walk you through making an API call to collect video statistics from a channel, and saving it as a pandas dataframe. It also offers you the Python notebook code and additional resources on GitHub.

5 DIY Data Science Project Ideas for Beginners [Unlimited Data Science Project Ideas]

There are practically millions of potential data science projects out there that I've been documented in tutorial and video form. But it's also useful to know how to create your own project. Every other project tutorial out there we'll talk about what other people want to do with - think about what you want to do.

Coming up with my own project was how I ended up getting into Python in the first place. I had a question, I needed an answer, the only way to get it was by analyzing my data with python. Rather than list more individual tutorials, I want to point you to some resources that can help you design your own data science projects from scratch.

Project 8: Tidy Tuesdays

This project relies on the Tidy Tuesday GitHub repo . The great thing about this repo is that every Tuesday, brand-new untidy data is uploaded. The cohort analyzes it, visualizes it, and generally plays around with it. It's a great place to learn from other people and experiment with it yourself.

This repo is best for people who want to learn R (though also good for some Python). It’s also best for basic data science skills, like reading files, doing introductory analysis, visualization, and reporting.

For example, this week’s Tidy Tuesday dataset was from the National Bureau of Economic Research. The way the dataset was structured meant that it was good to learn how to join tables. Maybe you’re interested in checking out the female representation of paper authors. Maybe you want to know about publishing frequency in summer versus winter. Either way, TidyTuesday can help you with some basic data science skills with new data every week. It goes back years, too, so you’ll be able to find something interesting no matter what kind of data you like, and you’ll never run out of data science project ideas.

Project 9: The Pudding

The Pudding does really jazzy visualizations and analysis, usually using JavaScript, Python, or R. TidyTuesday is great for sheer volume, but The Pudding offers some truly weird projects to take on.

Maybe you also are a huge Community fan like me, and you want to know how many times Abed says the word “Cool,” versus Jeff or Annie. Perhaps you love reading Agony Aunt letters and this insight into thirty years of American anxieties via Dear Abby letters intrigues you.

These projects offer a lot of cultural commentaries. They’re more challenging and niche than some others on this list, but they’re gripping and can teach you a lot about visualizations especially. The Pudding offers all their code on their GitHub repo which I encourage you to check out.

Project 10: 538

Sports and politics collide in the 538 blog, meeting in a glorious burst of statistics and math. Here, you can scroll through the articles, spot whatever interests you, and head to the GitHub repo to see the code and analysis behind the findings. From there, you can dive into the data yourself.

One project I had a fun time digging into was Superbowl ads. The original article talked about how Americans love America, animals, and sex (as represented by their frequency in Superbowl ads). I was interested to know whether there were more sexual ads over the years. Find your own question and dive in!

Project 11: NASA

Who didn’t want to be an astronaut when they grew up? Now is (kind of) your opportunity to chase that dream.

NASA’s data isn’t as user-friendly as the three options I listed above. But the quantity (and general awesomeness) of the data on offer here makes it a must for any data science project list. Instead of trying to trawl through their dense literature and databases, I recommend you start with this “ Space Science with Python '' tutorial series. For example, want to know how close the asteroid 1997BQ passed by Earth in May 2020? Now’s your chance to find out.

Project 12: The Tate museum

The Tate museum ( http://shardcore.org/tatedata/ )

Maybe you’re more of an Arts & Humanities buff. Luckily for you, there’s data available for you to create your own data science project too. Look no further than the Tate museum’s data archive . Here, you can find the metadata for over 3,500 artists.

There’s a lot you can do for yourself with that data, but in case you’re already lost wondering where to begin, the Tate helpfully lists example data science projects others have done with access to this data.

7 Skills-Based Data Science Projects

The first section of this blog post dealt with pretty specific tutorials. The second taught you where to look to create your own data science project ideas. This final one will point you in the right direction for skills-based data science project ideas. This is the most relevant for those who are putting together a resume or thinking about applying for a data science job .

Each of these seven steps is worth being its own data science project for beginners, but once you’re ready, you can also use these seven to create a full project for more intermediate/advanced data scientists.

