Must-reads to Learn Data Science: September 2019

. 1 min read

Our must-reads series is a curated list of resources for the data science enthusiast and learner. Follow us for more recommendations that you absolutely cannot miss.  

Book:

An Introduction to Statistical Learning

If you were to ask me - what is the one book you would recommend learning data science (and machine learning, in particular) my answer would be unwavering - An Introduction to Statistical Learning by Gareth James et. al.

Very concisely written with extreme clarity, whenever I face any doubts, this is my first go-to reference. I personally like explanations when they are in the context of problem-solving and this is what the book sets out to do. At times, it could get mathematically heavy but if you are a little patient, clarity will soon follow. And as a bonus, there are labs with code at the end of every chapter for practice. Lots of supplementary tutorials are written around this book which makes it an excellent resource for reference.

Article:

What’s the difference between analytics and statistics?

Cassie Kozyrkov is one of the persons I follow on Medium and she writes really great and thought-provoking articles. Her recent article throws light on the differences between analytics and statistics. I like the points that she makes and how she explains the thin difference between statistics and analytics. She addresses some of the misconceptions in the role of an analyst (and overall about data analysis). Do read this blog post and definitely follow her for insightful articles.

Have recommendations of your own? Tweet to us, or leave us a comment on Facebook.



Get Started - Future proof your career

Join 150,000 aspirants. Learn Today - Apply Today. Try Free Programs

Learn Data Science Free with GLabs