Getting started with data science

Getting started with data science

Murtaza Haider’s book on analytics, Getting Started with Data Science, is available for pre-sale on The book will be released in July 2015.  The book is being published by IBM Press and Pearson. More on the book from the publisher:

… there’s been nothing sexy about learning data science — until now.

Getting Started with Data Science takes its approach from worldwide best-sellers like Freakonomics and the books of Malcolm Gladwell: it teaches through a powerful narrative packed with unforgettable stories.

Murtaza Haider offers careful, jargon-free coverage of basic theory and technique, backed with plenty of clear examples and practice opportunities. Everything’s software and platform independent, so you can learn what you need whether you work with R, Stata, SPSS, SAS, or another toolset.

Best of all, Haider teaches a crucial skillset most academic data science books ignore: how to transform data into narratives, graphics, and tables that make it vivid and actionable.

Every chapter is built around a real research challenge, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions like:

  • Are child safety seats safer for children than regular seat belts?
  • Which married parents are likelier to have affairs: fathers or mothers?
  • Is CEO compensation independent of a firm’s profitability?
  • Do attractive professors get better teaching evaluations?
  • What induces teenagers to start smoking?
  • What determines housing prices more: house size or location?
  • How do teenagers and older people differ in how they use social media?
  • Do risk-averse and risk-prone individuals differ in their purchases of big-ticket items?

For each problem, you’ll walk through identifying the right data and methods, creating summary statistics, describing and visualizing findings, and seeing how others have handled the challenge. In advanced chapters, you’ll also learn sophisticated statistical modeling techniques. Throughout, the focus is on data: finding it, using it, and powerfully communicating its meaning.