Allen Downey is een professor informatica wiens werk zich verdiept in de wereld van software en datawetenschap. Zijn geschriften verkennen regelmatig thema's van bayesiaanse kansrekening en statistiek, vaak tentoongesteld op zijn blog. Downey staat bekend om zijn open-source leerboeken die gericht zijn op het bieden van praktisch begrip van complexe onderwerpen.
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 and case studies.
This book offers a straightforward introduction to software design tailored for beginners with no prior programming experience. Utilizing the Python programming language, it focuses on essential concepts and practical applications, making it accessible for newcomers. Readers will learn foundational design principles that can be applied in real-world scenarios, empowering them to build their own software projects confidently.
If you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser. 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 objects, methods, and object-oriented programming Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design, data structures, and GUI-based programs through case studies
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.
Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.
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.
Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands- on guide.
Teaches the entire exploratory data analysis process using a single case study.
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.
"Teacher, data scientist, and blogger Allen B. Downey knows well that the human mind has both an innate ability to understand statistics and to be fooled by them. Statistically speaking, you will be less popular than your friends, arrive at a train station during a gap in service, and fail to find a running mate in a race. But more than surprising us, errors in statistical thinking, Downey shows, can have a huge impact. Statistical confusion has led to incorrect patient prognoses, caused mistakes in predicting disasters like earthquakes, hurt vaccination programs, hindered social justice efforts, and led to dubious policy decisions. Written for those who may have once taken a statistics course, but now forget almost everything they've learned, the book includes a diversity of examples that use real data and have real world impacts. Building understanding incrementally, Downey engagingly and accessibly helps readers understand what we might learn when we get the mathematics right, and the consequences when we get it all wrong"--