problem solving with algorithms and data structures using python reddit

• Table of Contents
• Scratch ActiveCode
• Navigation Help
• Help for Instructors
• About Runestone
• Report A Problem
• 1. Introduction
• 2. Analysis
• 3. Basic Data Structures
• 4. Recursion
• 5. Sorting and Searching
• 6. Trees and Tree Algorithms
• 7. Graphs and Graph Algorithms

Problem Solving with Algorithms and Data Structures using Python ¶

By Brad Miller and David Ranum, Luther College (as remixed by Jeffrey Elkner)

• 1.1. Objectives
• 1.2. Getting Started
• 1.3. What Is Computer Science?
• 1.4. What Is Programming?
• 1.5. Why Study Data Structures and Abstract Data Types?
• 1.6. Why Study Algorithms?
• 1.7. Review of Basic Python
• 1.8.1. Built-in Atomic Data Types
• 1.8.2. Built-in Collection Data Types
• 1.9.1. String Formatting
• 1.10. Control Structures
• 1.11. Exception Handling
• 1.12. Defining Functions
• 1.13.1. A Fraction Class
• 1.13.2. Inheritance: Logic Gates and Circuits
• 1.14. Summary
• 1.15. Key Terms
• 1.16. Discussion Questions
• 1.17. Programming Exercises
• 2.1. Objectives
• 2.2. What Is Algorithm Analysis?
• 2.3. Big-O Notation
• 2.4.1. Solution 1: Checking Off
• 2.4.2. Solution 2: Sort and Compare
• 2.4.3. Solution 3: Brute Force
• 2.4.4. Solution 4: Count and Compare
• 2.5. Performance of Python Data Structures
• 2.7. Dictionaries
• 2.8. Summary
• 2.9. Key Terms
• 2.10. Discussion Questions
• 2.11. Programming Exercises
• 3.1. Objectives
• 3.2. What Are Linear Structures?
• 3.3. What is a Stack?
• 3.4. The Stack Abstract Data Type
• 3.5. Implementing a Stack in Python
• 3.6. Simple Balanced Parentheses
• 3.7. Balanced Symbols (A General Case)
• 3.8. Converting Decimal Numbers to Binary Numbers
• 3.9.1. Conversion of Infix Expressions to Prefix and Postfix
• 3.9.2. General Infix-to-Postfix Conversion
• 3.9.3. Postfix Evaluation
• 3.10. What Is a Queue?
• 3.11. The Queue Abstract Data Type
• 3.12. Implementing a Queue in Python
• 3.13. Simulation: Hot Potato
• 3.14.1. Main Simulation Steps
• 3.14.2. Python Implementation
• 3.14.3. Discussion
• 3.15. What Is a Deque?
• 3.16. The Deque Abstract Data Type
• 3.17. Implementing a Deque in Python
• 3.18. Palindrome-Checker
• 3.19. Lists
• 3.20. The Unordered List Abstract Data Type
• 3.21.1. The Node Class
• 3.21.2. The Unordered List Class
• 3.22. The Ordered List Abstract Data Type
• 3.23.1. Analysis of Linked Lists
• 3.24. Summary
• 3.25. Key Terms
• 3.26. Discussion Questions
• 3.27. Programming Exercises
• 4.1. Objectives
