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Become a Pro
There are a plethora of different data science resources available these days. Many of these claim to be the best proverbial “introductory to advanced courseware and material of data science.” I have definitely made my fair share of mistakes in picking data science references, but this article will be going into the things I’ve learned throughout my experience in becoming effective in this subject. The following list is both practical and born out of the experience of reading through them one by one.

Data Science Books

Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville
I would not suggest reading many books from a data scientist professional, but this one is authored by someone with 15 years of authority in the field of data science. He worked on some genuinely large-scale projects with some of the top firms across the globe. This book by itself holds some of the best and latest way of achieving what you’ll need to become a professional data scientist. It is not just teaching theory or ideas.

Each chapter incorporates various case studies from his experiences in the field. Vincent Granville has global recognition for being one of the best-known data science resource talents. The reading is a bit advanced and it’s not advised for novices. This is still the best book for intermediate-advanced and professional data scientists. If you’re looking to know exactly how to serve as a data scientist professionally, then this is the book for you. This is just for advanced, intermediate and professional data scientists as you’ll want to know the fundamentals before diving into this book.

Introduction to Machine Learning with Python – A Guide for Data Scientists
This is the perfect work for beginners who have only a working knowledge of pandas, numpy or matplotlib. This is probably the most efficient way of learning the data science library of Scikit-Learn because the authors are a couple of the core developers of the open source scikit-learn package. They understand the library literally inside and out because they’ve both heavily contributed to building it!

The descriptions are manageable and the time you’ll spend working on the source code and exercises within the book are going be highly advantageous if you’re looking to master scikit-learn and its related libraries.