Project 13: Collect data

The very first step in any data science project is worth being a data science project itself: collect data.

Most times, data does not arrive perfectly formed into neat tables in your computer. You have to figure out how to get it from point A to point B in order to do everything else you want.

Turn it into a project and investigate how to collect data using some of the most popular data science languages , like Python and SQL. Here’s a great article tutorial for scraping data using Python.

Project 14: Clean data

The data is in! But it’s messy. Learning how to clean data was one of the biggest letdowns in my Master’s when I was studying bird conservation. I thought I’d be able to get data in and start analyzing right away. Sadly, there were issues: duplicates, N/As, numbers stored as text, and just about every other issue you can think of.

Some folks say cleaning data is 80% of a data scientist’s job. It’s worth knowing how to do.

I did my project using R, so if that’s you, I recommend this tutorial to learn how to load and clean data using R. If you’re a budding Pythonista, this tutorial helped me get to grips with cleaning data with Pandas and NumPy, both very common and useful Python packages.

Project 15: Explore data

Once your data is in and relatively tidy, it’s time for the exciting part: explore your data. This isn’t quite to the level of visualizing or analyzing it. Usually, there’s so much data you’re looking at that it helps to get a feel for what’s actually going on before you begin creating models. Think of this project like dipping your toe in the water to gauge the temperature.

This 2.5 hour video tutorial will teach you to build an exploratory data analysis project completely from scratch. It’s lengthy, and 100% comprehensive.

Project 16: Visualize data

There’s a lot you can do to visualize data, and a lot of data science skill is knowing which kind of visualization best represents the idea you’re trying to communicate. That’s why simply working on data viz is a great data science project idea for beginners.

This Kaggle tutorial is a bit boring but will teach you some of the basics of data visualization. With that knowledge, you can go on and create your own data science visualization project - this time using data that you care about.

Project 17: Regression

Regression is a super important predicting tool used in all avenues of data science. It’s what helps you statistically determine the relationship between X and Y. It’s the very basics of what will become machine learning.

You can create a project that focuses on regression with any dataset that has an X and Y variable. I did this myself with my bird data, predicting whether the size of the bird influenced the survival of the bird. Pick any dataset you like and use a method like Kaggle’s red wine quality data tutorial, linked here .

Project 18: Statistics in general

It’s easy to get caught up in the hype of NLP, ML, AI, DL, and every other data science acronym. But don’t forget data science of all kinds relies on statistics and math. To get the most out of any data science project idea you may have, ensure you have grasped the fundamentals of statistics underpinning the concepts of data science.

I’m cheating a little bit by grouping all these statistical fundamentals under a single subheader, but I recommend KDNuggets’s list of eight basic statistics concepts . From there, find a project that focuses on each of the eight. For instance, take the Tate dataset I linked above and learn about the “central tendency” by figuring out the median paint date of the artwork.

You can use any programming language for this project. I like Python since it’s great for beginners anyway, but R, SQL, JavaScript, or any of the other coding languages can accomplish the same goal.

Project 19: Machine learning

Let’s wrap up this list of data science project ideas for beginners with this one: machine learning. Any data scientist worth their salt knows about machine learning and can successfully use it to predict any number of things. Use what you learned from regression and apply it here.

To create a project that will teach you machine learning, nearly any dataset will do. For example, you can use Uber’s pickup data and ask questions like: does Uber make congestion worse? Alternatively, this tutorial which guides you through making movie recommendations could be a good project to tackle. I recommend using Python due to its TensorFlow package which is built for machine learning specifically.

Bonus Projects

There’s always more, and we could be suggesting projects indefinitely. You can see a lot more data projects on our platform for beginners and more advanced users.

We selected two projects that seem ideal for some more data exploration and regression practice.

For regression, there’s the Website Traffic Analysis data project by Linkfire . It makes you use Python (pandas and SciPy, to be specific) to analyze the website traffic, especially the volume, and distribution of events. From the analysis, you should be able to develop ideas for increasing the links’ click rates.

A relatively simple Predicting Price data project by Haensel AMS is perfect for practicing regression. You’re given seven attributes, and your task is to build the price prediction ML model. You’ll have to analyze data, then define, train, and evaluate the model.

