Top Free Data Science Courses
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Rashmi Karan
Manager - Content
Updated on Sep 22, 2022 18:36 IST
There has been a rapid upsurge in the number of people enrolling in online courses since we observed lockdown worldwide. Popular platforms like Coursera, edX, Datacamp, Udacity, NPTEL, Pluralsight, and Udemy, among others, remained the most high-in-demand online learning platforms. Data science is among the most popular picks for online learners, and data science courses gained much popularity.
To learn more about data science, read our blog – What is data science?
There has been a huge demand for the best data science certifications and courses from the best platforms. We have handpicked some of the most trending free data science courses. Most of these courses are auditable, meaning you can access them for free but would need to pay some amount to avail the certificate. Nonetheless, this is an excellent opportunity for those looking forward to learning data science.
You may also be interested in exploring:
These data science online courses are categorized based on their difficulty level. You can pick the one that suits your business requirements and personal aspirations. Let’s take a look.
Data Science Courses for Beginners
Recommended online courses
Best-suited Data Science courses for you
Learn Data Science with these high-rated online courses
₹1.18 L
12 months
₹80 K
4 months
₹2.5 L
2 years
₹90 K
24 months
₹2.5 L
2 years
Free
4 weeks
₹1.24 L
48 months
Data Science Courses at Intermediate Levels
| Course Name | Duration | Description |
| Computer Science: Algorithms, Theory, and Machines on Coursera | 20 Hours | Computer Science: Algorithms, Theory, and Machines by Princeton University cover the concepts of computer science for people having basic familiarity with Java programming. |
| Python and Statistics for Financial Analysis by the HKUST on Coursera | 13 hours | Python and Statistics for Financial Analysis include python coding and statistical concepts. You will learn how to apply these concepts to analyzing financial data, such as stock data. |
| Finalize a Data Science Project on Coursera | 12 hours | This course is designed for business professionals willing to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models, and implement and test pipelines that automate the model training, tuning, and deployment processes. |
| Address Business Issues with Data Science on Coursera | 3 weeks | Address Business Issues with Data Science is designed for business professionals willing to learn how to determine if a business issue is appropriate for a data science project. |
| Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory on Coursera | 3 weeks | Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory is a data science project for anyone with a foundation in programming and machine learning. In this project, you will learn to use Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project. |
| Applied Statistics with Python by edX | 16 Weeks | Applied Statistics with Python covers descriptive statistics and probability, hypothesis tests and confidence intervals, and linear regression analysis. |
| Data Science at Scale Specialization by the University of Washington on Coursera | 5 Months | Data Science at Scale Specialization will help to master the concepts of scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results. |
| Statistical Inference and Hypothesis Testing in Data Science Applications on Coursera | 5 Weeks | This course will focus on the theory and implementation of hypothesis testing and how to make informed decisions from data. You will also learn the general logic of hypothesis testing, error and error rates, power, simulation, the correct computation and interpretation of p-values, misuse of testing concepts, especially p-values, and the ethical implications of such misuse. |
| Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory on Coursera | 2 Hours | Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory is a guided project for those with a foundation in programming and machine learning and who want to develop Data Science and Machine learning projects. |
| Advanced Clinical Data Science by University of Colorado | 4 Weeks | Advanced Clinical Data Science will help you to deal with advanced clinical data science topics and techniques, including temporal and research quality analysis. |
| Data Science as a Field by Coursera | 4 Weeks | Data Science as a Field offers a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about Data Science. |
| Python for Genomic Data Science by Coursera | 4 Weeks | Python for Genomic Data Science offers an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization at Johns Hopkins University. |
| Applications of TinyML by Harvardx | 5 Weeks | Applications of TinyML course covers tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices. |
| Foundations of Data Science by Indian Institute of Management, Bangalore on edX | 7 weeks | The course covers the basic concepts in probability, their implementation in ML algorithms for Market Basket Analysis and Recommender Systems, random variables, discrete and continuous probability distributions, sampling, estimation, and central limit theorem. |
| Introduction to Data Science in Python by University of Michigan by the University of Michigan on Coursera | 4 weeks | This course will educate the learners about the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables. |
