data science assignment

12 Data Science Projects for Beginners and Experts

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

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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.

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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.

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

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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.

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• 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 combines math and statistics, specialized programming, advanced  analytics , artificial intelligence (AI)  and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by  Harvard Business Review  (link resides outside ibm.com). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:

• Data ingestion : The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT) , social media, and more.
• Data storage and data processing : Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using  ETL  (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a  data warehouse ,  data lake , or other repository.
• Data analysis : Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
• Communicate : Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

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Data science is considered a discipline, while data scientists are the practitioners within that field. Data scientists are not necessarily directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are typically handled by data engineers—but the data scientist may make recommendations about what sort of data is useful or required. While data scientists can build machine learning models, scaling these efforts at a larger level requires more software engineering skills to optimize a program to run more quickly. As a result, it’s common for a data scientist to partner with machine learning engineers to scale machine learning models.

Data scientist responsibilities can commonly overlap with a data analyst, particularly with exploratory data analysis and data visualization. However, a data scientist’s skillset is typically broader than the average data analyst. Comparatively speaking, data scientist leverage common programming languages, such as R and Python, to conduct more statistical inference and data visualization.

To perform these tasks, data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst. The data scientist must also understand the specifics of the business, such as automobile manufacturing, eCommerce, or healthcare.

In short, a data scientist must be able to:

• Know enough about the business to ask pertinent questions and identify business pain points.
• Apply statistics and computer science, along with business acumen, to data analysis.
• Use a wide range of tools and techniques for preparing and extracting data—everything from databases and SQL to data mining to data integration methods.
• Extract insights from big data using predictive analytics and  artificial intelligence  (AI), including  machine learning models ,  natural language processing , and  deep learning .
• Write programs that automate data processing and calculations.
• Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding.
• Explain how the results can be used to solve business problems.
• Collaborate with other data science team members, such as data and business analysts, IT architects, data engineers, and application developers.

These skills are in high demand, and as a result, many individuals that are breaking into a data science career, explore a variety of data science programs, such as certification programs, data science courses, and degree programs offered by educational institutions.

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It may be easy to confuse the terms “data science” and “business intelligence” (BI) because they both relate to an organization’s data and analysis of that data, but they do differ in focus.

Business intelligence (BI) is typically an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries. While data science tools overlap in much of this regard, business intelligence focuses more on data from the past, and the insights from BI tools are more descriptive in nature. It uses data to understand what happened before to inform a course of action. BI is geared toward static (unchanging) data that is usually structured. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used to categorize data or to make forecasts.

Data science and BI are not mutually exclusive—digitally savvy organizations use both to fully understand and extract value from their data.

Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities. These languages include the following (read more at " Python vs. R: What's the Difference? "):

• R Studio:  An open source programming language and environment for developing statistical computing and graphics.
• Python:  It is a dynamic and flexible programming language. The Python includes numerous libraries, such as NumPy, Pandas, Matplotlib, for analyzing data quickly.

To facilitate sharing code and other information, data scientists may use GitHub and Jupyter notebooks.

Some data scientists may prefer a user interface, and two common enterprise tools for statistical analysis include:

• SAS:  A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.
• IBM SPSS : Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.

Data scientists also gain proficiency in using big data processing platforms, such as Apache Spark, the open source framework Apache Hadoop, and NoSQL databases. They are also skilled with a wide range of data visualization tools, including simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel), built-for-purpose commercial visualization tools like Tableau and IBM Cognos, and open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs. For building machine learning models, data scientists frequently turn to several frameworks like PyTorch, TensorFlow, MXNet, and Spark MLib.

Given the steep learning curve in data science, many companies are seeking to accelerate their return on investment for AI projects; they often struggle to hire the talent needed to realize data science project’s full potential. To address this gap, they are turning to multipersona data science and machine learning (DSML) platforms, giving rise to the role of “citizen data scientist.”

Multipersona DSML platforms use automation, self-service portals, and low-code/no-code user interfaces so that people with little or no background in digital technology or expert data science can create business value using data science and machine learning. These platforms also support expert data scientists by also offering a more technical interface. Using a multipersona DSML platform encourages collaboration across the enterprise.

Cloud computing  scales data science by providing access to additional processing power, storage, and other tools required for data science projects.

Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end user—whether they are a large enterprise or a small startup.

Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud®, also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights. 

Enterprises can unlock numerous benefits from data science. Common use cases include process optimization through intelligent automation and enhanced targeting and personalization to improve the customer experience (CX). However, more specific examples include:

Here are a few representative use cases for data science and artificial intelligence:

• An international bank  delivers faster loan services with a mobile app  using machine learning-powered credit risk models and a  hybrid cloud computing  architecture that is both powerful and secure.
• An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
• A robotic process automation (RPA) solution provider developed a  cognitive business process mining solution  that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.
• A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
• An  urban police department created statistical incident analysis tools  (link resides outside ibm.com) to help officers understand when and where to deploy resources in order to prevent crime. The data-driven solution creates reports and dashboards to augment situational awareness for field officers.
• Shanghai Changjiang Science and Technology Development used IBM® Watson® technology to build an  AI-based medical assessment platform  that can analyze existing medical records to categorize patients based on their risk of experiencing a stroke and that can predict the success rate of different treatment plans.

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Fundamental of Data Science

• What is Data Science with Example?
• What Are the Roles and Responsibilities of a Data Scientist?
• Top 10 Data Science Job Profiles

Applications of Data Science

• Data Science vs Data Analytics
• Data Science VS Machine Learning
• Difference Between Data Science and Business Intelligence
• Data Science Fundamentals
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• How Much Math Do You Need to Become a Data Scientist?

Programming Language for Data Science

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Complete Data Science Program

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• Why Data Science Jobs Are in High Demand?

Data Science is a domain that comprises many sub-domains such as artificial intelligence, machine learning, statistics, data visualization, and analytics as well as provides practical examples and exercises to help you apply these concepts in the real world. Over the past few years, there has been tremendous demand for data scientists. To improve business efficiency it becomes important to analyze the data.

In this data science tutorial, we will provide a comprehensive overview of the core concepts, tools, and techniques used in the field of data science.

Data Science is a field that involves extracting insights and knowledge from data using various techniques and tools. If you are a beginner in Data Science, here are some steps you can follow to get started:

• Learn Programming: Programming is a fundamental skill for Data Science. Python is the most commonly used programming language in Data Science, and it has several libraries that are useful for Data Science, such as NumPy, Pandas, and Scikit-learn. You can start by learning the basics of Python programming.
• Learn Statistics: Statistics is the foundation of Data Science. Understanding statistical concepts such as mean, median, variance, and standard deviation is crucial for working with data. You can start by learning the basics of statistics.
• Learn Data Visualization: Data visualization is an essential skill for Data Science. It helps to understand patterns and trends in data. There are several libraries in Python that are useful for Data Visualization, such as Matplotlib and Seaborn.
• Learn Machine Learning: Machine learning is the core of Data Science. It involves building models that can learn from data and make predictions. There are several types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning. You can start by learning the basics of machine learning.
• Practice with Projects: Practice is essential for learning Data Science. You can start by working on small projects such as data cleaning, data analysis, and machine learning models. Kaggle is a platform where you can find data science projects and competitions to practice your skills.
• Learn from the Community: The Data Science community is very active, and there are several resources available to learn from. You can join online communities such as Reddit, LinkedIn, or Twitter. You can also attend local Data Science meetups and events.
• Continuously Learn: Data Science is a rapidly evolving field, and new techniques and tools are constantly emerging. Therefore, it’s essential to keep learning and stay updated with the latest trends and developments in Data Science.

In summary, learning Data Science involves programming, statistics, data visualization, machine learning, practice, learning from the community, and continuous learning. With dedication and consistent effort, you can become proficient in Data Science and start building solutions to real-world problems.

By the end of this tutorial, you’ll have a solid understanding of the key concepts and tools used in data science for beginners , and be well on your way to becoming proficient in the field.

Need for Data Science

There are 4 major reasons why there is a need for data science in the existing world today.

• Businesses are running today based on customer insight and that’s where data science comes from. With the help of data science, companies use Data Mining and sorting techniques to understand the area of interest of their users.
• Today, data science is being actively used to trim unstructured and unorganized data that also consumes less time.
• It helps in identifying the objective of a business and helps in reaching the goal (meanwhile it also helps in predicting the futuristic data based on the behavioural pattern)
• It empowers your organization by allocating the best of the best people within your workforce. It helps in sorting and filtering out the candidates from different platforms and that proportionally saves a lot of time also the chances of hiring a good candidate become more powerful.

Careers in Data Science

Data Science has been considered one of the most desirable jobs in the IT field today. The growth opportunities in data science jobs are comparatively high than in any other job. Companies are now focusing more on data science jobs to elevate their business goals which has also created a flood of data science jobs in the market.  

Some of the most notable jobs in data science are:- 

•  Data Scientist,
•  Data Architect,
•  Data Administrator,
•  Data Analyst, 
•  Business Analyst.

Data Science Life Cycle

It is a methodology followed to solve the data science problem.

• Business Understanding
• Data Understanding
• Preparation of Data
• Exploratory Data Analysis
• Data Modeling
• Model Evaluation
• Model Deployment

There are many applications of data science are as follows:- 

• Search Engines, 
• Transport, Finance,
•  E-Commerce, 
• Health Care, 
• Image Recognition,
•  Targeting recommendations, etc.

Prerequisites & Tools for Data Science

To gain expertise in the field of data science. firstly, you need to have a strong foundation in various aspects of data science. which includes knowledge of query languages like:- SQL, programming languages like R and python, and as well as visualization tools like:- PowerBI, Quilsense, Quilview, and Tableau. Additionally, having a basic understanding of statistics for machine learning is crucial. To effectively apply machine learning algorithms, it is essential to practice and implement them with use cases relevant to your desired domain.

Section 1: Python Basic

• Introduction of Python
• Taking input in Python
• Object-Oriented Programming
• Exception Handling

Section 2: R Basic

• Introduction to R Programming Language
• Decision Making – if, if-else, if-else-if ladder, nested if-else, and switch
• Loops (for, while, repeat)
• Encapsulation
• Polymorphism
• Inheritance

Section 3: Data Analysis with Python

• What is Data Analysis
• Data Analysis using Python
• Steps of Data Analysis Process
• Import Excel file with Pandas
• Import Text file with Pandas
• Read JSON Files with Pandas
• Pandas DataFrame
• Overview of Data Cleaning
• Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
• Working with Missing Data in Pandas
• Identify and Handle Missing Values
• Why is It Important?
• Data Visualization using Matplotlib
• Style Plots using Matplotlib
• Line chart in matplotlib
• Bar Plot in Matplotlib
• Box Plot in Python using matplotlib
• Scatter Plot in Matplotlib
• Heatmap in Matplotlib
• Three-dimensional Plotting using Matplotlib
• Seaborn Kdeplot
• Data Visualization with Python Seaborn
• Interactive Data Visualization with Bokeh
• Time Series Plot or Line plot with Pandas
• Exploratory Data Analysis on Iris Dataset
• Exploratory Data Analysis on Titanic Dataset

Section 4: Data Analysis with R

• Importing Data in R Script
• Import Data from a File in R
• Import a CSV File into R
• Data Frames in R
• DataFrame Manipulation
• Data Cleaning in R
• Working with Missing Data in R
• Data visualization with R and ggplot2
• Scatter plots in R Language 
• Graph Plotting in R Programming 
• Visualizing Missing Data with Barplot in R
• Histograms in R language
• Boxplots in R Language
• Time series visualization with ggplot2 in R
• Exploratory Data Analysis in R

Section 5: Web Scraping

• Introduction to Web Scraping
• What is Web Scraping and How to Use It?
• Web scraping from Wikipedia using Python
• Scraping Amazon Product Information using Beautiful Soup
• Web Scraping – Amazon Customer Reviews
• Scrape LinkedIn Using Selenium And Beautiful Soup in Python
• Extract all the URLs from the webpage Using R Language

Section 6: Basic Stat Mathematics

• Mean, Standard Deviation and Variance — Implementation
• Derivative and Function minimization
• Probability Distributions[ Set 1 , Set 2 , Set 3 ]
• Confidence Intervals
• Correlation and Covariance
• Random Variables
• Paired T-test
• Chi-squared Test
• ANOVA Test using Python[ One-way , Two-way ]
• ANOVA Test using R
• F-Stats With Python
• F-Stats With R