Data science project ideas for beginners are unlimited

If you have an ounce of creativity and curiosity, you can trawl the web to find the data and tutorials you need to create your very own data science projects, no matter what your interest or skill levels. This article should serve as a signpost pointing you to potential options which you can peruse at your leisure.

If you need only one project that’ll help you gain full-stack data science experience, check out our previous article Data Analytics Project Ideas That Will Get You The Job .

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Programming for Data Science

Teaching data scientists the tools they need to use computers to do data science

Assignments

Programming with python assignments.

• Assignment 1

Advanced Python Assignments

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• Assignment 4
• Assignment 5
• Assignment 6
• Assignment 7
• Assignment 8
• Assignment 9
• Assignment 10
• Assignment 11
• Assignment 12
• Assignment 13

Assignments

All assignments for the class will be listed here.

• There will be approximately 4-5 homework assignments, each with a number of programming problems.
• There may be additional sets of practice problems assigned to help reinforce various topics.
• A midterm “tutorial” assignment where you will write up a short tutorial on a data science subject.
• A final project, done in groups, on a data science problem of your choosing.

All assignments will be due at 11:59 pm ET (midnight) on the due date.

You are expected to know and adhere to the course policies , which govern late days, submissions, and collaboration.

Assignment dates

Due dates are tentative for any assignments that haven’t been released yet.

Homeworks are distributed Jupyter notebooks (we will also link Colab notebooks shortly), and are submitted for grading using code in the notebook as well (we will post a description of this proceess along with the first homework). To submit the assignments, sign up for an account (with your andrew email) on the autograding site https://mugrade.datasciencecourse.org

In lieu of a midterm exam, students will write a tutorial on a data science topic of their choosing. More information may be found here .

Again, no late days are permitted on the tutorial, and failure to submit by the deadline will result in zero points for the proposal component.

Final project

The final project of the course will consist of a large data science project done in teams of 2-3 people (single person or four person teams will be considered on an individual basis). The final report for this project will be a Jupyter notebook detailing the data collection, analysis, and results. In addition to the report, teams will also prepare a short video for showing during a final project video session.

Master of Science in Data Science

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Students take core courses in data mining and discovery, data analysis and visualization, and data ethics while choosing from a number of dynamic electives , including neural networks, artificial intelligence, natural language processing, machine learning, cyberinfrastructure, data warehousing, database development, data science and public interests, and advanced computational linguistics. With the MSDS, you'll graduate with the skills you need to excel in tomorrow's dynamic, data-driven economy .

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How Science Went to the Dogs (and Cats)

Pets were once dismissed as trivial scientific subjects. Today, companion animal science is hot.

Max, una mezcla de pastor alemán, malinois belga y husky de 2 años, fue fotografiado este mes en el parque Greenlake de Seattle. Max, un perro callejero que fue rescatado en un estado demacrado, participa en el Arca de Darwin, una iniciativa científica comunitaria que investiga la genética y el comportamiento de los animales. Credit... M. Scott Brauer para The New York Times

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By Emily Anthes

Emily Anthes, who has both a dog and a cat, has been writing about canine genetics since 2004.

• June 30, 2024

This article is part of our Pets special section on scientists’ growing interest in our animal companions.

Every dog has its day, and July 14, 2004, belonged to a boxer named Tasha. On that date, the National Institutes of Health announced that the barrel-chested, generously jowled canine had become the first dog to have her complete genome sequenced. “And everything has kind of exploded since then,” said Elaine Ostrander, a canine genomics expert at the National Human Genome Research Institute, who was part of the research team.

In the 20 years since, geneticists have fallen hard for our canine companions, sequencing thousands upon thousands of dogs, including pedigreed purebreds, mysterious mutts, highly trained working dogs, free-ranging village dogs and even ancient canine remains.

Research on canine cognition and behavior has taken off, too. “Now dog posters are taking up half of an animal behavior conference,” said Monique Udell, who directs the human-animal interaction lab at Oregon State University. “And we’re starting to see cat research following that same trend.”

