Introduction to Data Science books and courses

    I was asked about books and courses that will help to get started with learning Data Science (Data Mining, Machine Learning or Data Analysis).
   My main toolchain is Python, NumPy/SciPy/Pandas/Scikit-learn, Hadoop and MRJob. Based on this I put together a list of books that will good to start with:

Python

Learning Python. Mark Lutz.

http://www.amazon.com/Learning-Python-Mark-Lutz/dp/1449355730/

Book to learn Python before jumping to data science. 

Python for Data Analysis. Wes McKinney.
http://www.amazon.com/books/dp/1449319793

Book from the author of pandas module. Great book to learn how to do descriptive stats with Python.

Programming Collective Intelligence. Toby Segaran.

http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325

Introduction to self written Machine learning algorithms with Python.

Machine Learning in Action. Peter Harrington.

http://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181

k-Nearest neighbors, naive Bayes, SVM, decision trees with examples in Python

Hadoop

Hadoop: Definitive Guide. Tom White.
http://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/0596521979

Definitive guide from one of the early contributors to Hadoop source code and person with wast experience working with it.

R & Stats

Data Analysis with Open Source Tools. Phillipp K. Janert.

http://www.amazon.com/Data-Analysis-Source-Tools-ebook/dp/B004FGMTYA

Sometimes Python is just not enough and this book will help to start working with R.

Think stats. Allen B. Downey.

http://www.amazon.com/dp/1449307116

If you are coming from Computer Science major you better get this book about probability theory and stats.

Good read on Data Science

Predictive Analytics Power Predict. Eric Siegel.

http://www.amazon.com/Predictive-Analytics-Power-Predict-ebook/dp/B00BGC2WGQ/

Good read on Predictive Analytics philosophy and examples of real world tasks that people solved with it.

Courses

• Introduction to Data Science - Good introduction to all main concepts that data scientist should know (SQL, NoSQL, Hadoop, R, Machine learning algorithms and visualization and etc).
• Computing for Data Analysis - Course about learning R and solving real problems with it.
• Machine Learning - Basics of Machine learning from Andrew Ng (Founder of Coursera and Director of AI Lab in Stanford).
• Computational Investment - Course that will teach how building a trade-robot for stock exchange in Python using all the tools that Data Scientist uses (see as practical examples).

    This list of books and courses will be updated when I'll find something worth reading or watching on this topic. If somebody knows a good book that I should add to this list - please, let me know.