This page holds learning resources I found useful.

Table of Contents

Data Analysis

Book

• Python for Data Analysis, 2nd Edition (2017), by Wes McKinney

Statistics

Book

• All of Statistics: A Concise Course in Statistical Inference (2013), by Larry A. Wasserman

• The Elements of Statistical Learning, 2nd Edition (2009), by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

  • Note: also a book on machine learning

• An Introduction to Statistical Learning with Applications in R (2017), by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

  • Note: a more friendly version of "The Elements of Statistical Learning"

Machine Learning

Book

• Machine Learning: A Probabilistic Perspective (2012), by Kevin P. Murphy

Deep Learning

Book

• Deep  Learning (2016), by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Programming

Book

• Data Structures and Algorithms in Python (2013), Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser

• Cracking the Coding Interview, 6th Edition (2015), by Gayle Laakmann McDowell 

• Python 3 Object Oriented Programming, 3rd Edition (2018), by Dusty Phillips

Tools

Spark

• Advanced Analytics with Spark, 2nd Edition (2017), by Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills

  • Note: Nice hands-on examples; mainly using Scala while some Python; Spark 2.1

• Learning Spark, 2nd Edition (2020), by Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee

  • Note: Spark 3.0; mainly using Python; very well-structured and clear to beginners

• Learning Apache Spark with Python (2020), by Wenqiang Feng

  • Note: Spark 2.1; mainly using Python; a tutorial style book from the author, very easy to follow and many useful use cases

• Note:

  • Spark 2.0+ has many new features compared to Spark 1.0+;  Spark 2.4+ has some important features different from versions before 2.3; the latest Spark 3.0 is now available.

Others

• Data drift

  • Dataset Shift in Machine Learning (2009)

• Machine Learning Explainability

  • An Introduction to Machine Learning Interpretability, 2nd Edition (2019), by Patrick Hall, Navdeep Gill

    • Note: a quick and nice book to guide you through the practical machine learning explainability 

• AutoML

  • Automated Machine Learning: Methods, Systems, Challenges (2019), by Hutter, Frank, Kotthoff, Lars, Vanschoren, Joaquin 

• more...