Statistics Every Programmer Needs

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 technology

Whether you’re analyzing application performance metrics, creating relevant dashboards and reports, or immersing yourself in a numbers-heavy coding project, every programmer needs to know how to turn raw data into actionable insight. Statistics and quantitative analysis are the essential tools every programmer needs to clarify uncertainty, optimize outcomes, and make informed choices.

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.

What's inside

• Probability basics and distributions
• Random variables
• Regression
• Decision trees and random forests
• Time series analysis
• Linear programming
• Monte Carlo and Markov methods and much more

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.

Table of Contents

1 Laying the groundwork
2 Exploring probability and counting
3 Exploring probability distributions and conditional probabilities
4 Fitting a linear regression
5 Fitting a logistic regression
6 Fitting a decision tree and a random forest
7 Fitting time series models
8 Transforming data into decisions with linear programming
9 Running Monte Carlo simulations
10 Building and plotting a decision tree
11 Predicting future states with Markov analysis
12 Examining and testing naturally occurring number sequences
13 Managing projects
14 Visualizing quality control

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