• 4.2. What Is Recursion?
• 4.3. Calculating the Sum of a List of Numbers
• 4.4. The Three Laws of Recursion
• 4.5. Converting an Integer to a String in Any Base
• 4.6. Stack Frames: Implementing Recursion
• 4.7. Introduction: Visualizing Recursion
• 4.8. Sierpinski Triangle
• 4.9. Complex Recursive Problems
• 4.10. Tower of Hanoi
• 4.11. Exploring a Maze
• 4.12. Dynamic Programming
• 4.13. Summary
• 4.14. Key Terms
• 4.15. Discussion Questions
• 4.16. Glossary
• 4.17. Programming Exercises
• 5.1. Objectives
• 5.2. Searching
• 5.3.1. Analysis of Sequential Search
• 5.4.1. Analysis of Binary Search
• 5.5.1. Hash Functions
• 5.5.2. Collision Resolution
• 5.5.3. Implementing the Map Abstract Data Type
• 5.5.4. Analysis of Hashing
• 5.6. Sorting
• 5.7. The Bubble Sort
• 5.8. The Selection Sort
• 5.9. The Insertion Sort
• 5.10. The Shell Sort
• 5.11. The Merge Sort
• 5.12. The Quick Sort
• 5.13. Summary
• 5.14. Key Terms
• 5.15. Discussion Questions
• 5.16. Programming Exercises
• 6.1. Objectives
• 6.2. Examples of Trees
• 6.3. Vocabulary and Definitions
• 6.4. List of Lists Representation
• 6.5. Nodes and References
• 6.6. Parse Tree
• 6.7. Tree Traversals
• 6.8. Priority Queues with Binary Heaps
• 6.9. Binary Heap Operations
• 6.10.1. The Structure Property
• 6.10.2. The Heap Order Property
• 6.10.3. Heap Operations
• 6.11. Binary Search Trees
• 6.12. Search Tree Operations
• 6.13. Search Tree Implementation
• 6.14. Search Tree Analysis
• 6.15. Balanced Binary Search Trees
• 6.16. AVL Tree Performance
• 6.17. AVL Tree Implementation
• 6.18. Summary of Map ADT Implementations
• 6.19. Summary
• 6.20. Key Terms
• 6.21. Discussion Questions
• 6.22. Programming Exercises
• 7.1. Objectives
• 7.2. Vocabulary and Definitions
• 7.3. The Graph Abstract Data Type
• 7.4. An Adjacency Matrix
• 7.5. An Adjacency List
• 7.6. Implementation
• 7.7. The Word Ladder Problem
• 7.8. Building the Word Ladder Graph
• 7.9. Implementing Breadth First Search
• 7.10. Breadth First Search Analysis
• 7.11. The Knight’s Tour Problem
• 7.12. Building the Knight’s Tour Graph
• 7.13. Implementing Knight’s Tour
• 7.14. Knight’s Tour Analysis
• 7.15. General Depth First Search
• 7.16. Depth First Search Analysis
• 7.17. Topological Sorting
• 7.18. Strongly Connected Components
• 7.19. Shortest Path Problems
• 7.20. Dijkstra’s Algorithm
• 7.21. Analysis of Dijkstra’s Algorithm
• 7.22. Prim’s Spanning Tree Algorithm
• 7.23. Summary
• 7.24. Key Terms
• 7.25. Discussion Questions
• 7.26. Programming Exercises

