edX courses are taught by some of the top-ranked universities and industry-leading companies in the world. edX has a wide range of Data Science and Machine Learning courses too. So, if you are looking for the Best edX Courses for Data Science and Machine Learning, this article is for you. In this article, you will find the 25 Best edX Courses for Data Science and Machine Learning.
Now without further ado, let’s get started with Best edX Courses for Data Science and Machine Learning–
Best edX Courses for Data Science and Machine Learning
- 1. Professional Certificate in Data Science
- 2. MicroMasters® Program inData Science
- 3. Fundamentals of Google AI for Web-Based Machine Learning
- 4. Professional Certificate in Deep Learning
- 5. MicroMasters® Program in Statistics and Data Science
- 6. The Math of Data Science: Linear Algebra
- 7. Linear Algebra – Foundations to Frontiers
- 8. Probability and Statistics in Data Science using Python
- 9. Fundamentals of Statistics
- 10. Probability – The Science of Uncertainty and Data
- 11. Data Science: R Basics
- 12. Machine Learning for Data Science and Analytics
- 13. PyTorch Basics for Machine Learning
- 14. Deep Learning with Python and PyTorch
- 15. Python Basics for Data Science
- 16. Introduction to Data Science
- 17. Deep Learning with Tensorflow
- 18. Introducing Text Analytics
- 19. Text Analytics 2: Visualizing Natural Language Processing
- 20. Introduction to Artificial Intelligence with Python
- 21. Deep Learning Fundamentals with Keras
- 22. Introduction to Deep Learning
- 23. Data Science: Visualization
- 24. Predictive Analytics using Python
- 25. Fundamentals of Data Visualization with Power BI
- Conclusion
The first course in the list of Best edX Courses for Data Science and Machine Learning is-
1. Professional Certificate in Data Science
Provider- Harvard University
Time to Complete- 1 year 5 months ( If you spend 3 hours per week)
In this program, you will learn probability, inference, regression, and machine learning. Along with this, you will learn an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git, and GitHub, and reproducible document preparation with RStudio.
Each course contains case studies such as Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.
Along with this, you will also learn R, statistical concepts, and data analysis techniques. There are 9 courses in this program. Now let’s see the course details-
Course Details-
- Data Science: R Basics
- Data Science: Visualization
- Data Science: Probability
- Data Science: Inference & Modeling
- Data Science: Productivity Tools
- Data Science: Wrangling
- Data Science: Linear Regression
- Data Science: Machine Learning
- Data Science: Capstone
Extra Benefits-
- You will get a Shareable Certificate.
Who Should Enroll?
- Anyone can enroll. There is no prerequisite.
Interested to Enroll?
If yes, then check out the program details here- Professional Certificate in Data Science
2. MicroMasters® Program inData Science
Provider- UCSanDiego
Time to Complete- 10 months (9-11 hours per week)
In this program, you will learn the mathematical and computational tools that form the basis of data science. You will also learn how to use those tools to make data-driven business recommendations.
This program has two sides to data science learning- mathematical and applied.
In the mathematics course, you will learn probability, statistics, and machine learning. And in applied, you will get to know about Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment, and Apache Spark.
This program has 4 courses. Now let’s see the details of the courses-
Courses Include-
- Python for Data Science
- Probability & Statistics in Data Science using Python
- Machine Learning Fundamentals
- Big data analytics using Spark
Extra Benefits-
- You will get a Shareable Certificate.
Who Should Enroll?
- Those who are familiar with programming languages and have a basic understanding of high-school-level math.
Interested to Enroll?
If yes, then check out the program details here- MicroMasters® Program inData Science
3. Fundamentals of Google AI for Web-Based Machine Learning
Provider- Google
Time to Complete- 2 months
In this program, you will learn how machine learning works and how ML, AI, and deep learning all fit together. This program will provide an introduction and overview of the TensorFlow.js library and the advantages of using ML in JavaScript.
After that, you will learn ways to consume existing machine learning models and how to write custom models from a blank canvas (Linear Regression, Convolutional Neural Network).
In the end, you will understand how to use industry-standard pre-made models for object detection or natural language processing and how to convert Python models to TensorFlow.js format to run them client-side in a web browser.
Course Syllabus-
- Google AI for Anyone
- Google AI for JavaScript developers with TensorFlow.js
Extra Benefits-
- You will get a Shareable Certificate.
Who Should Enroll?
- Anyone can enroll.
Interested to Enroll?