Section 7: Machine Learning

• Linear Regression
• Regression Trees
• Non-Linear Regression
• Bayesian Linear Regression
• Polynomial Regression[ Using Python, Using R ]
• Random Forest
• Decision Trees
• Logistic Regression
• Support Vector Machines
• Neural Networks
• K-means clustering
• DBScan clustering
• KNN (k-nearest neighbours)
• Hierarchal clustering
• Anomaly detection
• Principle Component Analysis
• Decision Tree
• Implementing Decision tree
• Decision Tree Regression using sklearn

Section 8: Deep Learning

• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptrons (MLPs)
• Deep Learning with Python OpenCV
• Pneumonia Detection using Deep Learning

Section 9: Natural Language Processing

• Introduction to Natural Language Processing
• Natural Language Processing
• Applications of NLP
• Scikit-learn
• Natural language Toolkit (NLTK)
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Syntax Tree – Natural Language Processing
• Translation and Natural Language Processing using Google
• NLP analysis of Restaurant reviews

Some project Ideas for Beginner in Data Science – link

Faqs on data science tutorials for beginners, q1: what is data science.

Answer:  

Data science is a field that involves using techniques from statistics, mathematics, and computer science to analyze and draw insights from data.

Q2: What skills do I need to be a data scientist?

Data scientists typically need skills in statistics, machine learning, data visualization, and programming. Strong communication and critical thinking skills are also important.

Q3: What programming languages should I learn for data science?

Some popular programming languages for data science include Python, R, and SQL. It’s also helpful to have some familiarity with other languages like Java and C++.

Q4: How long does it take to learn data science?

Learning data science is an ongoing process that can take several months to several years, depending on your background and level of experience.

Q5: What kind of jobs can I get with a background in data science?

Some common job titles in data science include data analyst, data scientist, machine learning engineer, and business intelligence analyst.

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Top Data Science Projects With Source Code

Data science project ideas, best data science projects for beginners, intermediate data science projects with source code, advanced data science projects with source code, additional resources.

Data Science continues to grow in popularity as a promising career path for this era. It’s one of the most exciting and attractive options available. Demand for Data Scientists is increasing in the market. According to recent reports, demand will skyrocket in the future years, increasing by many times. Data Science encompasses a wide range of scientific methods, procedures, techniques, and information retrieval systems to detect meaningful patterns in organized and unstructured data. More opportunities emerge in the market as more industries recognize the value of Data Science. 

If you’re interested in Data Science and want to learn more about the technology, now is as good a time as ever to develop your abilities to understand and manage the upcoming problems. Initially, understanding it can be difficult, but with regular effort, you will soon understand the many concepts and terminology used in the field. If you are interested in becoming a Data Scientist , it is strongly recommended that you apply your skills to become a competent professional in this sector. If you’re genuinely interested in learning what it’s like to be a professional after gaining some solid theoretical understanding of Data Science, now is the time to start working on some actual projects. 

As a result, participating in live Data Science Projects will enhance your confidence, technical expertise, and general confidence. But, most significantly, if you undertake Data Science projects for final year projects, you will find it much simpler to land a solid job.

Confused about your next job?

This article aims to give project ideas on data science that are appropriate for different levels of learners.

 This section will provide a list of data science project ideas for students new to Python or data science in general. These data science projects in python ideas will provide you with all of the tools you’ll need to succeed as a data science developer . The following are the data science project ideas with source code.

1. Fake News Detection Using Python

Fake news do not require any introduction. It is very much easy to spread all the fake information in today’s all-connected world across the internet. Fake news is sometimes transmitted through the internet by some unauthorised sources, which creates issues for the targeted person and it makes them panic and leads to even violence. To combat the spread of fake news, it’s critical to determine the information’s legitimacy, which this Data Science project can help with. To do so, Python can be used, and a model is created using TfidfVectorizer. PassiveAggressiveClassifier can be implemented to distinguish between true and fake news. Pandas, NumPy, and sci-kit-learn are some Python packages suitable for this project, and we can utilize News.csv for the dataset.

Source Code – Fake news detection using python

2. Data Science Project on Detecting Forest Fire

Developing a project for identifying the forest fire and wildfire system is an alternatively good example to exhibit one’s skills in Data Scienc e. The forest fire or wildfire is an uncontrollable fire that develops in a forest. All the  forest fir will create havoc during weekends on the animal habitat, surrounding environment and human property. k-means clustering can be used for the identification of the  crucial hotspots during forest fire  and to reduce the  severity , to regulate them and even  to predict the behaviour of the wildfire. This is advantageous for allocating the required resources. To enhance the model’s accuracy, it is ideal to use climatological data to find out the common periods and seasons for wildfires.

