Journey to Data Scientist: Interviews with More Than Twenty Amazing Data Scientists – When author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than twenty interviews, she asks leading data scientists questions about starting in the field and the future of the industry.
Learn Python the Hard Way – Newly updated for Python 3, the original and still the most popular way for total beginners to finally learn how to code. Learn Python The Hard Way takes you from absolute zero to able to read and write basic Python to then understand other books on Python.
O’Reilly Free Data Science Library – This library compiles the best data insights from O’Reilly editors, authors, and Strata speakers for you in one place, so you can dive deep into the latest of what’s happening in data science and big data.
Bayesian Reasoning and Machine Learning – The book targets students with backgrounds in computer science, engineering, applied statistics, physics, and bioinformatics that want to gain knowledge of Machine Learning. The author introduces fundamental concepts in inference using laymans terms and a low level of algebra and calculus.
Guide to Data Mining – This free book takes a learning-by-doing approach to explain basic data mining techniques. Guide to Data Mining introduces practical data mining, collective intelligence, and building recommendation systems.
Interpretable Machine Learning – This online book is about making machine learning models and their decisions interpretable. It’s suitable for machine learning practitioners, data scientists, statisticians and anyone else interested in making machine decisions more human.
The Data Science Handbook – The Data Science Handbook is a compilation of thorough interviews with 25 accomplished data scientists with their insights, stories, and advice. While this book isn’t a tutorial on data science topics, it gives practical career insight into a variety of industries.
Art of Data Science – This book describes the process of analyzing data. The authors have developed backgrounds in managing data analysts as well as conducting their own data analyses.
The Data Analytics Handbook – This Handbook takes an in-depth look at the data science industry through interviews with data scientists, data analysts, CEOs, managers, and researchers at the cutting edge of the data science industry.
Numsense! Data Science for the Layman: No Math Added – As the title implies, this book breaks down data science for people of all backgrounds, leaving out the quantitative jargon. Aspiring students, enterprising business professionals, or other eager learners can find tutorials and easy to understand explanations.
D3 Tips and Tricks v4.x – This books includes tips and tricks for using d3.js (version 4), one of the leading data visualization tools for the web. It’s aimed at getting you started and moving you forward.
Data Mining Algorithms in R – Those who know the programming language R and wish to learn more about data mining will benefit from this WikiBook. Understanding how the algorithms work will help grow your understanding of data mining.
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition – Learn to use data mining for marketing or sales purposes. You’ll pick up advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk.