Just a few decades ago, many researchers considered pets to be deeply unserious subjects. (“I didn’t want to study dogs,” said Alexandra Horowitz, who has since become a prominent researcher in the field of canine cognition.) Today, companion animals are absolutely in vogue. Scientists around the world are peering deep into the bodies and minds of cats and dogs, hoping to learn more about how they wriggled their way into our lives, how they experience the world and how to keep them living in it longer. It’s a shift that some experts say is long overdue.

“We have a responsibility to deeply understand these animals if we’re going to live with them,” Dr. Udell said. “We also have this great potential to learn a lot about them and a lot about ourselves in the process.”

Pet projects

For geneticists, dogs and cats are both rich subjects , given their long, close history with humans and their susceptibility to many of the same diseases, from cancer to diabetes.

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ABOVE: Data from the arcade game in Borderlands 3 helped scientists to map the relationships of different species in the human gut microbiome. The Gearbox Entertainment Company

B y April 2020, more than two billion people were under some form of lockdown to prevent the spread of SARS-CoV-2. 1 As restaurants, sports stadiums, and concert venues closed, people increasingly turned to home-based activities: tending to sourdough starters, cultivating houseplants, and playing video games— lots and lots of video games. 2

That same month, a new feature appeared in the massively popular game Borderlands 3 : an arcade booth tucked neatly into a corner of Sanctuary III, the spaceship that players use to traverse the galaxy. At the arcade game-within-a-game, players solve simple puzzles by aligning sequences of colored tiles, a setup somewhat reminiscent of Candy Crush . But the minigame’s apparent simplicity is deceptive; it is the result of years of collaboration between game designers and scientists and has become one of the most successful citizen scientist projects ever created.

In a paper published in Nature Biotechnology , the team analyzed data collected from millions of players that engaged with the Borderlands Science project. 3 Their efforts helped scientists assess evolutionary relationships between bacterial species in an enormous dataset of human gut microbes, informing future explorations into the roles of these microbes in health and disease.

“I think it's a brilliant approach,” said Amy Sterling , who was not involved in the Borderlands Science project. Sterling is a veteran in the crowd-sourced science space; she has served as the executive director of Eyewire , a long-running citizen science game for mapping neural circuits, since 2012. 4 “We know that games have myriad impacts on humans, some good and some bad, but putting citizen science in a game like Borderlands —there’s arguably no downside to that, it's a win for everyone.”

Like many scientific endeavours, this one began with a problem, said Jérôme Waldispühl , a computational biologist at McGill University and coauthor of the study. “There is a fundamental problem that we have in biology, which is the problem of multiple sequence alignment (MSA). Basically, this is the process of comparing DNA sequences from different organisms to better understand the phylogeny and how they relate to each other.”

While MSA computer programs have improved over the past few decades, they still require human supervision, said Waldispühl. As organisms diverge, their DNA changes in different and unpredictable ways. Nucleotides can be inserted, deleted, or swapped for a different type of nucleotide and there aren’t necessarily hard-and-fast rules that computers can follow to figure out how to match up the mutated sequences. However, the unexplainable black box of the human mind is often able to intuit the optimal alignment and correct the computer’s mistakes. 

“The truth is that there's no real rule for doing this,” said Waldispühl. “Often, it's about looking at the pattern, the context in which a mutation or an insertion or deletion occurs. It’s relying on the aesthetic of the alignment.” While human curation of these datasets is important, it’s also incredibly time consuming, causing a major bottleneck in the research pipeline. 

“We needed to figure out how to integrate the human mind into the computing process at scale,” said Waldispühl.

In 2010, Waldispühl and his research team launched Phylo, a crowd-sourced, lightly gamified online program that allowed anyone to contribute to MSA curation. Phylo achieved modest success, gaining nearly 36,000 registered users in its first five years. 5

“Very soon, however, it became apparent for us that even if we were one of the most successful citizen science projects in the world, the engagement and the volume of participation were not nearly enough to solve the type of problems that we are interested in,” he said. 

