---
product_id: 83768721
title: "Classic Computer Science Problems in Python: Easy to Advanced Programming Challenges to Sharpen Your Coding Skills and Improve Your Algorithmic Thinking"
price: "806245Ar"
currency: MGA
in_stock: true
reviews_count: 10
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---

# Python-focused implementations 30+ classic algorithms covered Advanced AI & neural network challenges Classic Computer Science Problems in Python: Easy to Advanced Programming Challenges to Sharpen Your Coding Skills and Improve Your Algorithmic Thinking

**Price:** 806245Ar
**Availability:** ✅ In Stock

## Summary

> 🐍 Code smarter, think deeper — unlock Python’s classic algorithm secrets today!

## Quick Answers

- **What is this?** Classic Computer Science Problems in Python: Easy to Advanced Programming Challenges to Sharpen Your Coding Skills and Improve Your Algorithmic Thinking
- **How much does it cost?** 806245Ar with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [madagascar.desertcart.com](https://madagascar.desertcart.com/products/83768721-classic-computer-science-problems-in-python-easy-to-advanced-programming)

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## Why This Product

- Free international shipping included
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## Key Features

- • **Challenge Your Limits:** Designed for intermediate to advanced Python users ready to level up their algorithmic thinking.
- • **Python-First Approach:** All challenges come with clean, ready-to-run Python code—perfect for hands-on learning and quick iteration.
- • **Master Timeless Algorithms:** From binary search to adversarial search, sharpen your core coding skills with proven techniques.
- • **Real-World Problem Solving:** Apply classic computer science problems that underpin modern data science, machine learning, and software engineering.
- • **Explore AI & Genetic Algorithms:** Dive into cutting-edge topics like neural networks and genetic algorithms to future-proof your skillset.

## Overview

Classic Computer Science Problems in Python offers 30+ algorithmic challenges with Python implementations, covering search, clustering, graphs, AI, and more. Tailored for intermediate to advanced Python programmers, it bridges foundational concepts with modern applications in data science and machine learning, making it an essential resource to sharpen your coding and algorithmic thinking.

## Description

Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problemsin Swift.

Review: A really good new algorithm book - I really liked Classic Computer Science Problems in Python. It covers many interesting algorithms, several of which I had not read about before. My favorites in the book were neural networks, constraint-satisfaction problems, genetic algorithms, and the minimax algorithm. For most of the algorithms, the author develops a framework solution, and then uses it to solve a few different problems of the same kind. The solutions are complete but small programs that you can download from GitHub. They are well suited for exploring the solutions in more detail. For example, I used the neural network program to see how the neuron weights varied between different runs. I found this to be one of the better algorithm books that I have read. I have written a more detailed review on my blog, which can be found by googling.
Review: Great Content, But Moves Too Fast, Not Enough Depth - This is a good book, but I don't think it serves a very wide audience well. It covers some of the most famous and popular algorithms out there – but it moves so fast and goes into so little depth that I can't recommend it to someone junior looking to learn these topics (which seems to be the target audience). I think anyone who already has a handle on all these algorithms, and just wants to learn efficient ways to write them in Python, will be happy with this book. But if you don't know these algos, and/or you're not already very confident in Python, this book is equally as confusing as educational. I know Python well, and I know most of the algos in this book already, so for me those were easy to read over and the Python code made perfect sense. The few that I didn't already know, however, left me confused and frustrated, because the explanations just didn't go into enough granular detail (about the algo logic nor the python implementation) or give enough different examples for me to really make sense of them from reading alone (which a well written book does offer). Final word: If you already know Python and you just want a reference implementation of algos you already firmly understand, this book is a walk in the park. For anyone else there is certainly lots of value in this book, but it may also leave you with as many questions as answers by the final page.

## Features

- New Store Stock

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | 251,096 in Books ( See Top 100 in Books ) 286 in Web Scripting & Programming 299 in Introduction to Programming |
| Customer Reviews | 4.4 out of 5 stars 149 Reviews |

## Images

![Classic Computer Science Problems in Python: Easy to Advanced Programming Challenges to Sharpen Your Coding Skills and Improve Your Algorithmic Thinking - Image 1](https://m.media-amazon.com/images/I/71Cb-dAAzfL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ A really good new algorithm book
*by H***E on 27 October 2019*

I really liked Classic Computer Science Problems in Python. It covers many interesting algorithms, several of which I had not read about before. My favorites in the book were neural networks, constraint-satisfaction problems, genetic algorithms, and the minimax algorithm. For most of the algorithms, the author develops a framework solution, and then uses it to solve a few different problems of the same kind. The solutions are complete but small programs that you can download from GitHub. They are well suited for exploring the solutions in more detail. For example, I used the neural network program to see how the neuron weights varied between different runs. I found this to be one of the better algorithm books that I have read. I have written a more detailed review on my blog, which can be found by googling.

### ⭐⭐⭐ Great Content, But Moves Too Fast, Not Enough Depth
*by J***Y on 15 September 2025*

This is a good book, but I don't think it serves a very wide audience well. It covers some of the most famous and popular algorithms out there – but it moves so fast and goes into so little depth that I can't recommend it to someone junior looking to learn these topics (which seems to be the target audience). I think anyone who already has a handle on all these algorithms, and just wants to learn efficient ways to write them in Python, will be happy with this book. But if you don't know these algos, and/or you're not already very confident in Python, this book is equally as confusing as educational. I know Python well, and I know most of the algos in this book already, so for me those were easy to read over and the Python code made perfect sense. The few that I didn't already know, however, left me confused and frustrated, because the explanations just didn't go into enough granular detail (about the algo logic nor the python implementation) or give enough different examples for me to really make sense of them from reading alone (which a well written book does offer). Final word: If you already know Python and you just want a reference implementation of algos you already firmly understand, this book is a walk in the park. For anyone else there is certainly lots of value in this book, but it may also leave you with as many questions as answers by the final page.

### ⭐⭐⭐⭐ Great book...depending on why you bought it
*by J***N on 12 February 2022*

I'm about 6 chapters in now and feel like I'm ready to give an initial review this book. Firstly, if you're new to programming and python then this is not the one for you. I generally like the way each problem or theme is presented and resolved overall, but there are huge jumps in how the solution is built up and I can't help thinking it could have been kept a lot simpler or clearer in places. Really helpful diagrams and commentary though, and personally I appreciate the lack of fluff between subjects. I've bought too many books trying to shoehorn comedy nuggets into every other paragraph or a cheesy segway that adds nothing. Has to be mentioned that there are incredibly useful sections at the end of the chapters giving brief descriptions of how and where these solutions can be used in the real world. It's my experience that very few books on algorithms and/or machine learning actually bother to do this and as soon as I came across this in the sample download I bought the book immediately. As hinted before I struggled a bit comprehending some of the python used, but that's my fault and relative inexperience with it. The author specifies this is the intermediate python programmers and I would go as far as to say this could be changed to "intermediate/advanced." So in summary, I bought this book hoping to see how some familiar problems / concepts are applied from scratch using some fairly advanced python to drag me up to speed and that's exactly what it is. So I'm very happy, but you may not be if you want more of a gentle introduction to both topics.

## Frequently Bought Together

- Classic Computer Science Problems in Python
- Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming
- Python Programming: An Introduction to Computer Science, Fourth Edition

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*Last updated: 2026-08-18*