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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
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
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
Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Statistical analysis with R on real NBA data.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
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
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
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
Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Statistical analysis with R on real NBA data.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
- GenresMathematics
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Published October 27, 2025
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September 10, 2025I find this book to be well organised to fit different levels of readers. It's going to be my companion for a long time and one of the main reasons for this is that the author makes nearly zero assumptions about the reader except that they are interested in applying statistics to their programming projects. It seems to me there should be another volume of the book to cover other salient topics. Of course, 450 pages are just not enough to cover the entire breadth of statistics. But the author knows his stuff well enough to know what should make the cut this time. Good book!
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