| Pattern Discovery in Data Mining by University of Illinois at Urbana-Champaign on Coursera | 5 weeks | You can learn the general concepts and applications of data mining, concepts and applications of pattern discovery, methods for data-driven phrase mining, and applications of pattern discovery. |
| R Programming by Johns Hopkins University on Coursera | 4 Weeks | In this course, you will learn how to program in R and use R for practical data analysis. You will learn how to install and configure the software necessary for a statistical programming environment. |
| Build a Data Science Web App with Streamlit and Python by Coursera | 2 Hours | It is a hands-on project on building your first machine learning web app with the Streamlit library in Python. The project would require you to have prior experience writing simple Python scripts and using pandas for data manipulation. |
| Introduction to Data Science in Python by University of Michigan by Coursera | 4 Weeks | With this course, you will learn the basics of the python programming environment, such as fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the numpy library. |
| Data Science in Stratified Healthcare and Precision Medicine by The University of Edinburgh on Coursera | 5 Weeks | The course covers different types of data and computational methods in stratified healthcare and precision medicine, as well as successful case studies. |
| Data Science with Databricks for Data Analysts Specialization by Coursera | 5 Months | This specialization is designed for data analysts who can utilize advanced technologies like Databricks and Apache Spark, enabling them to expand the toolbox for working with data. |
| Machine Learning: Unsupervised Learning by Georgia Institute of Technology on Udacity | 1 Month | This course will help you to use Unsupervised Learning approaches to find structure in unlabeled data. This includes randomized optimization, clustering, and feature selection and transformation. |
| Python for Data Engineering Project by IBM on edX | 1 week | You would learn how to use Python for data engineering tasks, including extensive data set extraction from multiple sources using web scraping and APIs, data transformation to gain valuable business insights, and data extraction in multiple file formats from different sources, etc. |
| Applied Statistics with Python by Southern New Hampshire University on edX | 16 weeks | The content covers three components of Applied Statistics, including – descriptive statistics and probability, hypothesis tests and confidence intervals, and linear regression analysis. |
| Hands-on Text Mining and Analytics by Yonsei University on Coursera | 6 weeks | With the help of this course, you can learn key components of text mining techniques, including text preprocessing, sentiment analysis, and topic modeling. |
| Bayesian Statistics: From Concept to Data Analysis by the University of California on Coursera | 10 Hours | With the help of this course, you will understand the concepts of the Bayesian approach, the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses. |
| Causal Diagrams: Draw Your Assumptions Before Your Conclusions on edX | 9 Weeks | This course will translate expert knowledge into a causal diagram, learn how to draw causal diagrams under different assumptions, use causal diagrams to identify common biases, etc. |
| Genomic Data Science Specialization by Johns Hopkins University on Coursera | 10 months | This Specialization will help you to understand, analyze, and interpret data from next-generation sequencing experiments. Learn working with tools like the command line and a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. |
| Data Science with Databricks for Data Analysts Specialization by Databricks on Coursera | 5 months | This course will enable you to learn new technologies like Databricks and Apache Spark, and you will complete a series of hands-on lab assignments and projects. The lab assignments will allow you to test-drive Databricks and Apache Spark to streamline today’s most popular data science workflows. |
| Data Science: Statistics and Machine Learning Specialization by Johns Hopkins University on Coursera | 5 Months | This course covers topics like statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you will apply the skills learned by building a data product using real-world data. |
| Data Science by IBM on Coursera | 6 Hours | The course covers key topics like data science introduction and algorithms, Big data & Hadoop, Neural networks and Deep Learning, and application of Data science. |
| Regression Models by Johns Hopkins University on Coursera | 5 weeks | This course covers regression analysis, least squares, and regression models’ inference. It also focuses on modern thinking on model selection and novel uses of regression models, including scatterplot smoothing. |
| Exploratory Data Analysis by Coursera (Guided Project) | It is a project-based course that will help you to learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. You will apply EDA to a real-world dataset. | |
| Machine Learning Using SAS Viya by SAS on Coursera | 4 weeks | The course covers the theoretical foundation for different techniques of supervised machine learning models. This course uses Model Studio, the pipeline flow interface in SAS Viya. You will learn to prepare, develop, compare, and deploy advanced analytics models. |
| Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning from deeplearning.ai on Coursera | 4 weeks | This course will teach you the best practices for using TensorFlow, a popular open-source framework for machine learning. |
| Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership on Coursera | 4 weeks | This course will help you understand the end-to-end implementation of machine learning. You will learn how to avoid common management mistakes that hamper machine learning projects. |
| Statistical Inference and Modeling for High-throughput Experiments from Harvard University on edX | 4 weeks | In this specialized data science course, you will explore different statistics topics. These topics include multiple testing problems, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. You will then learn statistical modeling and its application in high-throughput data. |
| Data Visualization with Python | 10 hours | Data Visualization with Python will cover various techniques for presenting data visually. Apart from this, the course also covers data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. |
| Data Science For Business Leaders from Altius | 5 Weeks | The course is designed for business leaders, including executives, strategists, and innovators. This course will help them to drive a competitive advantage using data science. |
| AI Workflow: Business Priorities and Data Ingestion by IBM on Coursera | 6 Hours | The course is a part of the IBM AI Enterprise Workflow Certification specialization. It covers the specialization and prerequisites, design thinking concepts, and scientific thinking basics. You will also get to build a data ingestion pipeline using Python and Jupyter notebooks. |
| Fundamentals Of Artificial Intelligence from IIT Guwahati on NPTEL | 4 weeks | With this course, you can have a basic understanding of problem-solving, knowledge representation, reasoning, and learning methods of AI. It will also offer an overview of the principles and practices of AI to address complex real-world problems. |
| Statistics and R on edX | 4 weeks | The course will introduce basic statistical concepts and R programming skills required for data analysis in life sciences. |
| Data Analysis with R by Facebook on Udacity | 2 Months | The course is a part of the Data Analyst Nanodegree program. It is an approach to summarizing and visualizing the essential characteristics of a data set. You will also get to understand the data’s underlying structure and variables. |
| Essential Math for Machine Learning: R Edition by edX | 6 weeks | This machine learning course will teach you the essential mathematical foundations for machine learning and artificial intelligence. To complete this course successfully, you should have basic math knowledge and some programming experience. |
| Databases and SQL for Data Science by IBM on Coursera | 15 Hours | The course will introduce you to relational database concepts, which can help you learn and apply foundational knowledge of SQL. To take this course, you should know SQL, Python, or programming. |
| Applied Data Science with Python Specialization by the University of Michigan on Coursera | 5 months | This data science course is a series of 5 courses that would help you to gain new insights into your data. You would learn to apply data science methods and techniques and acquire analytical skills. |
| Data Analytics in Health – From Basics to Business on edX | 4 weeks | The course covers the application of data analytics and big data in ensuring better diagnosis, care, and curing. |
| Intro to Data Science by Udacity | 2 Months | This data science course will cover the basics of data science, including – Statistics and Machine Learning for Data Analysis Data Communication Information Visualization Working with Big Data |
| Getting and Cleaning Data by Johns Hopkins University via Coursera | 19 Hours | This course will cover the basic ways to obtain data from the web, APIs, databases, and other sources in various formats. You will also learn the basics of data cleaning and ways to achieve tidy data. This course also includes the basics of collecting, cleaning, and sharing data. |
| The Data Scientist’s Toolbox by Johns Hopkins University via Coursera | 13 Hours | This free data science course covers the basics of data analysis tools. It also covers a practical introduction to the tools for version control, markdown, git, GitHub, R, and RStudio. |
| Process Mining: Data science in Action by Eindhoven University of Technology via Coursera | 22 Hours | The course will highlight the key analysis techniques in process mining and various process discovery algorithms. It will provide easy-to-use software, real-life data sets, and practical skills to apply the theory across different applications directly. |
Data Science Courses for the Experts
| Course Name | Duration | Description |
| Health Data Science Foundation by the University of Illinois at Urbana-Champaign on Coursera | 24 hours | Health Data Science Foundation course is designed for people interested in machine learning applications in the medical field or vice versa. You will learn about health data analysis, types of neural networks, training, and application of neural networks applied on real-world medical scenarios, etc. |
| Practical Data Science Specialization by DeepLearning.AI and Amazon Web Services on Coursera | 3 Months | The specialization covers concepts of mathematics, statistics, data visualization, and programming using purpose-built ML tools in the AWS cloud. The course is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages. |
| Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI and Amazon Web Services on Coursera | 4 Months | Machine Learning Engineering for Production (MLOps) Specialization requires you to have prior knowledge of AI / deep learning, intermediate skills in Python, and experience with any deep learning framework (PyTorch, Keras, or TensorFlow). It will help you to conceptualize, build, and maintain integrated systems that continuously operate in production. |
| Communicating Data Science Results by the University of Washington on Coursera | 3 Weeks | Communicating Data Science Results will help you to learn to recognize, design, and use compelling visualizations. |
| Build a Data Science Web App with Streamlit and Python by Coursera | 2 Hours | In this guided project, you will build interactive web applications with Streamlit and Python and use Pandas for data manipulation in data science workflows. |
| Data Science for Business with R Programming by Coursera | 2 Hours | This Guided Project will help the professionals solve the basic questions of economics using data science tools, including what to produce and for whom to produce, by analyzing the market forces and trends. This project will guide business professionals in real-world strategic decision-making based on data and direct towards effective allocation of resources for business. |
| Building a Data Science Team by Johns Hopkins University on Coursera | 6 Hours | The course will guide you on finding the right people to fill out your data science team, organizing them to give them the best chance to feel empowered and successful, and managing your team as it grows. |
| Regression Models by Johns Hopkins University on Coursera | 4 Weeks | This course covers regression analysis, least squares, and inference using regression models, ANOVA, analysis of residuals, and variability, among other concepts. |
| Advanced Machine Learning on Google Cloud Specialization by Google Cloud Training on Coursera | 3 Months | This specialization focuses on advanced machine learning topics using the Google Cloud Platform, offering hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. |
| Introduction to Data Science and Basic Statistics for Business by TecdeMonterreyX on edX | 4 Weeks | This course will help you to acquire statistical methods and technological tools for decision-making in business. It covers the analysis of statistical elements of information, as well as concepts and statistical foundations for data science applications. |
| Advanced Linear Models for Data Science 1: Least Squares by Johns Hopkins University on Coursera | 8 Hours | Advanced Linear Models for Data Science 1: Least Squares is an introduction to least squares from a linear algebraic and mathematical perspective. You will gain a firm foundation in a linear algebraic treatment of regression modeling. |
| Advanced Linear Models for Data Science 2: Statistical Linear Models by Johns Hopkins University on Coursera | 6 Hours | This class is an introduction to least squares from a linear algebraic and mathematical perspective. |
| Developing AI Applications on Azure on Coursera | 6 Hours | This course introduces the concepts of Artificial Intelligence and Machine learning. It will discuss machine learning types and tasks and machine learning algorithms. |
| Advanced Statistics for Data Science Specialization by Johns Hopkins University on Coursera | 22 Hours | This specialization includes Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications such as probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling. |
| Python for Data Science on edX | 8 weeks | With the help of this course, you will learn how to use powerful, open-source Python tools, including Pandas, Git, and Matplotlib, to manipulate, analyze, and visualize complex datasets. |
| Google Cloud Computing Foundations from IIT Kharagpur, Google Cloud on NPTEL | 8 weeks | The course covers the basics of cloud, big data, and machine learning and its applicability to the Google Cloud Platform. 26 labs on Qwiklabs are a part of the course. |
| Deep Learning in Computer Vision by National Research University Higher School of Economics on Coursera | 17 hours | This data science course is part of the Advanced Machine Learning Specialization. It covers topics like computer vision, modern deep learning models, image and action recognition, and new image generation. |
| Python for Data Science from IIT Madras on NPTEL | 4 weeks | Learn how to use python programming for solving data science problems. |
| Predictive Analytics and Data Mining by University of Illinois at Urbana-Champaign on Coursera | 24 hours | This data science course is designed for businesses and managers to enable them to apply data analytics to real-world challenges. It will help them to identify the ideal analytical tools and understand valid and reliable ways to collect, analyze, visualize data, and utilize data in decision-making. |
| AI Workflow: Machine Learning, Visual Recognition and NLP by IBM | 7 hours | The program is designed for existing data science practitioners with expertise in building machine learning models. It aims to sharpen building and deploying AI skills in large enterprises. It includes lectures and case studies focusing on natural language processing and image analysis to provide a realistic context for the model pipelines. |
