Book mentions in this thread

  • Votes: 14

    Think Bayes

    by Allen B. Downey

  • Votes: 12

    The Elements of Statistical Learning

    by Trevor Hastie

  • Votes: 11

    The Visual Display of Quantitative Information

    by Tufte

    Paperback edition of Edward Tufte's classic book on statistical charts, graphs, and tables, The Visual Display of Quantitative Information. "Best 100 books of the 20th Century." Amazon.com.
  • Votes: 5

    The Signal and the Noise

    by Nate Silver

    The founder of FiveThirtyEight.com challenges myths about predictions in subjects ranging from the financial market and weather to sports and politics, profiling the world of prediction to explain how readers can distinguish true signals from hype, in a report that also reveals the sources and societal costs of wrongful predictions.
  • Votes: 4

    Python for Data Analysis

    by Wes McKinney

    Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
  • Votes: 4

    Moneyball

    by Michael Lewis

  • Votes: 3

    Dataclysm

    by Christian Rudder

  • Votes: 3

    Everybody Lies

    by Seth Stephens-Davidowitz

  • Votes: 3

    Data Smart

    by John W. Foreman

  • Votes: 3

    Gujarati Basic Econometrics Solution Key 4th Edition

    by Damodar Gujarati

  • Votes: 2

    An Introduction to Statistical Learning

    by Gareth James

  • Votes: 2

    R for Data Science

    by Hadley Wickham

  • Votes: 2

    Fundamentals of Statistical Signal Processing, Volume II

    by Steven Kay

  • Votes: 2

    Weapons of Math Destruction

    by Cathy O'Neil

  • Votes: 2

    Applied Multivariate Statistical Analysis

    by Wichern Johnson

  • Votes: 2

    Freakonomics

    by Steven D. Levitt

    Assume nothing, question everything. This is the message at the heart of Freakonomics, Levitt and Dubner's rule-breaking, iconoclastic book about crack dealers, cheating teachers and bizarre baby names that turned everyone's view of the world upside-down and became an international multi-million-copy-selling phenomenon. 'Prepare to be dazzled' Malcolm Gladwell 'A sensation ... you'll be stimulated, provoked and entertained. Of how many books can that be said?' Sunday Telegraph 'Has you chuckling one minute and gasping in amazement the next' Wall Street Journal 'Dazzling ... a delight' Economist 'Made me laugh out loud' Scotland on Sunday
  • Votes: 2

    Emergence

    by Steven Johnson