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Skilled data professionals are continuing to be in high demand. So it’s a great time to break into data science. But how—and where—do you start?

Should you sign up for bootcamps, professional certificates, and graduate programs to learn data science? Yes, these are all good options. However, you can learn data science for free and still switch careers successfully.

To help you get started, we’ve compiled a list of free and high-quality university courses that’ll help you learn data science from the ground up. Because these courses have a structured curriculum, you don’t have to worry about what to learn and in which order—and only focus on learning and getting better.

Let’s get started!

If you need a refresher in Python programming before you start learning data science, check out CS50’s Introduction to Programming with Python taught at Harvard University.

After learning programming fundamentals with Python, you can check out this Introduction to Data Science with Python course, also from Harvard.

In this course, you’ll learn the following topics:

- Programming basics
- Using Python for coding, statistics, and data storytelling
- Python data science libraries such as NumPy, pandas, matplotlib, and scikit-learn
- Building and evaluating machine learning models
- Applications of machine learning

Course link: Introduction to Data Science with Python

Introduction to Computational Thinking and Data Science from MIT is another good course to learn data science foundations. This course will help you gain familiarity with data science and essential statistics concepts.

Here is an overview of what this course covers:

- Optimization problems
- Stochastic thinking
- Random walks
- Monte Carlo Simulation
- Confidence intervals
- Understanding experimental data
- Clustering
- Classification

Course link: Introduction to Computational Thinking and Data Science

Statistical learning from Sanford University is yet another popular course to learn how the different machine learning algorithms work.

The programming exercises in this course are in R. But you can also work through them using Python. I’ll also suggest you to use the Python edition of the Introduction to Statistical Learning book (which is also free) as a companion to this course

This course covers the following topics:

- Linear regression
- Classification
- Resampling methods
- Model selection
- Regularization
- Tree-based methods
- Support vector machines
- Unsupervised learning here are some of the topics that this course covers

Course link: Statistical Learning

Even if you’re familiar with building machine learning models using Python and Python libraries such as scikit-learn, you should understand certain math concepts as well.

Learning math concepts will be helpful if you ever want to get into machine learning research and will also give you an edge in technical interviews. This is important learning these will help you get the edge will give you an edge in technical interview

The Topics in Mathematics of Data Science course from MIT will teach you certain math topics related to data science. Specifically, advanced dimensionality reduction and clustering concepts.

Here are some of the topics you’ll learn:

- Principal component analysis
- Spectral clustering
- Compressed sensing
- Approximation algorithms

Course link: Topics in Mathematics of Data Science

From one or more of the courses we’ve seen thus far, you should be comfortable with:

- Python data science libraries
- Working of machine learning algorithms

The Data Science: Machine Learning course from Harvard will help you review machine learning basics and apply them to build a recommender system.

So this course teaches you:

- Machine learning basics
- Cross validation
- Popular machine learning algorithms
- Regularization techniques
- Building a recommender system

Course link: Data Science: Machine Learning

So you now have a list of high-quality data science courses from elite universities like Harvard, MIT, and Stanford to learn data science.

From Python data science libraries to the inner workings of machine learning algorithms, you can check out one more of these courses to find the best fit for you. Happy learning!

** Bala Priya C** is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more.