Statistics Every Programmer Needs
Statistics Every Programmer Needs
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Statistics Every Programmer Needs
Statistics Every Programmer Needs
Statistics Every Programmer Needs helps you make better, data-informed decisions by teaching key statistical methods like regression, simulation, and decision trees.
Statistics Every Programmer Needs
Artikelnr.: 191240413

Statistics Every Programmer Needs

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Artikelnr.: 191240413

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Statistics Every Programmer Needs helps you make better, data-informed decisions by teaching key statistical methods like regression, simulation, and decision trees.
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Besondere Merkmale

Essential Knowledge
Covers critical statistics concepts that every programmer should understand, enabling better data analysis and informed decision-making in software development tasks.
Practical Application
Focuses on real-world applications of statistics, ensuring programmers can apply learned concepts directly to enhance their coding and project outcomes.
User-Friendly Approach
Designed for programmers with varying levels of statistical knowledge, breaking down complex topics into easily digestible information that promotes effective learning.

Produktdetails

Shop Statistics Every Programmer Needs online at a best price in Germany. 1633436055
  • 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 reader 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. 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 Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Publisher Manning
Publication date September 9, 2025
Language English
Print length 448 pages
ISBN-10 1633436055
ISBN-13 978-1633436053
Item Weight 794 g
Dimensions 7.38 x 1 x 9.25 inches (18.7 x 2.5 x 23.5 cm)

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English Edition Gary Sutton Format: Paperback Editorial Review

Statistics Every Programmer Needs is a comprehensive guide published by Manning, set to release on September 9, 2025. This 448-page book is tailored to help programmers grasp essential statistical concepts crucial in data analysis and machine learning. Written in English, it delves into topics that are often challenging for developers but necessary for effective problem-solving. The book's structured approach and clarity make it a valuable tool for anyone looking to enhance their programming skills with statistical knowledge. Readers have noted its engaging writing style and practical examples, which ensure a better understanding of the concepts presented.

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Vorteile

  • Comprehensive guide to statistics for programmers
  • Structured and clear explanations
  • Includes practical examples for easier understanding
  • Suitable for both beginners and advanced programmers
  • Publisher Manning is well-respected in the field

Nachteile

  • The book may feel dense for some readers.

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Merkmale und Vorteile

  • Transform statistics from a challenge into a crucial skill with practical Python examples.
  • Learn key statistical methods, including regression, simulation, and decision trees.
  • Enhance decision-making skills for real-world programming scenarios.
  • Access structured and reproducible methods for complex decisions.
  • Includes well-annotated and reusable Python code examples.
  • Perfect for analyzing performance metrics and creating actionable insights.

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