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 techniquesBuild predictive models and simulationsOptimize decisions under constraintsInterpret and validate results with statistical rigorImplement quantitative methods using PythonIn 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 technologyWhether youre 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 bookStatistics Every Programmer Needs teaches you how to apply statistics to the everyday problems youll face as a software developer. Each chapter is a new tutorial. Youll 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 insideProbability basics and distributionsRandom variablesRegressionDecision trees and random forestsTime series analysisLinear programmingMonte Carlo and Markov methods and much moreAbout the readerExamples are in Python.About the authorGary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data.Table of Contents1 Laying the groundwork2 Exploring probability and counting3 Exploring probability distributions and conditional probabilities4 Fitting a linear regression5 Fitting a logistic regression6 Fitting a decision tree and a random forest7 Fitting time series models8 Transforming data into decisions with linear programming9 Running Monte Carlo simulations10 Building and plotting a decision tree11 Predicting future states with Markov analysis12 Examining and testing naturally occurring number sequences13 Managing projects14 Visualizing quality control