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 examples
• Understand how to formulate problems, think creatively about solutions, and develop, test, and debug programs
• Learn about input and output, decisions and loops, classes and methods, strings and arrays, recursion and polymorphism
• Determine which program development methods work best for you, and practice the important skill of debugging

If you understand basic mathematics and know how to program with Python, youâ 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 theyâ re applied in the real world. In the first chapter alone, youâ 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.

Youâ ll explore:

• Periodic signals and their spectrums
• Harmonic structure of simple waveforms
• Chirps and other sounds whose spectrum changes over time
• Noise signals and natural sources of noise
• The autocorrelation function for estimating pitch
• The discrete cosine transform (DCT) for compression
• The Fast Fourier Transform for spectral analysis
• Relating operations in time to filters in the frequency domain
• Linear time-invariant (LTI) system theory
• Amplitude modulation (AM) used in radio

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

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.

An introductory textbook for people who have not programmed before. Covers basic MATLAB programming with emphasis on modeling and simulation of physical systems.

Elements of Data Science is an introduction to the practical skills of working with data, written for people with no programming experience. Concepts are explained clearly and concisely, and exercises in each chapter demonstrate the real-world use of each feature. * Step-by-Step Approach: Learn how to execute a data science project from start to finish, formulating questions, visualizing data, applying statistical methods, and communicating results. * Practical Python Programming: This book starts with basic Python concepts and builds up to advanced data processing and analysis techniques. * Interactive Learning: Jupyter notebooks are available for each chapter, so readers can follow along, run experiments, and build understanding through hands-on exercises. * Solid Foundation: Explore fundamental concepts such as exploratory data analysis, statistical inference, regression analysis, classification algorithms, and more, all through the lens of real-world case studies. Whether you're a student, a professional, or simply curious about the power of data, Elements of Data Science provides essential tools for finding insights in data.