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:
Learning Python. Mark Lutz.
Book to learn Python before jumping to data science.
Python for Data Analysis. Wes McKinney.
Book from the author of pandas module. Great book to learn how to do descriptive stats with Python.
Programming Collective Intelligence. Toby Segaran.
Introduction to self written Machine learning algorithms with Python.
Machine Learning in Action. Peter Harrington.
k-Nearest neighbors, naive Bayes, SVM, decision trees with examples in Python
Hadoop: Definitive Guide. Tom White.
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.
Sometimes Python is just not enough and this book will help to start working with R.
Think stats. Allen B. Downey.
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.
Good read on Predictive Analytics philosophy and examples of real world tasks that people solved with it.
- 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.