Waldispühl was not the only person considering the issue of engagement in citizen science. In 2014, computer scientist and study coauthor Attila Szantner cofounded the company Massively Multiplayer Online Science with the aim of bringing crowd-sourced science into video games. Two years later, Waldispühl and Szantner teamed up and, along with Gearbox Entertainment, the company behind Borderlands , began the lengthy process of designing the Borderlands Science project.  

The team knew that they wanted to address the MSA problem, but they still needed to find a sufficiently large data set to analyze. Fortunately, the researchers behind the University of California, San Diego’s Microsetta Initiative , a microbiome sequencing study performed using “donations” from thousands of volunteers, were willing to share their data. The resulting dataset contains nearly one million 16S ribosomal RNA sequences, which serve as rough taxonomic markers for different species of bacteria. 

Daniel McDonald , the initiative’s scientific director and coauthor of the study, said that construction of the phylogenies of these diverse populations is more than just an idle curiosity. “Once we can estimate a phylogeny, we can use this relationship information to help us understand how diverse a microbiome sample is—for example, is your microbiome more diverse than my microbiome? And we can look at this in the context of the evolution that's represented.”

Researchers can also gain valuable insights by performing these assessments across all the samples. “We’ve found that in the microbiome space, using phylogeny tends to be much more informative and powerful for teasing apart subtle differences in humans that we can ultimately relate back to things like health and lifestyle and diet,” said McDonald. 

Borderlands Science launched on April 7, 2020. The team had considered postponing given that the world’s attention seemed to be elsewhere as global COVID-19 cases hit one million and kept climbing. However, almost immediately following the launch, they realized that the project would be a massive success.  

“That single day when we launched this feature inside Borderlands 3 , we collected five times more data than during the 10 years that Jérôme was running [Phylo],” said Szantner. “This is the kind of crazy power and resources that [video game] players bring to scientific research.” 

The team walked a fine line between creating a game that was fun and engaging and ensuring that players’ contributions were still scientifically relevant. Ultimately, they succeeded on both counts. Players were highly engaged in the game—since the launch, more than four million people have participated in the initiative, solving more than 135 million RNA mini puzzles. 3 Their involvement also contributed to scientific understanding: curation of MSA algorithm output by Borderlands Science players improved phylogenetic tree structure compared to MSA algorithms used alone. 

“There are different reasons why we might want to involve a crowd in scientific research,” said Marion Poetz , who studies innovation in science at the Copenhagen Business School and who was not involved in the project. “One of them is to leverage volume to improve efficiency. In this case—it's impressive—millions of people have done something that researchers probably would need thousands of years to complete.” 

Sterling was similarly impressed by the scale of the project and hopes that other video game manufacturers will follow suit. “I could imagine a future where it's just a part of corporate social responsibility for these huge game studios to launch a new citizen science project every year as a way of proving to the world that games give back,” she said. 

However, Poetz said that citizen science projects can have other goals that don’t necessarily have anything to do with the data. These include improving science literacy, changing attitudes about science, and democratizing the scientific process. Embedding a citizen science project within a video game not only vastly expands the project’s reach—more than three billion people around the world play some form of video games—it also increases the diversity of the players. 6

“I think that is the special thing here,” Poetz said. “There are data showing that certain types of people contribute to crowd science projects; they’re more likely to be white, well-educated, and so forth. By embedding this particular project in a video game, it may reach people that usually would not go on Zooniverse or other crowd science platforms.”

Poetz noted that while this project had clearly succeeded in reaching a wide audience, many of whom may not have otherwise been scientifically inclined, she would have been interested to learn whether the team had specifically pursued any non-scientific goals, the approaches they had used in pursuit of these goals, and the metrics they used to determine the outcomes of these efforts.

Waldispühl said that while the non-scientific impacts of the project were not initially a major focus, he has come to appreciate that they are an important and valuable element. “Initially, I was approaching this as a computational problem; I wanted to make computers smarter than they are now,” he said. “But eventually, what I found fantastic was the feedback from players.”

“Many people divert away from science because of social pressure, or people telling them they’re not smart enough. With this game, we are noticing many people really engaging in the scientific process. And by doing this, they’ve realized that they can do things they never thought they could do and they’ve realized that science is fun,” Waldispühl continued.   

“We are trying to infuse society with this feeling that science is good,” he said. “Ultimately, we want to inspire people, to fight disinformation, and reinforce public trust in science, which I think is a real game changer going beyond the scientific results themselves.”

• Koh D. COVID-19 lockdowns throughout the world. Occup Med . 2020;70(5):322. 
• DiFrancisco-Donoghue J, et al. Gaming in pandemic times: An international survey assessing the effects of COVID-19 lockdowns on young video gamers’ health . Int J Environ Res Public Health . 2023;20(19):6855.
• Sarrazin-Gendron R, et al. Improving microbial phylogeny with citizen science within a mass-market video game. Nat Biotechnol . 38622344.
• Kim JS, et al. Space-time wiring specificity supports direction selectivity in the retina. Nature . 2014;509(7500):331-336.
• Singh A, et al. Lessons from an online massive genomics computer game. Proc AAAI HCOMP . 2017;5:177-186.
• Morikawa Y. The gaming industry sees a staggering surge in popularity . GlobalEDGE, Michigan State University. https://globaledge.msu.edu/blog/post/57295/the-gaming-industry-sees-a-staggering-surge-in-popularity

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LSU Cyber AI Team Supports $25 Million National Defense and Energy Project

July 08, 2024

The National Nuclear Security Administration, part of the U.S. Department of Energy, has awarded $50 million in cooperative agreements to only two university consortia to support nuclear security and nonproliferation. LSU researchers will work with colleagues at 15 universities and eight national labs to develop AI models to protect the nation from nuclear threats while training a new generation of data science, cyber and AI experts.

LSU’s James Ghawaly, principal investigator on the project and assistant professor of computer science and engineering, and Golden G. Richard III, professor of computer science and engineering and director of the LSU Cyber Center, both with joint appointments in the LSU Center for Computation & Technology, are part of a national research team led by the University of Tennessee, Knoxville, called the Enabling Capabilities in Technology Consortium. Leading the project’s data science component, Ghawaly and Richard will support U.S. nuclear security missions and educate highly talented cyber and data science professionals with AI skill sets who can pursue careers in the Department of Energy’s national labs.

“With the strong cyber focus we have here at LSU, we will be able to look at signals people haven’t been looking at that hard, like radiofrequency emissions and other digital signatures that can help fingerprint and track threats that could be transporting lost or smuggled nuclear or radiological material,” Ghawaly said. “This is in addition to the sensors most often used, such as radiation detectors, cameras and LiDAR.”

Ghawaly is developing foundational AI models to make sense of these multimodal data, to find patterns and create unique fingerprints.

“After you've detected a threat, you want to be able to track it in a very busy environment with a low signal-noise ratio,” Ghawaly said.

The broader research thrusts of the Enabling Capabilities in Technology Consortium include fundamental science in earth, environmental, atmospheric and space science, as well as radio and nuclear chemistry and applied science and engineering in areas of nuclear chemical engineering, advanced nuclear fuel systems engineering and reactor systems engineering.

Connecting these thrusts are three cross-cutting areas: detection, characterization and response methodologies and tools; data science for nuclear nonproliferation; and education and training.

“Advances in global security, clean energy and artificial intelligence are especially critical to our nation and our world at this time,” said Jason Hayward, professor of nuclear engineering at University of Tennessee, Knoxville and director of the consortium. “Our efforts will help produce the new knowledge and diverse talented workforce necessary to enable the U.S. and its allies to safely and securely triple nuclear power output throughout the world by 2050.”

Other schools in the consortium are Colorado School of Mines; Air Force Institute of Technology; Clemson University; University of California, Santa Barbara; University of Hawaii; the Massachusetts Institute of Technology; North Carolina State University; the University of Oklahoma; Oregon State University; Texas A&M University; the University of Texas at San Antonio; University of Utah, and Virginia Polytechnic Institute and State University.

