How to Think Like a Computer Scientist : Learning with Python

Allen B. Downey

Jeffrey Elkner

Chris Meyers

Lulupress

"How to Think Like a Computer Scientist: Learning with Python" is an introduction to computer science using the Python programming language. It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

How to Think Like a Computer Scientist: Learning With Python

Reference

Green Tea Press

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.
<ul>
<li>Develop your understanding of probability and statistics by writing and testing code</li>
<li>Run experiments to test statistical behavior, such as generating samples from several distributions</li>
<li>Use simulations to understand concepts that are hard to grasp mathematically</li>
<li>Learn topics not usually covered in an introductory course, such as Bayesian estimation</li>
<li>Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools</li>
<li>Use statistical inference to answer questions about real-world data</li>
</ul>

Think Stats

Computers & Technology

Computer Science

O'Reilly Media

Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. 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 exercises, case studies, and easy-to-understand explanations.
You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
<ul>
<li>Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables</li>
<li>Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines</li>
<li>Get starter code and solutions to help you re-implement and extend original experiments in complexity</li>
<li>Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics</li>
<li>Examine case studies of complex systems submitted by students and readers</li>
</ul>

Think Complexity: Complexity Science and Computational Modeling

Programming Languages

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.Start with the basics, including language syntax and semanticsGet a clear definition of each programming conceptLearn values, variables, statements, functions, and data structures in a logical progressionDiscover how to work with files and databasesUnderstand objects, methods, and object-oriented programmingUse debugging techniques to fix syntax, runtime, and semantic errorsExplore interface design, data structures, and GUI-based programs th

Think Python

Programming

O'Reilly Media, Inc.

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.
<ul>
<li>Use your existing programming skills to learn and understand Bayesian statistics</li>
<li>Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing</li>
<li>Get started with simple examples, using coins, M&amp;Ms, Dungeons &amp; Dragons dice, paintball, and hockey</li>
<li>Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.</li>
</ul>

Think Bayes: Bayesian Statistics in Python

Think Python: How to Think Like a Computer Scientist

O'Reilly Media, Incorporated

Modeling and Simulation in Python: An Introduction for Scientists and Engineers

No Starch Press

See the best price to sell, buy, or rent books by Allen B. Downey. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Allen B. Downey