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Skilled data professionals continue to be in high demand. So it's a good time to get into data science. But how and where to start?
Should you enroll in 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 successfully change careers.
To help you get started, we've compiled a list of free, high-quality college courses that will help you learn data science from scratch. Because these courses have a structured curriculum, you don't have to worry about what to learn and in what order, and just focus on learning and improving.
Let us begin!
If you need a refresher on Python programming before you start learning data science, check out CS50 Introduction to Python Programming He taught at Harvard University.
After learning the fundamentals of programming with Python, you can refer to this Introduction to data science with Python Of course, also from Harvard.
In this course, you will learn the following topics:
- Programming Basics
- Using Python for data coding, statistics, and storytelling
- Python data science libraries such as NumPy, pandas, matplotlib, and scikit-learn
- Creation and evaluation of 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 the fundamentals of data science. This course will help you become familiar with data science and essential statistical concepts.
Here is an overview of what this course covers:
- Optimization problems
- stochastic thinking
- random walks
- Monte Carlo simulation
- Confidence intervals
- Understand experimental data
- Group
- Classification
Course link: Introduction to Computational Thinking and Data Science
Statistical learning from Sanford University is another popular course to learn how different machine learning algorithms work.
The programming exercises in this course are in R. But you can also do them using Python. I also suggest that you use the Python edition of the book Introduction to Statistical Learning (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 this course covers.
Course link: Statistical learning
Even if you are familiar with building machine learning models using Python and Python libraries like scikit-learn, you also need to understand certain mathematical concepts.
Learning mathematical concepts will be helpful if you ever want to get into machine learning research and will also give you an advantage in technical interviews. This is an important learning that will help you get the edge and give you an advantage in the technical interview.
He Data Science Mathematics Topics This MIT course will teach you certain mathematical topics related to data science. Specifically, advanced concepts of dimensionality reduction and clustering.
These are some of the topics you will learn:
- Principal component analysis
- Spectral grouping
- Compressed sensing
- Approximation algorithms
Course link: Data Science Mathematics Topics
From one or more of the courses we've looked at so far, you should feel comfortable with:
- Python Data Science Libraries
- Operation of machine learning algorithms.
He Data Science: Machine Learning The Harvard course will help you review the basics of machine learning and apply them to create a recommender system.
So this course teaches you:
- Machine Learning Basics
- Cross validation
- Popular machine learning algorithms
- Regularization techniques
- Building a recommendation system
Course link: Data Science: Machine Learning
Now you 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 one that best suits your needs. Happy learning!
Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more.
