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Gold Blog38 Free Courses on Coursera for Data Science


There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.



By Aqsa Zafar, Ph.D. Scholar in Machine Learning | Founder at MLTUT | Solopreneur | Blogger.

Coursera is an E-Learning platform and has a wide range of Free Data Science Courses. That’s why in this article, I am going to share with you Free Courses on Coursera for Data Science.

For these courses, You don’t need to pay a single buck. So take a few minutes to check out these Free Courses on Coursera for Data Science.

I divided these courses based on the knowledge level of Beginner, Intermediate, and Advanced. You can find the course according to your knowledge level.

Now without any further ado, let’s get started.

Free Courses on Coursera for Data Science

For your convenience, I created a table from where you can review the course according to Rating, Time to Complete, and Provider.

 

Beginner-Level

 

S/N Course Name Rating Time to Complete Provider
1. Introduction to Statistics 4.5/5 15 hours Stanford University
2. Machine Learning 4.9/5 61 hours Stanford University
3. Foundations of Data Science: K-Means Clustering in Python 4.6/5 29 hours University of London
4. Data Analytics for Lean Six Sigma 4.8/5 11 hours University of Amsterdam
5. Computational Neuroscience 4.6/5 26 hours University of Washington
6. An Intuitive Introduction to Probability 4.7/5 30 hours University of Zurich
7. Probability and Statistics: To p or not to p? 4.6/5 16 hours University of London
8. Data Science Ethics 4.8/5 15 hours University of Michigan
9. Excel Basics for Data Visualizations 4.1/5 1 hour Coursera community
10. Predicting heart disease using Machine Learning 4.1/5 50 minutes Coursera community
11. Exploratory Time Series Analysis 4.2/5 82 minutes Coursera community
12. Practical Crowdsourcing for Efficient Machine Learning 3.0/5 17 hours Yandex
13. Population Health: Responsible Data Analysis 4.6/5 21 hours Leiden University

 

Intermediate-Level

 

S/N Course Name Rating Time to Complete Provider
14. Process Mining: Data science in Action 4.7/5 22 hours Eindhoven University of Technology
15. Bayesian Statistics: From Concept to Data Analysis 4.6/5 12 hours UC Santa Cruz
16. Practical Time Series Analysis 4.6/5 26 hours The State University of New York
17. Introduction to Embedded Machine Learning 4.8/5 17 hours Edge Impulse
18. Improving your statistical inferences 4.9/5 28 hours Eindhoven University of Technology
19. Data Science in Stratified Healthcare and Precision Medicine 4.6/5 17 hours University of Edinburgh
20. Machine Translation 4.5/5 27 hours Karlsruhe Institute of Technology
21. Computer Vision with Embedded Machine Learning NA 31 hours Edge Impulse
22. Experimentation for Improvement 4.8/5 13 hours  McMaster University
23. Power and Sample Size for Multilevel and Longitudinal Study Designs 4.4/5 24 hours University of Florida
24. Improving Your Statistical Questions 4.9/5 18 hours Eindhoven University of Technology
25. Population Health: Predictive Analytics 5.0/5 18 hours Leiden University
26. Global Statistics – Composite Indices for International Comparisons 4.6/5 16 hours University of Geneva
27. Brain Tumor Classification Using Keras 4.5/5 2 hours Coursera community
28. Basic Data Analysis and Model Building using Python 3.6/5 1.5 hours Coursera community
29. Forecasting Univariate Time Series with an LSTM 3.5/5 2 hours Coursera community
30. Predicting Wine Quality with Random Forest and Scikit-Learn NA 2.5 hours Coursera community
31. Analyzing WhatsApp Chat Data 4.1/5 2 hours Coursera community
32. Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory 4.4/5 2 hours Coursera community
33. Hands-on Text Mining and Analytics 3.9/5 13 hours Yonsei University
34. Regression using Scikit-Learn 3.4/5 1.5 hours Coursera community

 

Advanced-Level

 

S/N Course Name Rating Time to Complete Provider
35. Causal Inference 3.3/5 12 hours Columbia University
36. Causal Inference 2 3.4/5 6 hours Columbia University
37. Data Science for Business with R Programming 4.9/5 2 hours Coursera community
38. Segment your market using factor analysis with R programming NA 2 hours Coursera community

 

Original. Reposted with permission.

 

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