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 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

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. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills.

About the book:

Statistics Every Programmer Needs teaches you how to apply statistics to the everyday problems you'll face as a software developer. Each chapter is a new tutorial. You'll predict ultramarathon times using linear regression, forecast stock prices with time series models, analyze system reliability using Markov chains, and much more. The book emphasizes a balance between theory and hands-on Python implementation, with annotated code and real-world examples to ensure practical understanding and adaptability across industries. Examples are 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.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.