If you want to under computer science, but have no programming experience, this book is for you.Think Java starts with the most basic programming concepts and carefully defines every term when it’s first used. The authors then develop each new concept in a logical progression. Larger pieces like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.

This book is also concise. Each chapter is 12-14 pages and covers the material for one week of a college course. Rather than present a comprehensive presentation of Java, the authors instead focus on the tools of software design, starting with small functions and basic algorithms, and working up to object-oriented design.

• Readable: while most other books forget to define terms or introduce too much material at once, Think Java is careful to define all terms and present difficult topics gradually
• One concept at a time: breaks the topics that give students the most trouble into a series of small steps so that students can exercise new concepts before continuing
• Balance of language and concepts: the book is not primarily about Java, but uses Java examples to demonstrate software engineering concepts
  
  Think Java is appropriate as a textbook in an introductory college or high school class. The book is currently in use at several colleges, universities, and high schools. It’s appropriate for people learning Java as a first language, including students learning on their own and professionals who are retraining.

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 one step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design.

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.

This updated second edition has a more direct focus on Python 3, along with simplified installation instructions. You'll also get added coverage of more topics, including list comprehensions and additional data structures.

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 code
• Run experiments to test statistical behavior, such as generating samples from several distributions
• Use simulations to understand concepts that are hard to grasp mathematically
• Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
• Use statistical inference to answer questions about real-world data

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 statistics
• Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
• Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey
• Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.