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

• Module Index
• Search Page

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Problem Solving with Algorithms and Data Structures using Python ¶

By Brad Miller and David Ranum, Luther College

There is a wonderful collection of YouTube videos recorded by Gerry Jenkins to support all of the chapters in this text.

• 1.1. Objectives
• 1.2. Getting Started
• 1.3. What Is Computer Science?
• 1.4. What Is Programming?
• 1.5. Why Study Data Structures and Abstract Data Types?
• 1.6. Why Study Algorithms?
• 1.7. Review of Basic Python
• 1.8.1. Built-in Atomic Data Types
• 1.8.2. Built-in Collection Data Types
• 1.9.1. String Formatting
• 1.10. Control Structures
• 1.11. Exception Handling
• 1.12. Defining Functions
• 1.13.1. A Fraction Class
• 1.13.2. Inheritance: Logic Gates and Circuits
• 1.14. Summary
• 1.15. Key Terms
• 1.16. Discussion Questions
• 1.17. Programming Exercises
• 2.1.1. A Basic implementation of the MSDie class
• 2.2. Making your Class Comparable
• 3.1. Objectives
• 3.2. What Is Algorithm Analysis?
• 3.3. Big-O Notation
• 3.4.1. Solution 1: Checking Off
• 3.4.2. Solution 2: Sort and Compare
• 3.4.3. Solution 3: Brute Force
• 3.4.4. Solution 4: Count and Compare
• 3.5. Performance of Python Data Structures
• 3.7. Dictionaries
• 3.8. Summary
• 3.9. Key Terms
• 3.10. Discussion Questions
• 3.11. Programming Exercises
• 4.1. Objectives
• 4.2. What Are Linear Structures?
• 4.3. What is a Stack?
• 4.4. The Stack Abstract Data Type
• 4.5. Implementing a Stack in Python
• 4.6. Simple Balanced Parentheses
• 4.7. Balanced Symbols (A General Case)
• 4.8. Converting Decimal Numbers to Binary Numbers
• 4.9.1. Conversion of Infix Expressions to Prefix and Postfix
• 4.9.2. General Infix-to-Postfix Conversion
• 4.9.3. Postfix Evaluation
• 4.10. What Is a Queue?
• 4.11. The Queue Abstract Data Type
• 4.12. Implementing a Queue in Python
• 4.13. Simulation: Hot Potato
• 4.14.1. Main Simulation Steps
• 4.14.2. Python Implementation
• 4.14.3. Discussion
• 4.15. What Is a Deque?
• 4.16. The Deque Abstract Data Type
• 4.17. Implementing a Deque in Python
• 4.18. Palindrome-Checker
• 4.19. Lists
• 4.20. The Unordered List Abstract Data Type
• 4.21.1. The Node Class
• 4.21.2. The Unordered List Class
• 4.22. The Ordered List Abstract Data Type
• 4.23.1. Analysis of Linked Lists
• 4.24. Summary
• 4.25. Key Terms
• 4.26. Discussion Questions
• 4.27. Programming Exercises
• 5.1. Objectives
• 5.2. What Is Recursion?
• 5.3. Calculating the Sum of a List of Numbers
• 5.4. The Three Laws of Recursion
• 5.5. Converting an Integer to a String in Any Base
• 5.6. Stack Frames: Implementing Recursion
• 5.7. Introduction: Visualizing Recursion
• 5.8. Sierpinski Triangle
• 5.9. Complex Recursive Problems
• 5.10. Tower of Hanoi
• 5.11. Exploring a Maze
• 5.12. Dynamic Programming
• 5.13. Summary
• 5.14. Key Terms
• 5.15. Discussion Questions
• 5.16. Glossary
• 5.17. Programming Exercises
• 6.1. Objectives
• 6.2. Searching
• 6.3.1. Analysis of Sequential Search
• 6.4.1. Analysis of Binary Search
• 6.5.1. Hash Functions
• 6.5.2. Collision Resolution
• 6.5.3. Implementing the Map Abstract Data Type
• 6.5.4. Analysis of Hashing
• 6.6. Sorting
• 6.7. The Bubble Sort
• 6.8. The Selection Sort
• 6.9. The Insertion Sort
• 6.10. The Shell Sort
• 6.11. The Merge Sort
• 6.12. The Quick Sort
• 6.13. Summary
• 6.14. Key Terms
• 6.15. Discussion Questions
• 6.16. Programming Exercises
• 7.1. Objectives
• 7.2. Examples of Trees
• 7.3. Vocabulary and Definitions
• 7.4. List of Lists Representation
• 7.5. Nodes and References
• 7.6. Parse Tree
• 7.7. Tree Traversals
• 7.8. Priority Queues with Binary Heaps
• 7.9. Binary Heap Operations
• 7.10.1. The Structure Property
• 7.10.2. The Heap Order Property
• 7.10.3. Heap Operations
• 7.11. Binary Search Trees
• 7.12. Search Tree Operations
• 7.13. Search Tree Implementation
• 7.14. Search Tree Analysis
• 7.15. Balanced Binary Search Trees
• 7.16. AVL Tree Performance
• 7.17. AVL Tree Implementation
• 7.18. Summary of Map ADT Implementations
• 7.19. Summary
• 7.20. Key Terms
• 7.21. Discussion Questions
• 7.22. Programming Exercises
• 8.1. Objectives
• 8.2. Vocabulary and Definitions
• 8.3. The Graph Abstract Data Type
• 8.4. An Adjacency Matrix
• 8.5. An Adjacency List
• 8.6. Implementation
• 8.7. The Word Ladder Problem
• 8.8. Building the Word Ladder Graph
• 8.9. Implementing Breadth First Search
• 8.10. Breadth First Search Analysis
• 8.11. The Knight’s Tour Problem
• 8.12. Building the Knight’s Tour Graph
• 8.13. Implementing Knight’s Tour
• 8.14. Knight’s Tour Analysis
• 8.15. General Depth First Search
• 8.16. Depth First Search Analysis
• 8.17. Topological Sorting
• 8.18. Strongly Connected Components
• 8.19. Shortest Path Problems
• 8.20. Dijkstra’s Algorithm
• 8.21. Analysis of Dijkstra’s Algorithm
• 8.22. Prim’s Spanning Tree Algorithm
• 8.23. Summary
• 8.24. Key Terms
• 8.25. Discussion Questions
• 8.26. Programming Exercises