Source Code – Detecting Forest Fire

3. Detection of Road Lane Lines  

A Live Lane-Line Detection Systems built-in Python language is another Data Science project idea for beginners. A human driver receives lane detecting instruction from lines placed on the road in this project. The lines placed on the roads indicate where the lanes are located for human driving. It also refers to the vehicle’s steering direction. This application is crucial for the development of self-driving cars. This application for the Data Science Project is critical for the development of self-driving cars.

Source Code – Detection of Road Lane Lines

4. Project on Sentimental Analysis

The act of evaluating words to determine sentiments and opinions that may be positive or negative in polarity is known as sentimental analysis. This is a sort of categorization in which the classifications are either binary (optimistic or pessimistic) or multiple (happy, angry, sad, disgusted, etc.). The project is written R Language, and u the dataset provided by the Janeausten R package is used. The general-purpose lexicons like AFINN, bing, and Loughran are used to execute an inner join and present the results using a word cloud.

Source Code – Project on Sentimental Analysis

5. Project on Influences of Climatic Pattern on the food chain supply globally

The abnormalities and changes occurring in the climate very often are the main challenges impressed on the environment that needs to be taken care of. These environmental changes will affect the human beings on earth. This Data Science Project makes an attempt to analyse the changes in the food production globally that occurs due to change in climatic conditions. The main purpose of this study is to evaluate the consequences of climatic changes on primary agricultural yields. This project will evaluate all the effects related to change in temperature and rainfall pattern. The amount of carbon dioxide that impacts plant development and the uncertainties in climate change will next be considered. As a result, data representations will be the primary focus of this project. It will also assess productivity across different locations and geographical regions.

In this section, data science projects for intermediate level learners are discussed:

1. Project on  Speech Recognition through the Emotions

One of the fundamental strategies for us to communicate ourselves is the speech, and it involves various feelings including silence, anger, happiness, and passion etc. It is possible to use the emotions behind the speech to reorganize our emotions, the service we offer, and the end products to deliver a custom-made service to particular persons by evaluating the emotions behind it. The main aim of this project is to identify and get the feelings from multiple files involving sound that comprises the human speech. Python’s SoundFile, Librosa,, NumPy, Scikit-learn, and PyAaudio packages can be used to produce something alike. In addition, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for the dataset containing over 7300 files.

Source Code – Speech Emotion Analyzer and Speech Emotion Recognition

2. Project on Gender Detection and Age Prediction 

This project on detecting the gender and predicting the age identified as a classification challenge, will put your Machine Learning and Computer Vision skills to work. The goal is to create a system that can analyze a person’s photograph and determine their age and gender. Python and the OpenCV library to implement Convolutional Neural Networks can be used for this entertaining project. For this project, the Adience dataset can be downloaded. Remember that factors like cosmetics, lighting, and facial expressions will make this difficult, and try to throw your model off.

Source Code – Gender Detection and Age Prediction

3. Project on Developing Chatbots

Chatbots are important for companies since this project can answer all the questions posed by the clients and information without the process being slowing down. The customer support workload has been decreased by the procedures which is fully automating. This process can be easily obtained by implementing Machine Learning,  Artificial Intelligence and Data Science techniques. Chatbots operate by assessing the customer’s input and responding with a mapped response. Recurrent Neural Networks using the intentions JSON dataset may be used to train the chatbot, while Python can be used to implement it. The objective of the chatbot will determine whether it is domain-specific or open-domain.

Source Code – Developing Chatbots

4. Project on Detection of Drowsiness in Drivers

Sleepy drivers are one of the causes of road accidents, which claim many fatalities each year. Because drowsiness is a possible cause of road danger, one of the best methods to avoid it is to install a drowsiness detection system. Another technology that can save many lives is a driver sleepiness detection system that continuously assesses the driver’s eyes and alerts him with alarms if the system detects that the driver closes his eyes very often. A webcam is required for this project for the system to monitor the driver’s eyes regularly. This Python project will require a deep learning model as well as packages such as OpenCV, TensorFlow, Pygame, and Keras to do this.