| Statistical Inference and Modeling for High-throughput Experiments by Harvard on edX | 4 weeks | This course covers various statistics topics such as multiple testing problems, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. You will then learn about statistical modeling and its application in high-throughput data. |
| Big Data, Genes, and Medicine by The State University of New York | 23 hours | You will get to explore different topics in Big Data Science and Bioinformatics, including Big Data analytics on real datasets in a healthcare and biological context. It will also help you prepare data, interpret and visualize the results, and share them. |
| Knowledge-Based AI: Cognitive Systems by Georgia Tech on Udacity | 7 Weeks | This is a core course in artificial intelligence. It covers structured knowledge representations, problem-solving methodologies, planning, decision-making, and learning methods. Learn to design knowledge-based AI agents and human cognition and build a relationship between knowledge-based artificial intelligence. |
| Artificial Intelligence for Robotics by Georgia Tech Masters on Udacity | 2 Months | The course covers basic methods in Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking, and control, with a focus on robotics. The program also covers programming examples and assignments for building self-driving cars. |
| Python for Data Science by the University of California, San Diego on edX | 4 Weeks | This advanced data science course will explore topics like Introduction to Spyder, Python for Data Science, Variables and Datatypes, Operators, Tuples, Dictionary, etc. |
| Big Data and Education by the University of Pennsylvania on edX | 8 Weeks | The course will explore different methodologies for educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You will learn how and when to apply these methods. |
| Applied AI with DeepLearning by IBM on Coursera | 22 Hours | The data science course will help you to understand different aspects of deep learning and models. It will also cover the usage of these modes by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. |
Hope this list helps you in making your decision to pick the most suitable data science courses. All the best!
FAQs
What are the major job responsibilities of a data scientist?
A data scientist's primary job responsibilities are - To dig data from primary and secondary sources To utilize machine learning techniques for selecting features, and building and optimizing classifiers To enhance data collection procedures for building analytic systems To process, clean, and verify the integrity of data To create automated anomaly detection systems and track their performance
What are the best resources to learn Python in Data Science?
You can learn Python through tutorials that cover concepts from beginner to advanced levels. Multiple eLearning portals provide such tutorials. You can learn Python from Books, tutorials, MOOCs, classroom courses, YouTube and also live Apps. However, you cannot become a good data scientist by just learning Python. You should master Data Science with Python which covers programming with Python, Database Technologies, a good hold on Mathematics and Statistical principles as well as Machine Learning with Python. You should also be good with Information Retrieval.
How is machine learning different from data science?
Machine learning, as well as statistical principles, are a small part of data science. Algorithms applied in machine learning are data dependent and apply a training set so as to fine-tune a model for algorithmic parameters. Most of them comprise of techniques like regression, naive Bayes or supervised clustering, etc.
How can I turn unstructured data into structured data in data science projects?
NLP and Information Extraction are the processes to do it. Suppose you are having a template that needs to be filled with data extracted from an unstructured information feed. Honestly, this is a very basic method of creating structured data out of an unstructured feed. Based on research, you can also discover structures of data from unstructured data. While there will be no template in the same you can construct a graph with multiple nodes which in a way represent data extracts as well as links that represent how or why information that is related to each other gets fragmented.
What is the difference between Big Data and Data Science?
The term big data is popularly used to describe exponential growth and availability of data. This data can be present in both structured and unstructured formats. Anybody who works on this data or deals with it in some way or the other to process, analyze, or make sense of such massive amounts of data is a Big Data Professional. Whereas, Data Scientists are basically given the task of investigating complex problems. They apply their knowledge of mathematics and statistical principles in conjunction with computer science algorithms to arrive at answers. Such areas not only represent their knowledge but also portray that they are the most proficient Scientists of data.
Is data science worth learning?
Data Science is an ever-growing industry with a lot of scopes so yes, it's worth learning. Data Scientists should have strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge
About the Author
Rashmi Karan
Manager - Content
Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
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