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about the book

Statistics Every Programmer Needs teaches the nuts and bolts of applying statistics to the everyday problems you’ll face as a software developer. Each self-contained chapter provides a complete and comprehensive tutorial on a specific quantitative technique. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills.

You’ll predict ultramarathon split times using linear regression, identify raisin types from morphological features, forecast stock prices using time series models, analyze system reliability using Markov chains, and much more. You’ll not only learn how to use each method, but why it works, and how to explain your results. Whatever your field, you’ll soon be ready to model uncertainty, optimize resources, forecast outcomes, and assess risk with mathematical precision.

about the reader

For analysts, managers, or anyone looking to incorporate data into their decision making. Examples in Python.

about the author

Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data, also published by Manning. Mr. Sutton earned his undergraduate degree from the University of Southern California and master’s degrees from George Washington University and Northwestern University.