The eight national laboratories involved are Idaho National Laboratory; Lawrence Berkeley National Laboratory; Lawrence Livermore National Laboratory; Los Alamos National Laboratory; Oak Ridge National Laboratory; Pacific Northwest National Laboratory; Sandia National Laboratories; and Savannah River National Laboratory.

https://www.energy.gov/nnsa/articles/nnsa-awards-50-million-cooperative-agreements-two-university-consortia-support

LSU Office of Research & Economic Development

Abbi Rocha Laymoun

LSU Media Relations

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All Data Science Assignments are Available in this file

akashkadam4/ExcelR-Data-Science-Assignments

Folders and files.

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Excelr-data-science-assignments.

This is All Data Science Assignments Files. You can see all the files are Available in this Repositories.

List of Following Files of all Data Science Assignments Topices:-

1.Assignment 1 (Basic Statistics_Level 1)

2.Assignment 2(Basic Statistics_Level-2)

3.Assignment 3(Hypothesis Testing)

4.Assignment 4(Simple Linear Regression)

5.Assignment 5(Multi Linear Regression)

6.Assignment 6(Logistic Regression)

7.Assignment 7(Clustering)

8.Assignment 8(PCA)

9.Assignment 9(Association Rules)

10.Assignment 10(Recommendation system)

11.Assignment 11(KNN)

12.Assignment 12(Decision tree)

13.Assignment 13(Random Forests)

14.Assignment 14(Support Vector Machines)

15.Assignment 15(Neural Networks)

16.Assignment 16(Text Mining)

17.Assignment 17(Naive Bayes)

18.Assignment 18(Forecasting)

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OC High Schoolers Reach for the Cloud(s)

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High school students from Orange County recently attended Google’s Women@IRV  event to help them learn about careers in STEM and hear advice from those who have gone before them.

The event was facilitated by UC Irvine’s Donald Bren School of Information and Computer Science (ICS). Vinh Luong,  Assistant Director for the ICS Office of Outreach, Access and Inclusion, reached out to this year’s Orange County Aspirations in Computing award winners to extend an invitation to the event.

“The students who attended this event were enthusiastic and engaged, having already demonstrated an interest in STEM,” he said. “They asked thoughtful questions and said they came away feeling inspired by the women they met.”

The event, which was hosted by Google at their Irvine office, kicked off with a short presentation by Amy Schendel, Senior Software Engineer, who shared her educational and career journey and provided valuable advice about making the most of all opportunities, even those that seem unrelated to your goals. She talked about the ubiquity of computers and the transferable skill set that a computing degree offers, making you equipped to work in almost any industry. She suggested joining national organizations such as the Association for Computing Machinery (ACM ), Computing Research Association (CRA) and the Institute of Electrical and Electronics Engineers (IEEE ).

After a short “networking bingo” exercise, there was a panel event featuring four women who shared their wealth of knowledge and experience on a range of topics, before responding to eager questions from the audience.

Panel members

Advice from the panel

• Aside from computing skills, it’s important to be creative and flexible, have good communication skills, and collaborate well with others.
• It’s also important to take feedback seriously but not personally, and to be confident and speak up when necessary, while learning from others.

“Impostor syndrome is real – remind yourself that even if you don’t see somebody who looks like you or dresses like you, you belong there.” – Alyssa

“Focus on enjoying the journey, not just the destination. Enjoy whatever stage of life you’re at, and balance that with working hard for the future.” – Shivana

“Find that project or that company or that team that makes you feel happy contributing to it – liking your job and feeling connected to the work gives you a sense of purpose. ” – Nandita

Student feedback

Jacquelyn Phan, a rising sophomore/junior/senior at Westminster High School, said, “Meeting and interacting with such accomplished professional women has not only broadened our understanding of the STEM field but also fueled our ambitions and aspirations. Everyone’s stories shared and the guidance provided has left a lasting impact on us, motivating us to pursue our goals with renewed enthusiasm and confidence.”

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Cyber@uci team places fourth in national cybersecurity competition (uci news), venushacks 2024, ics project expo-nential growth, commentary: california’s public universities come through – at least for one family (edsource), pride month 2024: supporting lgbtq+ in tech, best master’s in data science for 2024 (fortune).