Statistics Every Programmer Needs - by Gary Sutton (Paperback)

Book Synopsis

Put statistics into practice with Python!

Data-driven decisions rely on statistics. Statistics Every Programmer Needs introduces the statistical and quantitative methods that will help you go beyond "gut feeling" for tasks like predicting stock prices or assessing quality control, with examples using the rich tools of the Python data ecosystem.

Statistics Every Programmer Needs will teach you how to:

- Apply foundational and advanced statistical techniques
- Build predictive models and simulations
- Optimize decisions under constraints
- Interpret and validate results with statistical rigor
- Implement quantitative methods using Python

You've got the raw data--how do you turn it into actionable insights you can use to make decisions? Statistics and quantitative technologies are the essential tools every programmer needs for navigating uncertainty, optimizing outcomes, and making informed choices. In this hands-on guide, stats expert Gary Sutton blends the theory behind these statistical techniques with practical Python-based applications, offering structured, reproducible, and defensible methods for tackling complex decisions.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

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.

About the Author

Gary Sutton is a vice president for a leading financial services company. He has built and led high-performing business intelligence and analytics organizations across multiple verticals, where R was the preferred programming language for predictive modeling, statistical analyses, and other quantitative insights. Gary earned his undergraduate degree from the University of Southern California, a Masters from George Washington University, and a second Masters in Data Science, from Northwestern University.