Source Code – Driver Drowsiness Detection and Driver Drowsiness Detection

5. Project on Diabetic Retinopathy

Diabetic Retinopathy is a primary cause of blindness in people with diabetes. An automated diabetic retinopathy screening system can be developed. On retina photographs of both damaged and healthy people, a neural network can be trained. This research will determine whether or not the patient has retinopathy.

Source Code – Diabetic Retinopathy Detection and Diabetic Retinopathy Detection Topics

In this section, the data science projects for advanced learners are discussed.

1. Project on Detection of Credit Card Fraud

Credit card fraud is more widespread than you might believe, and it’s been on the rise recently. By the end of 2022, we’ll have crossed a billion credit card users, metaphorically. However, credit card firms have been able to successfully identify and intercept these frauds with significant accuracy because of advancements in technology such as Artificial Intelligence, Machine Learning, and Data Science . Simply stated, the concept is to examine a customer’s regular spending pattern, involving locating the geography of such spendings, to distinguish between fraudulent and non-fraudulent transactions. The languages R or Python can be used to ingest the customer’s recent transactions as a dataset into decision trees, Artificial Neural Networks, and Logistic Regression for this project. The system’s overall accuracy would increases if additional data is fed.

Source Code – Credit Card Fraud Detection and Credit Card Fraud Topics

2. Project on Customer Segmentations

One of the most well-known Data Science projects is customer segmentation. Companies build various groupings of customers before launching any marketing. Customer segmentation is a prominent unsupervised learning application. Companies utilize clustering to discover client groupings and target the possible user base. They classify clients based on shared traits such as gender, age, interests, and spending habits to market to each group successfully. Visualization of the gender and age distributions can be done using K-means clustering. Then their annual earnings and spending habits are also analyzed.

Source Code – Customer Segmentations and Customer Segmentations Topics

3. Project on the recognition of traffic signals

Traffic signs and rules are extremely crucial to observe to avoid any accidents. To observe the guideline, one must first comprehend the appearance of the traffic sign. Before receiving a driver’s license, a person must first study all of the traffic signs. However, automated vehicles are on the rise, and in the not-too-distant future, there will be no human drivers. In the Traffic Signs Recognition project, you’ll discover how software can use a picture as input to recognize the type of traffic sign. The German Traffic Signs Recognition Benchmark dataset (GTSRB) is used to train a Deep Neural Network that can identify the class of a traffic sign. A simple graphical user interface (GUI) to communicate with the application can also be created. Python can be used.

Source Code – Traffic Sign Detection , Traffic Sign Detection Using Capsule Networks , and Traffic Sign Recognition

4.Project on recommendation System for Films

In this data science project, the language R can be used to generate a machine learning-based movie recommendation. A recommendation system uses a filtering procedure to send forth suggestions to users based on other users’ interests and browsing history. If A and B enjoy Home Alone and B enjoys Mean Girls, it can be recommended to A; they may enjoy it as well. Customers will be more engaged with the platform as a result of this.

Source Code – Recommendation System for Films

5. Project on Breast Cancer Classification

Breast cancer cases have been on the rise in recent years, and the best approach to combat it is to detect it early and adopt appropriate preventive measures. To develop such a system with Python, the model can be trained on the IDC(Invasive Ductal Carcinoma) dataset, which provides histology images for cancer-inducing malignant cells. Convolutional Neural Networks are better suited for this project, and NumPy, OpenCV, TensorFlow, Keras, sci-kit-learn, and Matplotlib are among the Python libraries that can be utilized.

Source Code – Breast Cancer Risk Prediction , Breast Cancer Classification , and Breast Cancer Classification Topics

A thorough insight about data science, its importance, and the data science projects for beginners and final years are discussed. All of these data science projects’ source code is available on Github. So get started right away and create a Data Science project. Follow the steps from beginner to advanced, and then move on to other projects.

Q. How do you get ideas for data science projects?

The ideas for data science projects can be obtained by following these simple tips:

• Attending networking events and mingle with people.
• Make use of your interests and hobbies to come up with new ideas.
• In your day job, solve problems
• Get to know the data science toolbox.
• Make your data science solutions.