If yes, then check out the program details here- Fundamentals of Google AI for Web-Based Machine Learning
4. Professional Certificate in Deep Learning
Provider- IBM
Time to Complete- 8 months(If you spend 2 – 4 hours per week)
This is a professional certificate program for Deep Learning offered by IBM. In this program, you will learn the fundamentals of deep learning and learn how to build, train, and deploy different types of Deep learning algorithms such as Convolutional Networks, Recurrent Networks, and Autoencoders.
You will use Python libraries like Keras, PyTorch, and Tensorflow and work on hands-on labs, assignments, and projects. There are 6 courses in this professional certificate. Now, let’s see all the 6 courses of this Specialization Program-
Courses Include-
- Deep Learning Fundamentals with Keras
- PyTorch Basics for Machine Learning
- Deep Learning with Python and PyTorch
- Deep Learning with Tensorflow
- Using GPUs to Scale and Speed up Deep Learning
- Applied Deep Learning Capstone Project
Extra Benefits-
- You will get a Shareable Certificate.
Who Should Enroll?
- Those who have a basic understanding of Python and machine learning.
Interested to Enroll?
If yes, then check here- Professional Certificate in Deep Learning
5. MicroMasters® Program in Statistics and Data Science
Provider- MITx
Time to Complete- 1 year 2 months
This program will help you to master the foundations of data science, statistics, and machine learning. You will analyze big data and make data-driven predictions through probabilistic modeling and statistical inference and identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making.
You will also develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data and learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks.
There are 6 courses in this program. Now, let’s see all the 6 courses of this Specialization Program-
Courses Include-
- Probability – The Science of Uncertainty and Data
- Fundamentals of Statistics
- Machine Learning with Python: from Linear Models to Deep Learning
- Capstone Exam in Statistics and Data Science
- Data Analysis in Social Science—Assessing Your Knowledge
- Data Analysis: Statistical Modeling and Computation in Applications
Extra Benefits-
- You will get a Shareable Certificate.
Who Should Enroll?
- There is no prerequisite to enrolling in this program.
Interested to Enroll?
If yes, then check out the program details here- MicroMasters® Program in Statistics and Data Science
6. The Math of Data Science: Linear Algebra
Provider- RICE University
Time to Complete- 8 Weeks( If you spend 6-8 hours per week)
This is another best course to learn Linear algebra for data science. In this course, you will learn relationships between linear equations, matrices, linear transformations, the significance of the basis and dimension of a vector space, etc.
This course will not only teach theoretical concepts but also teach how to use linear algebra to solve real-world problems. This Linear Algebra course also covers some advanced concepts of linear algebra such as basis and dimension.
Extra Benefits-
- You will get a Shareable Certificate upon completion.
Who Should Enroll?
- Those who studied High school algebra.
Interested to Enroll?
If yes, then check out all details here- The Math of Data Science: Linear Algebra
7. Linear Algebra – Foundations to Frontiers
Provider- UTAustinX
Time to Complete- 15 Weeks( If you spend 6-10 hours per week)
This is another best course to learn linear algebra concepts. In this course, you will learn Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, Eigenvalues, and Eigenvectors, etc.
Along with this, you will also get to know about the research on the development of linear algebra libraries. This course was taught by Professor Robert van de Geijn, an expert on high-performance linear algebra libraries.
Extra Benefits-
- You will get a Shareable Certificate upon completion.
- You will get a MATLAB license throughout this course free of charge.
Who Should Enroll?
- Those who have studied High School Algebra, Geometry, and Pre-Calculus.
Interested to Enroll?
If yes, then check out all details here- Linear Algebra – Foundations to Frontiers
8. Probability and Statistics in Data Science using Python
Provider– UCSanDiego
Time to Complete- 10 Weeks
This course covers both the mathematical theory, and get the hands-on experience of applying this theory to actual data using Jupyter notebooks. In this course, you will learn about random variables, dependence, correlation, regression, PCA, entropy, and MDL.
Who Should Enroll?
- Those who have a good understanding of Python and have basic knowledge of maths.
Interested to Enroll?
If yes, then check out all details here- Probability and Statistics in Data Science using Python
9. Fundamentals of Statistics
Provider- MITx
Time to Complete- 18 Weeks
This is an advanced-level course, that teaches core ideas on firm mathematical grounds that begin with the construction of estimators and tests, as well as an analysis of their asymptotic performance.
In this course, you will learn how to make predictions using linear, nonlinear, and generalized linear models, how to perform dimension reduction using principal component analysis (PCA), how to choose between different models using the goodness of fit test, etc.
Who Should Enroll?
- Those who are familiar with vectors and matrices and have college-level single and multi-variable calculus knowledge.
Interested to Enroll?
If yes, then check out all details here- Fundamentals of Statistics
10. Probability – The Science of Uncertainty and Data
Provider- MITx
Time to Complete- 16 Weeks
This is one of the most demanding courses for probability. In this course, you will learn multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, Bayesian inference methods, Poisson processes, and Markov chains.
Who Should Enroll?
- Those who know college-level calculus, are comfortable with mathematical reasoning, and familiar with sequences, limits, infinite series, the chain rule, and ordinary or multiple integrals.
Interested to Enroll?
If yes, then check out all details here- Probability – The Science of Uncertainty and Data
11. Data Science: R Basics
Provider- Harvard University
Time to Complete- 8 weeks
In this course, you will learn R’s functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting.
You will develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.
Who Should Enroll?
- Those are a beginner and want to learn R Programming for Data Science.
Interested to Enroll?
If yes, then check out all details here- Data Science: R Basics
12. Machine Learning for Data Science and Analytics
Provider- Columbia University
Time to Complete- 5 weeks
This course will provide an introduction to machine learning and algorithms. After that, you will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics.
You will also learn how to prepare data, deal with missing data and create custom data analysis solutions for different industries and basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms, and dynamic programming.
Who Should Enroll?
- Those who know high school-level math.
Interested to Enroll?
If yes, then check out all details here- Machine Learning for Data Science and Analytics
13. PyTorch Basics for Machine Learning
Provider- IBM
Time to Complete- 5 Weeks
This course will teach you the fundamentals of Pytorch such as PyTorch’s tensors, tensor types, operations, PyTorchs Automatic Differentiation package and integration with Pandas and Numpy, etc.
In this course, you will also learn how to train a linear regression model, and how to make a prediction using PyTorch’s linear class and custom modules.
In short, this is the perfect course for those who want to gain an in-depth understanding of PyTorch basics. There are 5 modules in the course.
Extra Benefits-
- You will get a Certificate of completion.
Who Should Enroll?
- Those who have previous knowledge in Python Programming.
Interested to Enroll?
If yes, then check out the program details here- PyTorch Basics for Machine Learning
14. Deep Learning with Python and PyTorch
Provider- IBM
Time to Complete- 6 Weeks
After gaining PyTorch basics from PyTorch Basics for Machine Learning course, this course will teach you how to build deep neural networks in PyTorch. And how to apply methods such as dropout, initialization, different types of optimizers, and batch normalization.
Then you will learn Convolutional Neural Networks, how to train the model on a GPU, Transfer Learning, dimensionality reduction techniques, and autoencoders. Throughout this course, you will learn from expert instructors of IBM.
Extra Benefits-
- You will get a Certificate of completion.
Who Should Enroll?
- Those who have previous knowledge in Python Programming and familiar with machine learning concepts.
Interested to Enroll?
If yes, then check out the program details here- Deep Learning with Python and PyTorch
15. Python Basics for Data Science
Provider- IBM
Time to Complete- 5 Weeks
This is a beginner-friendly free course to learn Python for data science. In this course, you will learn the Python basics (how to define variables in Python, Sets, conditional statements, and functions), how to operate on files to read and write data in Python, and how to use pandas for data analysis in Python.
Interested to Enroll?
If yes, then check out all details here- Python Basics for Data Science.
16. Introduction to Data Science
Provider- IBM
Time to Complete- 6 Weeks
This free course is good for beginners to understand the basics of data science such as tools and algorithms used daily, skills needed to be a successful data scientist, the role of data science within a business, etc.
Interested to Enroll?
If yes, then check out all details here- Introduction to Data Science
17. Deep Learning with Tensorflow
Provider- IBM
Time to Complete- 5 weeks
The course material of this course is available freely. But for the certificate, you have to pay. In this course, you will learn the foundational TensorFlow concepts such as the main functions, operations, and execution pipelines.
This course will also teach how to use TensorFlow in curve fitting, regression, classification, and minimization of error functions. You will understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
Interested to Enroll?
If yes, then check out all details here- Deep Learning with Tensorflow
18. Introducing Text Analytics
Provider- University of Canterbury
Time to Complete- 6 weeks
In this course, you will learn the core techniques of natural language processing (NLP) and computational linguistics. This is not a theoretical course. In this course, you will learn how it works and why it works at the same time.
You will also learn to use Python packages like pandas, scikit-learn, and TensorFlow. This course will also cover the following topics- text processing, text mining, sentiment analysis, and topic modeling.
Who Should Enroll?
- Those who are beginners and want to learn NLP basics.
Interested to Enroll?
