Courses to practice crucial Data Science skills.
These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a
learning community, keeping you accountable and on the path.
Our experts recommend these courses, from free
selections to paid programs.
Machine Learning A-Z™
Kirill Eremenko's course on Udemy is a classic with almost a million (!) students worldwide. A-Z takes you from a bit of coding knowledge to making your own predictions and building ML models pretty swiftly. At prices between $10 - $20 it's also cheaper than many alternatives.
Lex Fridman's MIT Deep Learning
Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.
Harvard Online Data Science
To become an expert data scientist you need practice and experience. By completing this course you will get an opportunity to apply and gain knowledge in R data analysis. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
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Best books to further your Data Science understanding.
A well-written and thorough book can be an amazing path to build deeper understand and also act as a
handbook as you discover the internet's vast resources.
These are our and our experts top picks to get
started building career-relevant skills.
R for Data Science
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.
Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.
Statistics for Data Scientists
Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
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The Data Science must-reads you shouldn't miss.
Key articles and posts of industry experts can help you get a better picture of what you are getting
In our opinion, these are some must-reads you really shouldn't miss.
Simple Reinforcement Learning with Tensorflow
This 8-part series by Arthur Juliani (Deep RL researcher at Unity) is an amazing entry point to the new and mysterious advancements of Reinforcement Learning, perfectly suited for folks coming from other topics in Machine Learning.
Karpathy on "Software 2.0"
Andrej Karpathy is the Director of AI at Tesla. Before that, though, he authored this blog post in 2017 talking about Deep Learning as "Software 2.0" of some sort. A must-read if you ever want to have another way of thinking about ML.