Allen B. Downey – författare

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time-beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.With this popular hands-on guide at your side, you'll get:A grounding in the syntax and semantics of the Python languageA clear definition of each programming concept, with emphasis on clear vocabularyHow to work with variables, statements, functions, and data structures in a logical progressionTechniques for reading and writing files and databasesA solid understanding of objects, methods, and object-oriented programmingDebugging strategies for syntax, runtime, and semantic errorsAn introduction to recursion, interface design, data structures, and basic algorithmsHow to use LLMs-including effective prompts, testing code, and debuggingAnd more

An essential guide to the ways data can improve decision making.  Statistics are everywhere: in news reports, at the doctor’s office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which.  Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfalls—like the base rate fallacy, length-biased sampling, and Simpson’s paradox—and shines a light on what we learn when we interpret data correctly, and what goes wrong when we don’t. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statistics—or if you have and forgot everything you learned—this book will offer new insight into the methods and measurements that help us understand the world.

"Think DSP: Digital Signal Processing in Python" is an introduction to signal processing and system analysis using a computational approach. The premise of this book (like the others in the Think X series) is that if you know how to program, you can use that skill to learn other things. By the end of the first chapter, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Subsequent chapters follow a logical progression that develops the important ideas incrementally, with a focus on applications.

If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand types, methods, and multiple dispatch Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design and data structures through case studies

An essential guide to the ways data can improve decision making.  Statistics are everywhere: in news reports, at the doctor’s office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which.  Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfalls—like the base rate fallacy, length-biased sampling, and Simpson’s paradox—and shines a light on what we learn when we interpret data correctly, and what goes wrong when we don’t. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statistics—or if you have and forgot everything you learned—this book will offer new insight into the methods and measurements that help us understand the world.

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.

With this popular hands-on guide at your side, you''ll get:

A grounding in the syntax and semantics of the Python languageA clear definition of each programming concept, with emphasis on clear vocabularyHow to work with variables, statements, functions, and data structures in a logical progressionTechniques for reading and writing files and databasesA solid understanding of objects, methods, and object-oriented programmingDebugging strategies for syntax, runtime, and semantic errorsAn introduction to recursion, interface design, data structures, and basic algorithmsHow to use LLMs—including effective prompts, testing code, and debuggingAnd more

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.

With this popular hands-on guide at your side, you''ll get:

A grounding in the syntax and semantics of the Python languageA clear definition of each programming concept, with emphasis on clear vocabularyHow to work with variables, statements, functions, and data structures in a logical progressionTechniques for reading and writing files and databasesA solid understanding of objects, methods, and object-oriented programmingDebugging strategies for syntax, runtime, and semantic errorsAn introduction to recursion, interface design, data structures, and basic algorithmsHow to use LLMs—including effective prompts, testing code, and debuggingAnd more

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you''ll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to identifying patterns and testing hypotheses.

Whether you''re a data scientist, software engineer, or data enthusiast, you''ll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You''ll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.

Analyze data distributions and visualize patterns using Python librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you''ll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to identifying patterns and testing hypotheses.

Whether you''re a data scientist, software engineer, or data enthusiast, you''ll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You''ll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.

Analyze data distributions and visualize patterns using Python librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis-from wrangling data and generating statistics to identifying patterns and testing hypotheses.Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.Analyze data distributions and visualize patterns using Python librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.

Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds.

Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.

You’ll explore:

Periodic signals and their spectrumsHarmonic structure of simple waveformsChirps and other sounds whose spectrum changes over timeNoise signals and natural sources of noiseThe autocorrelation function for estimating pitchThe discrete cosine transform (DCT) for compressionThe Fast Fourier Transform for spectral analysisRelating operations in time to filters in the frequency domainLinear time-invariant (LTI) system theoryAmplitude modulation (AM) used in radio

Other books in this series include Think Stats and Think Bayes, also by Allen Downey.

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds.

Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.

You’ll explore:

Periodic signals and their spectrumsHarmonic structure of simple waveformsChirps and other sounds whose spectrum changes over timeNoise signals and natural sources of noiseThe autocorrelation function for estimating pitchThe discrete cosine transform (DCT) for compressionThe Fast Fourier Transform for spectral analysisRelating operations in time to filters in the frequency domainLinear time-invariant (LTI) system theoryAmplitude modulation (AM) used in radio

Other books in this series include Think Stats and Think Bayes, also by Allen Downey.

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you''ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.

Use your existing programming skills to learn and understand Bayesian statisticsWork with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testingGet started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockeyLearn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.

By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online.

Use data structures such as lists and maps, and understand how they workBuild an application that reads Wikipedia pages, parses the contents, and navigates the resulting data treeAnalyze code to predict how fast it will run and how much memory it will requireWrite classes that implement the Map interface, using a hash table and binary search treeBuild a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results

Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.