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

Search Page

DEV Community

Posted on Jul 8, 2023

Mastering Data Structures and Algorithms in Python: A Step-by-Step Tutorial

Introduction.

Data structures and algorithms are fundamental concepts in computer science that enable efficient and organized data storage and manipulation. In this beginner's guide, we will explore the basics of data structures and algorithms in Python, providing a detailed explanation and code snippets to illustrate their implementation and usage.

Data Structures

Lists are versatile data structures used to store collections of items. They can hold various data types and allow for dynamic resizing. Lists provide methods for appending, inserting, and removing elements. Example:

Lists also support indexing and slicing, allowing you to access and manipulate specific elements or subsets of elements efficiently.

Dictionaries:

Dictionaries store data as key-value pairs, allowing for fast lookup and retrieval based on unique keys. Dictionaries are commonly used for mapping and associating data. Example:

Dictionaries support operations like adding, modifying, and deleting key-value pairs. They also provide methods to access keys, values, and both keys and values.

Sets are unordered collections of unique elements. They are useful for operations like finding intersections, unions, and differences between sets. Sets can be created using curly braces or the set() constructor. Example:

Sets provide methods for common set operations like adding elements, removing elements, and performing mathematical set operations like union, intersection, and difference.

Linear Search:

Linear search is a simple algorithm used to find the position of a value in a list. It sequentially checks each element until a match is found. Linear search has a time complexity of O(n), where n is the number of elements in the list. Example:

Linear search is straightforward to implement but may not be efficient for large lists.

Bubble Sort:

Bubble sort is a basic sorting algorithm that repeatedly compares adjacent elements and swaps them if they are in the wrong order. It iterates through the list multiple times until the list is sorted. Bubble sort has a time complexity of O(n^2), making it inefficient for large lists. Example:

Bubble sort is easy to understand and implement but should be avoided for large datasets due to its inefficiency.

Binary Search:

Binary search is an efficient algorithm for finding a target value in a sorted list. It repeatedly divides the search space in half until the target is found or determined to be absent. Binary search has a time complexity of O(log n), where n is the number of elements in the sorted list. Example:

Binary search is efficient for sorted lists and allows for significant reductions in search space at each step.

Data structures and algorithms are essential tools for organizing and manipulating data efficiently. In this beginner's guide, we explored basic data structures such as lists, dictionaries, and sets, along with fundamental algorithms like linear search, bubble sort, and binary search in Python.

Remember, this guide only scratches the surface of data structures and algorithms. There are numerous other data structures like stacks, queues, trees, and graphs, as well as advanced algorithms like merge sort, quicksort, and dynamic programming. Exploring these concepts further will empower you to solve complex problems and develop efficient solutions.

Continuously practice and enhance your understanding of data structures and algorithms. As you progress, explore more advanced topics and algorithms to expand your programming repertoire and problem-solving abilities.

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Data Structures and Algorithms in Python - Full Course for Beginners

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Introduction. Binary Search Linked Lists and Complexity. Introduction. Problem. The Method. Solution. Complexity and Big O notation. Binary Search vs Linear Search. Generic Binary Search. Summary and Conclusion. Assignment Walkthrough. Introduction. Problem- Rotated Lists. The Method. Solution. Summary and Conclusion. Binary Search Trees Python Tutorial. Introduction. Problem. The Method. Binary tree. Traversing Binary Tree. Binary Search Tree. Self-Balancing Binary Trees and AVL Trees. Summary and Conclusion. Hash Tables and Python Dictionaries. Introduction. Problem. Data List. Hash Function. Basic Hash Table Implementation. Handling Collisions with Linear Probing. Summary and Conclusion. Sorting Algorithms and Divide & Conquer. Introduction. Problem. The Method. Custom Comparison Functions. Summary and Conclusion. Recursion Memoization & Dynamic Programming. Introduction. Problem. The Method. Solution. Knapsack Problems. The Method. Solution. Summary and Conclusion. Graph Algorithms BFS, DFS & Shortest Paths. Introduction. Graph Data Structure. Graph Algorithms - Breadth-First Search. Depth-First Search. Shortest Paths. Summary and Conclusion. Python Interview Questions Tips & Advice. Introduction. The Method. Solution. Summary and Conclusion.

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Data Structures & Algorithms in Python for Effective Problem Solving

Particularly in Python, a language renowned for its simplicity and power, understanding and utilizing these tools is essential for tackling complex data challenges. This article delves into the intricate world of data structures and algorithms, tailored specifically for Python. We will explore from fundamental concepts to advanced implementations, underscoring their practical applications in real-world scenarios.

With a focus on both efficiency and practicality, this guide is an indispensable resource for data engineers looking to deepen their Python expertise and elevate their problem-solving acumen.

Fundamental Data Structures in Python

Data structures are the building blocks of efficient programming. In Python , the primary structures include Arrays, Stacks, Queues, and Linked Lists. Each serves a unique purpose and is chosen based on the specific requirements of the data operation.

An array is a collection of items stored at contiguous memory locations. In Python, arrays are dynamic, allowing for ease of modification. They are ideal for storing data that is accessed sequentially or randomly, where the index plays a crucial role in the quick retrieval of data.

Stacks and queues are linear structures that differ in how elements are inserted and removed. A stack follows the Last In First Out (LIFO) principle, making it ideal for tasks where the most recent data is required first. In contrast, a queue uses the First In First Out (FIFO) method, applicable in scenarios like task scheduling.

Python Implementation:

A linked list is a sequence of nodes where each node is connected to the next node. It offers flexibility in memory utilization and is ideal for applications where the size of the data set changes frequently.

Comparative Analysis:

• Arrays are fast for index-based operations but slow for insertions and deletions.
• Stacks excel in backtracking algorithms.
• Queues are essential for breadth-first search in graphs.
• Linked Lists offer dynamic memory allocation but increased time complexity for direct access operations.

Exploring Python Algorithms :

• Algorithms are the procedures or formulas for solving a problem. Python core algorithms include sorting and searching, each with its unique applications and efficiencies.
• Quick Sort: A divide-and-conquer algorithm that picks an element as a pivot and partitions the array around the pivot. It’s efficient for large datasets.

Merge Sort: Also a divide-and-conquer algorithm, merge sort divides the array into halves, sorts them, and then merges them. It’s stable and efficient for large datasets.

Binary Search: An efficient algorithm for finding an item from a sorted list of items. It works by repeatedly dividing in half the portion of the list that could contain the item until you’ve narrowed down the possible locations to just one.

Linear Search: A simple method that checks every element until the desired element is found or the list ends. It’s straightforward but less efficient for large datasets.

Discuss the importance of understanding algorithmic complexity, especially Big O notation, to evaluate the efficiency of an algorithm. Emphasize how this knowledge is crucial in choosing the right algorithm for a data engineering task.

Advanced Data Structures for Complex Problems

Trees are hierarchical data structures essential for representing relationships and hierarchies.

Binary Trees – A tree in which each node has up to two children. Useful in various applications, including binary search trees and heap data structures.

Graphs are collections of nodes connected by edges, instrumental in representing networks like social networks or transportation systems.

Types of Graphs – Directed vs. Undirected Graphs.

Applications –  Implementing algorithms like Dijkstra’s for shortest-path problems.

Hash tables store key-value pairs and are known for their fast retrieval and insertion operations.

Python’s Implementation: Using dictionaries as a form of hash tables.

Algorithm Optimization Techniques

Understanding Algorithm Complexity

A fundamental step in optimization is to grasp the time and space complexities of algorithms, typically expressed in Big O notation. This understanding is crucial for predicting how an algorithm scales with the size of the input and identifying potential performance bottlenecks. Alongside this, comparing different algorithms for the same task can reveal insights into which algorithm is more efficient under specific conditions.

Efficient Use of Data Structures

The efficiency of an algorithm is often tied to the choice of data structures. For example, dictionaries in Python are optimal for fast lookups, while other structures might be better suited for different types of operations. Additionally, considering space-time trade-offs is important, as sometimes using additional memory, like caching results, can significantly reduce the time complexity of an algorithm.

Code Level Optimization

This involves refining the code for efficiency. Critical improvements can be made by minimizing operations within loops, using Python’s efficient constructs where possible, and avoiding redundant computations. Techniques like memoization, which involve caching the results of expensive function calls, can greatly improve the performance of recursive algorithms.

Employing Algorithmic Techniques

Techniques such as divide and conquer, which involve breaking a problem into smaller sub-problems, solving them independently, and combining their results, can often be more efficient than tackling the problem as a whole. Similarly, dynamic programming, which solves complex problems by breaking them down into simpler subproblems and storing the results to avoid redundant calculations, can significantly enhance performance.

Profiling and Benchmarking for Optimization

Tools like Python’s cProfile or timeit module are invaluable for identifying parts of the code that are time or memory intensive. Regular testing of the code with various inputs ensures that optimizations improve performance as intended and do not introduce new issues.

Leveraging Parallel Processing and Concurrency

With multi-core processors, distributing tasks across multiple cores can greatly improve performance. For I/O bound tasks, asynchronous programming can help run multiple operations concurrently, leading to more efficient use of resources.

Utilizing Efficient Libraries and Algorithms

Python’s ecosystem is rich with optimized libraries like NumPy or Pandas , which can be much more efficient for certain operations than custom code. Additionally, the choice of algorithm can greatly impact performance; for example, quick sort can be substantially faster than bubble sort for sorting operations.

Maintaining Code Quality

While optimizing, it’s crucial to ensure that the code remains readable and maintainable. Obscure optimizations that compromise the clarity of the code should be avoided. Documenting the reasons and methods behind specific optimizations is important for future maintenance and understanding of the code.

From the fundamental data structures like arrays, queues, and linked lists, to the more intricate constructs such as trees, graphs, and hash tables, we’ve seen how Python’s versatile toolkit can be adapted to various problem-solving contexts.

Through practical examples and detailed discussions, we aimed to not only impart theoretical knowledge but also provide hands-on insights. This synthesis of theory and practice is crucial in the realm of data engineering, where abstract concepts often need to be translated into tangible solutions.

Whether you’re preparing for tech interviews or seeking to enhance your problem-solving skills, our Python Data Engineer Interview course offers the perfect program to achieve your goals. Sign up now!

Chris Garzon

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and an asset management start up where he was responsible for building the entire Data Infrastructure from scratch. He is the author “Ace the Data Engineer Interview” and has helped 100’s of students break into the data engineer industry. He is also an angel investor, an advisor to multiple to multiple start ups, and the founder and CEO of Data Engineer Academy.

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Learn Algorithms and Data Structures in Python

Algorithms and data structures are important for most programmers to understand.

We just released a course on the freeCodeCamp YouTube channel that is a beginner-friendly introduction to common data structures (linked lists, stacks, queues, graphs) and algorithms (search, sorting, recursion, dynamic programming) in Python.

This course will help you prepare for coding interviews and assessments. In this course, you will:

• Watch live hands-on coding-focused video tutorials
• Practice coding with cloud Jupyter notebooks
• Solve questions from real programming interviews

Aakash N S teaches this course. He is the co-founder and CEO of Jovian and has created many popular courses about machine learning and programming.

The course is broken up into a series of lessons, assignments, and projects. There are Jupyter Notebook files to go along with each section.

Here is what is covered in the course:

Lesson 1 - Binary Search, Linked Lists and Complexity

• Linear and Binary Search
• Complexity and Big O Notation
• Linked Lists using Python Classes

Assignment 1 - Binary Search Practice

• