1] This sections contain lot of reference books.
2] Basically here project supported books are there
3] Machining learning for project and learning in Python.
4] Docker related books
- Advanced Data Analysis from an Elementary Point of View
- An Introduction to R - W. N. Venables, D. M. Smith, and the R Core Team
- Analyzing Linguistic Data: a practical introduction to statistics - R. H. Baayan
- Applied Data Science - Ian Langmore and Daniel Krasner -
- Concepts and Applications of Inferential Statistics - Richard Lowry
- Forecasting: Principles and Practice - Rob J. Hyndman and George Athanasopoulos
- Introduction to Probability - Charles M. Grinstead and J. Laurie Snell
- Introduction to Statistical Thought - Michael Lavine
- OpenIntro Statistics - Second Edition - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel
- simpleR - Using R for Introductory Statistics - John Verzani
- Statistics
- Think Stats: Probability and Statistics for Programmers v2.0 - Allen B. Downey
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science - Bradley Efron and Trevor Hastie
- Data Science: An Introduction - Wikibook -
- Disruptive Possibilities: How Big Data Changes Everything - Jeffrey Needham
- Introduction to Data Science - Jeffery Stanton
- Real-Time Big Data Analytics: Emerging Architecture - Mike Barlow
- The Evolution of Data Products - Mike Loukides
- The Promise and Peril of Big Data - David Bollier
- Data-Intensive Text Processing with MapReduce - Jimmy Lin and Chris Dyer
- Fundamental Numerical Methods and Data Analysis - George W. Collins
- Introduction to Metadata
- Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics - W. N. Venables, D. M. Smith, and the R Core Team
- Modeling with Data: Tools and Techniques for Scientific Computing - Ben Klemens -
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Hadley Wickham & Garrett Grolemund
- Advanced R - Hadley Wickham
- Introduction to Social Network Methods - Robert A. Hanneman and Mark Riddle
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg
- Network Science - Sarah Morrison
- The Wealth of Networks - Yochai Benkler
- Data Mining and Analysis: Fundamental Concepts and Algorithms - Mohammed J. Zaki and Wagner Meira Jr.
- Data Mining and Knowledge Discovery in Real Life Applications - Julio Ponce and Adem Karahoca -
- Data Mining for Social Network Data
- Mining of Massive Datasets - Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman
- Knowledge-Oriented Applications in Data Mining - Kimito Funatsu
- New Fundamental Technologies in Data Mining - Kimito Funatsu
- R and Data Mining: Examples and Case Studies - Yanchang Zhao
- The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- Theory and Applications for Advanced Text Mining - Shigeaki Sakurai
- A Course in Machine Learning - Hal Daume
- A First Encounter with Machine Learning - Max Welling
- Bayesian Reasoning and Machine Learning - David Barber
- Gaussian Processes for Machine Learning - Carl Edward Rasmussen and Christopher K. I. Williams
- Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
- Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon (main author)
- The LION Way: Machine Learning plus Intelligent Optimization - Robert Battiti and Mauro Brunato
- Thinking Bayes - Allen B. Downey
- Sklearn Basics
- Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Introduction to Information Retrival - Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze
- Interactive Data Visualization for the Web
- Plotting and Visualization in Python
- ggplot2: Elegant Graphics for Data Analysis - Hadley Wickham
- Data Journalism Handbook - Jonathan Gray, Liliana Bounegru, and Lucy Chambers -
- Building Data Science Teams - DJ Patil
- Information Theory, Inference, and Learning Algorithms - David MacKay -
- Mathematics for Computer Science - Eric Lehman, Thomas Leighton, and Albert R. Meyer
- The Field Guide to Data Science
- Data Mining with Weka - Ian H. Witten
- Mining Massive Datasets - Jeff Ullman, Jure Leskovec, Anand Rajaraman (Coursera)
- Introduction to Data Science - Bill Howe (Coursera)
- Introduction to Hadoop and MapReduce - Udacity
- Machine Learning - Andrew Ng (Coursera)
- Machine Learning Video Library - Yaser Abu-Mostafa
- Natural Language Processing - Dan Jurafsky and Christopher Manning (Coursera) -
- Social and Economic Networks: Models and Analysis - Matthew O. Jackson (Coursera)
- Social Network Analysis - Lada Adamic (Coursera)
- Deep Learning - Andrew Ng (Coursera)