Learning Resources
Data Science is an interdisciplinary field that blends elements of Statistics, Computer Science and Mathematics. On this page I am sharing some learning resources that I found useful: I hope this can help other learners to get started. Most of those resources are even freely available.
Computer Programming
When it comes to coding for data science, the choice of programming languages boils down to either Python or R. My current focus is on Python and here are some resources that I can recommend.
Free Books

Think Python
by Allen B. Downey
Green Tea Press
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This book is one of the best introduction to Python programming for complete beginners that I’ve found. The basic concepts are explained very clearly, and there’s plenty of programming exercises for practice. Available in PDF and HTML on the Green Tea Press website. Check out the other free titles from the same author (Think Bayes, Think Stats, Think Java and more). 
Python for Everybody
by Charles Severance
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Another excellent introduction for people new to programming. It serves a textbook for courses available on Coursera, edX, FutureLearn and freeCodeCamp. I didn’t actually use this book when I had to learn Python from scratch since I found out only recently, but I think it’s worth adding to the list. It can be downloaded from the author’s website. Also available online on Trinket. 
Automate the boring stuff with Python
by Al Sweigart
No Starch Press
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This is a goto book after you’ve mastered the basics of the language. It offers coding practice with interesting examples like sending email, manipulating files, web scraping, automating spreadsheets and image processing. There’s a free HTML version on the book’s website: automatetheboringstuff.com. More free Python books from the same author on: inventwithpython.com.
Other Books (nonfree)

Python for Data Analysis
by Wes McKinney
2017, O’Reilly
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The pandas library is what makes Python a data analysis powerhouse and this title is written by the creator of pandas himself.
I used the second edition of this book. However, in my opinion, it’s already outdated: that’s how fast this library has evolved since the book’s publication. In any case, I still have to find a better resource to learn pandas in detail. A third edition is on the way, and I would wait for that if I had to purchase a copy now. 
Learning SQL: Generate, Manipulate, and Retrieve Data
by Alan Beaulieu
2020, O’Reilly
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Data scientist are often required to use SQL to handle large datasets.
This text is not for absolute beginners, but it’s still one of the best resources to master the power of SQL. I used the second edition of this book to to get my grips on SQL years ago, and I keep going back to it when I need to refresh my knowledge.
Python MOOCs
There are a several online courses that can help to set some foundations of coding with Python if you, like me, don’t come from a computing background. Here are some that I liked most.
The next two courses are part of the Computational Thinking using Python program by MIT on edX:
Another good one:
 Using Python for Research
by Harvard University on edX.
Statistics
Free Books

Introduction to Modern Statistics
by Mine ÇetinkayaRundel and Johanna Hardin 
OpenIntro Statistics
by David Diez, Mine ÇetinkayaRundel and Christopher Barr
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Both books can be downladed for free from OpenIntro.
Mathematics
Free Books

Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
2020, Cambridge University Press
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Not a text for beginners, since it requires some solid foundations of Calculus and Linear Algebra. It can be downloaded for free on mmlbook.github.io/. 
Introduction to Probability for Data Science
by Stanley Chan
2021, Michigan Publishing
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Read for free on probability4datascience.com/, or download the free PDF from Michigan Publishing.
Machine Learning
Free Books
 An Introduction to Statistical Learning (with applications in R)
by G. James, D. Witten, T. Hastie, R. Tibshirani
Springer Verlag
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One of the best books around on Statistical Learning. It can be downloaded for free onfrom its web page www.statlearning.com or from trevorhastie.github.io/ISLR/.
Other Books (nonfree)
 Handson Machine Learning with ScikitLearn, Keras, and TensorFlow
by Aurélien Géron
2019, O’Reilly
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At the time of writing, I have just started with this title: I wish I had done this earlier! Beautifully and clearly written, it covers the most relevant Machine Learning topics from an applied point of view with just the right amount of theory.
Free Online Learning

Andrew Ng’s Machine Learning course on Coursera
Probably the most popular MOOCs on machine learning.
Data Science Online Learning Platforms

Dataquest
So far, my favourite learning platform. It does not use videos, rather just clearly written text and exercises, which I pefer. The link above contains a referral link, it should give you $15 off if you sign up.