If yes, then enroll here- Introducing Text Analytics
19. Text Analytics 2: Visualizing Natural Language Processing
Provider- University of Canterbury
Time to Complete- 6 weeks
This is a practical course and teaches how to visualize and interpret the output of text analytics and how to create visualizations ranging from word clouds, heatmaps, and line plots to distribution plots, choropleth maps, and facet grids.
This course is good to extend your knowledge of the core techniques of computational linguistics by working through case studies and visualizing their results.
Who Should Enroll?
- Those who are beginners and want to learn NLP basics.
Interested to Enroll?
If yes, then enroll here- Text Analytics 2: Visualizing Natural Language Processing
20. Introduction to Artificial Intelligence with Python
Provider- Harvard University
Time to Complete- 7 weeks
In this course, you will learn concepts and algorithms of artificial intelligence and dive into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation.
Throughout the course, you will work on hands-on projects to gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics.
You will also learn reinforcement learning, neural networks, and natural language processing.
Who Should Enroll?
- Those who have prior programming experience in Python.
Interested to Enroll?
If yes, then start learning here- Introduction to Artificial Intelligence with Python
21. Deep Learning Fundamentals with Keras
Provider- IBM
Time to Complete- 5 Weeks
In this course, you will learn the basics of deep learning and how to build your first deep learning model using Keras. This course will teach supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
The course material of this course is available free, but for a certificate, you have to pay. In this course, you will also learn how neural networks learn and what are activation functions.
Who Should Enroll?
- Those who are comfortable in Python programming and Machine Learning.
Interested to Enroll?
If yes, then start learning- Deep Learning Fundamentals with Keras
22. Introduction to Deep Learning
Provider- Université de Montréal
Time to Complete- 16 weeks
The course material of this course is freely available, but for a certificate, you have to pay. Which I think is not required. In this course, you will learn the fundamental concepts of deep learning.
You will learn the types of neural networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, Bias, and Discrimination in Machine Learning.
Who Should Enroll?
- Those who have programming knowledge and mathematics (linear algebra, statistics) knowledge.
Interested to Enroll?
If yes, then start learning- Introduction to Deep Learning
23. Data Science: Visualization
Provider- Harvard University
Time to Complete– 8 weeks
This course is Free to audit. In the free mode, you will get access to course materials but not certificates and graded assignments and exams.
In this course, you will learn the basics of data visualization and exploratory data analysis. For data visualization, the R programming package, ggplot2 is used. This course will also teach you how to use ggplot2 to create custom plots, data visualization principles, etc.
You Should Enroll if-
- You have basic knowledge of R programming.
Interested to Enroll?
If yes, then start learning- Data Science: Visualization
The next course in the list of Best edX Courses for Data Science and Machine Learning is-
24. Predictive Analytics using Python
Provider- The University of Edinburgh
Time to Complete- 8 months (If you spend 8 – 10 hours per week)
This is a Micro Masters program with 5 courses available on edX. In this program, you will learn how to prepare data for predictive modeling, data mining, and advanced analytics using Python programming. Throughout this course, you will apply a wide range of statistical and machine learning methodologies to real-life datasets.
Extra Benefits-
- You will get a Certificate of completion.
Who Should Enroll?
- Those who have an undergraduate-level experience in mathematics, statistics, or programming (Java, C, Python, Visual Basic).
Interested to Enroll?
If yes, then check out the details here- Predictive Analytics using Python
25. Fundamentals of Data Visualization with Power BI
Provider- DavidsonX
Time to Complete- 2 months ( If you spend 8-15 hours per week)
This is a professional certificate where you will understand the data lifecycle (analyzing, manipulating, and visualizing data) using R programming. Then you will apply what you have learned using PowerBI.
In this certificate program, you will work on business-related datasets and exercises and these exercises will make you more confident in working with data, creating data visualizations, and preparing reports and dashboards in PowerBI.
In this certificate program, there are two courses-
- Analyzing and Visualizing Data with Power BI
- Essentials of Data Literacy
Who Should Enroll?
- Those who are beginners and want to learn Power BI.
Interested to Enroll?
If yes, then check out all details here- Fundamentals of Data Visualization with Power BI
And here the list ends. I hope these 25 Best edX Courses for Data Science and Machine Learning will help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.
Conclusion
In this article, I tried to cover the 25 Best edX Courses for Data Science and Machine Learning. If you have any doubts or questions, feel free to ask me in the comment section.
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Though of the Day…
‘ It’s what you learn after you know it all that counts.’
– John Wooden
Written By Aqsa Zafar
Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.