Data Science and Analytics - Recommended books 2015-2018

• The Data Science Handbook, By Cady, Field, 28. February 2017 John Wiley & Sons Inc (28. Februar 2017), 418 pp.

• Practical Data Science Cookbook - Second Edition: Data pre-processing, analysis and visualization using R and Python, by Prabhanjan Tattar and Tony Ojeda, Packt Publishing - ebooks Account; 2nd Revised edition edition (June 29, 2017), 434 pp
  

• Python Data Science Handbook: Essential Tools for Working with Data 1st Edition, by Jake VanderPlas, O'Reilly Media; 1 edition (December 10, 2016), 548 pp.
  

• Mastering Azure Analytics: Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark, by Zoiner Tejada, O'Reilly Media; 1 edition (April 24, 2017), 412 pp.
  

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics), by Trevor Hastie and Robert Tibshirani,Springer; 2nd edition (2016), 745 pp
  

• Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner, By Vijay Kotu and Bala Deshpande, Morgan Kaufmann; 1 edition (December 17, 2014), 446 pp
  

• Critical Thinking: Your Guide to Effective Argument, Successful Analysis and Independent Study, by Tom Chatfield, SAGE Publications Ltd; 1 edition (November 23, 2017), 328 pp
  

• Introduction To Machine Learning 3Rd Edition, by Ethem Alpaydin, Prentice Hall of India; 3rd edition (2014), 609 pp
  

• Mining of Massive Datasets, by Jure Leskovec and Anand Rajaraman, Cambridge University Press; 2 edition (December 29, 2014), 476 pp
  

• Applied Predictive Modeling, by Max Kuhn and Kjell Johnson, Springer; 1st ed. 2013, Corr. 2nd printing 2018 edition (March 30, 2018), 600 pp.
  

• Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, by John D. Kelleher and Brian Mac NameeThe MIT Press, Jul 24, 2015, 624 pp.
  

• Deep Learning (Adaptive Computation and Machine Learning), by Ian Goodfellow and Yoshua Bengio, The MIT Press (November 18, 2016), 775 pp.
  

• Neural Networks for Time Series Forecasting with R: An Intuitive Step by Step Blueprint for Beginners, by N D Lewis, CreateSpace Independent Publishing Platform (March 27, 2017), 238 pp
  

• Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics), by Robert H. Shumway and David S. Stoffe, Springer; 4th ed. 2017 edition (April 11, 2017), 562 pp.
  

• Storytelling with Data: A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic, Wiley; 1 edition (November 2, 2015), 278 pp.
  

• The Truthful Art: Data, Charts, and Maps for Communication, by Alberto Cairo, New Riders; 1 edition (February 28, 2016), 400 pp.
  

• Data Visualisation: A Handbook for Data Driven Design, by Andy Kirk, SAGE Publications Ltd; 1 edition (August 15, 2016), 368 pp.
  

• Julia for Data Science, by Anshul Joshi, Packt Publishing (October 6, 2016), 348 pp.
  

• Pierson, Lillian, Data Science For Dummies (For Dummies (Computers))
  

• Mueller, John Paul, Python for Data Science For Dummies (For Dummies (Computers))
  

• Mueller, John Paul, Machine Learning For Dummies
  

• O'Neil, Cathy, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy