Classic Computer Science Problems in Python
Mar 2019 · Sold by Simon and Schuster
4.0star
2 reviews
Ebook
224
Pages
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About this ebook
"Whether you're a novice or a seasoned professional, there's an Aha! moment in this book for everyone." - James Watson, Adaptive
”Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.” —Daniel Kenney-Jung, MD, University of Minnesota
Key Features
• Master formal techniques taught in college computer science classes
• Connect computer science theory to real-world applications, data, and performance
• Prepare for programmer interviews
• Recognize the core ideas behind most “new” challenges
• Covers Python 3.7
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About The Book
Programming problems that seem new or unique are usually rooted in well-known engineering principles. Classic Computer Science Problems in Python guides you through time-tested scenarios, exercises, and algorithms that will prepare you for the “new” problems you’ll face when you start your next project.
In this amazing book, you'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. As you work through examples for web development, machine learning, and more, you'll remember important things you've forgotten and discover classic solutions that will save you hours of time.
What You Will Learn
• Search algorithms
• Common techniques for graphs
• Neural networks
• Genetic algorithms
• Adversarial search
• Uses type hints throughout
This Book Is Written For
For intermediate Python programmers.
About The Author
David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Java (Manning, 2020)
Table of Contents
1. Small problems
2. Search problems
3. Constraint-satisfaction problems
4. Graph problems
5. Genetic algorithms
6. K-means clustering
7. Fairly simple neural networks
8. Adversarial search
9. Miscellaneous problems
”Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.” —Daniel Kenney-Jung, MD, University of Minnesota
Key Features
• Master formal techniques taught in college computer science classes
• Connect computer science theory to real-world applications, data, and performance
• Prepare for programmer interviews
• Recognize the core ideas behind most “new” challenges
• Covers Python 3.7
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About The Book
Programming problems that seem new or unique are usually rooted in well-known engineering principles. Classic Computer Science Problems in Python guides you through time-tested scenarios, exercises, and algorithms that will prepare you for the “new” problems you’ll face when you start your next project.
In this amazing book, you'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. As you work through examples for web development, machine learning, and more, you'll remember important things you've forgotten and discover classic solutions that will save you hours of time.
What You Will Learn
• Search algorithms
• Common techniques for graphs
• Neural networks
• Genetic algorithms
• Adversarial search
• Uses type hints throughout
This Book Is Written For
For intermediate Python programmers.
About The Author
David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Java (Manning, 2020)
Table of Contents
1. Small problems
2. Search problems
3. Constraint-satisfaction problems
4. Graph problems
5. Genetic algorithms
6. K-means clustering
7. Fairly simple neural networks
8. Adversarial search
9. Miscellaneous problems
Ratings and reviews
4.0
2 reviews
Jennifer Graziano
- Flag inappropriate
May 24, 2025
Origin of the alphabet; number line, computer is the same place. There isn't a calendar without a programmer; different ones in society are for companies to supply basic utilities; motor vehicles, food clothes & shelter. Computer programs give the answers to outstanding questions; humans do not. They also drive all artificial moving objects; humans do not. One program designs all the building and a different one does cars; the program includes after they are built schedules for them and us. Cameras are a derivative of computer separate from video recorder for unreal pictures and photograph obstructing justice to disappear or vanish includes video tapes & DVD's.
About the author
David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problems in Swift.
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