Q. What projects do data scientists work on?

There are four different types of projects on which data scientists work:

• Projects to cleanse up data
• Projects involving exploratory data analysis.
• Projects involving data visualization
• Projects involving machine learning

Q. What projects can I do with R?

The following are the list of projects that can be done using R:

• Project on Sentiment Analysis 
• Project on Uber data analysis
• Project on Movie recommendation systems
• Project on Customer segmentation
• Project on Credit card fraud detection
• Project on wine preference prediction

Q. How do you contribute to open source data science projects?

There are numerous motivations to contribute to an open-source project, including:

• To make the software, you use every day better
• If you require a mentor, you should look for one.
• to get creative knowledge
• to demonstrate your abilities
• To learn a lot more about the software you’re working with
• To improve your reputation and advance your career

Q. How do I start a data science from scratch?

To start the data science journey from scratch, you should follow these steps mentioned below:

• Learn Python
• Learn the fundamentals of statistics and mathematics
• Learn Data analysis using Python
• Learn machine learning and start doing projects

Q.  How do you put a data science project on your resume?

Projects can be stated as accomplishments below a job description on a resume. Projects, Personal Projects, and Academic Projects can all be listed in a distinct section. Academic work should be listed in the education portion of the resume. You can also make a CV that is focused on a certain project.

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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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Data science homework help for beginners, why do students need data science homework help, 1.overwhelming workload, 2. lack of comprehension, 3. fear of failure, advantages of data science assignment help, important topics in data science homework help, listed below are the popular concepts and topics that the data science project help is based on:.

• Data Processing & Data Visualization • Tableau Dashboard Development • Data Wrangling & Data Transformation • Data Analysis using SPSS, Python, Rstudio, SAS • Statistical Distributions & Hypothesis Testing • Segmentation through Clustering • Predictive Modeling & Analysis • K-Nearest Neighborhood • Naïve Bayes Model • Linear & Quadratic Discriminat Analysis (LDA & QDA) • Linear Regression Modeling • Logistics Regression Modeling • Hierarchical Clustering, K-means Clustering • Time Series Forecasting & Survival Analysis • Recommendation systems • Data Engineering, Big Data Processing

• Machine Learning & Deep Learning Algorithms • Neural Network Model– ANN, CNN, RNN • Natural Language Processing (Encoders-Decoders, Word Vectorizer) • Fundamentals of Big Data and Integration of Hadoop with R & Python • Applied Mathematics and Computer Science • Large Language Models (LLM) • Generative Pre-trained Transformers (GPT) • Natural Language Generation (NLG) using LLM • Language Translation • Text Summarization using LLM • Text Classification using LLM • Question-answering tasks using LLM • Sentiment Analysis using LLM • Data Labeling & Text Annotations

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NASA's Voyager 1 spacecraft is talking nonsense. Its friends on Earth are worried

Nell Greenfieldboyce

This artist's impression shows one of the Voyager spacecraft moving through the darkness of space. NASA/JPL-Caltech hide caption

This artist's impression shows one of the Voyager spacecraft moving through the darkness of space.

The last time Stamatios "Tom" Krimigis saw the Voyager 1 space probe in person, it was the summer of 1977, just before it launched from Cape Canaveral, Florida.

Now Voyager 1 is over 15 billion miles away, beyond what many consider to be the edge of the solar system. Yet the on-board instrument Krimigis is in charge of is still going strong.

"I am the most surprised person in the world," says Krimigis — after all, the spacecraft's original mission to Jupiter and Saturn was only supposed to last about four years.

These days, though, he's also feeling another emotion when he thinks of Voyager 1.

"Frankly, I'm very worried," he says.

Ever since mid-November, the Voyager 1 spacecraft has been sending messages back to Earth that don't make any sense. It's as if the aging spacecraft has suffered some kind of stroke that's interfering with its ability to speak.

"It basically stopped talking to us in a coherent manner," says Suzanne Dodd of NASA's Jet Propulsion Laboratory, who has been the project manager for the Voyager interstellar mission since 2010. "It's a serious problem."

Instead of sending messages home in binary code, Voyager 1 is now just sending back alternating 1s and 0s. Dodd's team has tried the usual tricks to reset things — with no luck.

It looks like there's a problem with the onboard computer that takes data and packages it up to send back home. All of this computer technology is primitive compared to, say, the key fob that unlocks your car, says Dodd.

"The button you press to open the door of your car, that has more compute power than the Voyager spacecrafts do," she says. "It's remarkable that they keep flying, and that they've flown for 46-plus years."

Each of the Voyager probes carries an American flag and a copy of a golden record that can play greetings in many languages. NASA/JPL-Caltech hide caption

Each of the Voyager probes carries an American flag and a copy of a golden record that can play greetings in many languages.

Voyager 1 and its twin, Voyager 2, have outlasted many of those who designed and built them. So to try to fix Voyager 1's current woes, the dozen or so people on Dodd's team have had to pore over yellowed documents and old mimeographs.

"They're doing a lot of work to try and get into the heads of the original developers and figure out why they designed something the way they did and what we could possibly try that might give us some answers to what's going wrong with the spacecraft," says Dodd.

She says that they do have a list of possible fixes. As time goes on, they'll likely start sending commands to Voyager 1 that are more bold and risky.

"The things that we will do going forward are probably more challenging in the sense that you can't tell exactly if it's going to execute correctly — or if you're going to maybe do something you didn't want to do, inadvertently," says Dodd.

Linda Spilker , who serves as the Voyager mission's project scientist at NASA's Jet Propulsion Laboratory, says that when she comes to work she sees "all of these circuit diagrams up on the wall with sticky notes attached. And these people are just having a great time trying to troubleshoot, you know, the 60's and 70's technology."

"I'm cautiously optimistic," she says. "There's a lot of creativity there."

Still, this is a painstaking process that could take weeks, or even months. Voyager 1 is so distant, it takes almost a whole day for a signal to travel out there, and then a whole day for its response to return.

"We'll keep trying," says Dodd, "and it won't be quick."

In the meantime, Voyager's 1 discombobulation is a bummer for researchers like Stella Ocker , an astronomer with Caltech and the Carnegie Observatories

"We haven't been getting science data since this anomaly started," says Ocker, "and what that means is that we don't know what the environment that the spacecraft is traveling through looks like."

After 35 Years, Voyager Nears Edge Of Solar System

That interstellar environment isn't just empty darkness, she says. It contains stuff like gas, dust, and cosmic rays. Only the twin Voyager probes are far out enough to sample this cosmic stew.

"The science that I'm really interested in doing is actually only possible with Voyager 1," says Ocker, because Voyager 2 — despite being generally healthy for its advanced age — can't take the particular measurements she needs for her research.

Even if NASA's experts and consultants somehow come up with a miraculous plan that can get Voyager 1 back to normal, its time is running out.

The two Voyager probes are powered by plutonium, but that power system will eventually run out of juice. Mission managers have turned off heaters and taken other measures to conserve power and extend the Voyager probes' lifespan.

"My motto for a long time was 50 years or bust," says Krimigis with a laugh, "but we're sort of approaching that."

In a couple of years, the ebbing power supply will force managers to start turning off science instruments, one by one. The very last instrument might keep going until around 2030 or so.

When the power runs out and the probes are lifeless, Krimigis says both of these legendary space probes will basically become "space junk."

"It pains me to say that," he says. While Krimigis has participated in space missions to every planet, he says the Voyager program has a special place in his heart.

Spilker points out that each spacecraft will keep moving outward, carrying its copy of a golden record that has recorded greetings in many languages, along with the sounds of Earth.

"The science mission will end. But a part of Voyager and a part of us will continue on in the space between the stars," says Spilker, noting that the golden records "may even outlast humanity as we know it."

Krimigis, though, doubts that any alien will ever stumble across a Voyager probe and have a listen.

"Space is empty," he says, "and the probability of Voyager ever running into a planet is probably slim to none."

It will take about 40,000 years for Voyager 1 to approach another star; it will come within 1.7 light years of what NASA calls "an obscure star in the constellation Ursa Minor" — also known as the Little Dipper.

If NASA greenlights this interstellar mission, it could last 100 years

Knowing that the Voyager probes are running out of time, scientists have been drawing up plans for a new mission that, if funded and launched by NASA, would send another probe even farther out into the space between stars.

"If it happens, it would launch in the 2030s," says Ocker, "and it would reach twice as far as Voyager 1 in just 50 years."

• space exploration
• space science