By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online.

Use data structures such as lists and maps, and understand how they workBuild an application that reads Wikipedia pages, parses the contents, and navigates the resulting data treeAnalyze code to predict how fast it will run and how much memory it will requireWrite classes that implement the Map interface, using a hash table and binary search treeBuild a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results

Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.

If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.

Want to learn how to program and think like a computer scientist? This practical guide gets you started on your programming journey with the help of Perl 6, the younger sister of the popular Perl programming language. Ideal for beginners, this hands-on book includes over 100 exercises with multiple solutions, and more than 1,000 code examples so you can quickly practice what you learn. Experienced programmers—especially those who know Perl 5—will also benefit.

Divided into two parts, Think Perl 6 starts with basic concepts that every programmer needs to know, and then focuses on different programming paradigms and some more advanced programming techniques. With two semesters’ worth of lessons, this book is the perfect teaching tool for computer science beginners in colleges and universities.

Learn basic concepts including variables, expressions, statements, functions, conditionals, recursion, and loopsUnderstand commonly used basic data structures and the most useful algorithmsDive into object-oriented programming, and learn how to construct your own types and methods to extend the languageUse grammars and regular expressions to analyze textual contentExplore how functional programming can help you make your code simpler and more expressive

Want to learn how to program and think like a computer scientist? This practical guide gets you started on your programming journey with the help of Perl 6, the younger sister of the popular Perl programming language. Ideal for beginners, this hands-on book includes over 100 exercises with multiple solutions, and more than 1,000 code examples so you can quickly practice what you learn. Experienced programmers—especially those who know Perl 5—will also benefit.

Divided into two parts, Think Perl 6 starts with basic concepts that every programmer needs to know, and then focuses on different programming paradigms and some more advanced programming techniques. With two semesters’ worth of lessons, this book is the perfect teaching tool for computer science beginners in colleges and universities.

Learn basic concepts including variables, expressions, statements, functions, conditionals, recursion, and loopsUnderstand commonly used basic data structures and the most useful algorithmsDive into object-oriented programming, and learn how to construct your own types and methods to extend the languageUse grammars and regular expressions to analyze textual contentExplore how functional programming can help you make your code simpler and more expressive

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch.

Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics.

Start with the basics, including language syntax and semanticsGet a clear definition of each programming conceptLearn about values, variables, statements, functions, and data structures in a logical progressionDiscover how to work with files and databasesUnderstand types, methods, and multiple dispatchUse debugging techniques to fix syntax, runtime, and semantic errorsExplore interface design and data structures through case studies

If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch.

Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics.

Start with the basics, including language syntax and semanticsGet a clear definition of each programming conceptLearn about values, variables, statements, functions, and data structures in a logical progressionDiscover how to work with files and databasesUnderstand types, methods, and multiple dispatchUse debugging techniques to fix syntax, runtime, and semantic errorsExplore interface design and data structures through case studies

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end.

Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned.

Learn one concept at a time: tackle complex topics in a series of small steps with examplesUnderstand how to formulate problems, think creatively about solutions, and write programs clearly and accuratelyDetermine which development techniques work best for you, and practice the important skill of debuggingLearn relationships among input and output, decisions and loops, classes and methods, strings and arraysWork on exercises involving word games, graphics, puzzles, and playing cards

The updated second edition of Think Java also features new chapters on polymorphism and data processing, as well as content covering changes through Java 12.

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end.

Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned.

Learn one concept at a time: tackle complex topics in a series of small steps with examplesUnderstand how to formulate problems, think creatively about solutions, and write programs clearly and accuratelyDetermine which development techniques work best for you, and practice the important skill of debuggingLearn relationships among input and output, decisions and loops, classes and methods, strings and arraysWork on exercises involving word games, graphics, puzzles, and playing cards

The updated second edition of Think Java also features new chapters on polymorphism and data processing, as well as content covering changes through Java 12.

Think Java is a hands-on introduction to computer science and programming used by many universities and high schools around the world. Its conciseness, emphasis on vocabulary, and informal tone make it particularly appealing for readers with little or no experience. The book starts with the most basic programming concepts and gradually works its way to advanced object-oriented techniques.In this fully updated and expanded edition, authors Allen Downey and Chris Mayfield introduce programming as a means for solving interesting problems. Each chapter presents material for one week of a college course and includes exercises to help you practice what you’ve learned. Along the way, you’ll see nearly every topic required for the AP Computer Science A exam and Java SE Programmer I certification.Discover one concept at a time: tackle complex topics in a series of small steps with multiple examplesUnderstand how to formulate problems, think creatively about solutions, and develop, test, and debug programsLearn about input and output, decisions and loops, classes and methods, strings and arrays, recursion and polymorphismDetermine which program development methods work best for you, and practice the important skill of debugging