Data science collection books all of which are free. However, I have listed a few optional books that will provide additional context for those who are interested.
Introduction to Statistical Learning (Free online PDF) This book is a great reference for the machine learning and some of the statistics material in the class
Data Science from Scratch (Available as eBook for Berkeley students) This more applied book covers many of the topics in this class using Python but doesn’t go into sufficient depth for some of the more mathematical material.
Storytelling With Data: A Data Visualization Guide for Business Professionals
“Storytelling With Data” is designed to help readers build effective data-driven narratives. Cole Nussbaumer Knaflic, the author of the popular blog StorytellingWithData.com, explains approaches to getting rid of unnecessary data that obscures clear communication, converting complicated information into a concise summary, and using design principles to create impactful data visualizations.
R for Data Science Great resource by Hadley Wickham Chief scientist at RStudio.
Related to Statistics: Classic Statistical Learning books:
Related to Information Retrieval:
Introduction to Information Retrieval : Very important book to understand web crawling, data collection, data storage, feature engineering and Text analysis.
Mining of Massive Datasets : If you want to be a data scientist you will run into problem of computation. This is very important resource to understand how to process massive datasets.
To Learn Data Mining and Machine Learning Techniques:
Moving forward to Machine Learning (ML) and Deep Learning (DL):
ML and DL:
Deep Learning: Ian Goodfellow
Java based DL: Deeplearning4j Documentation
TensorFlow: Tutorials | TensorFlow :
Interviews with Data Scientists
Build a Data Science Team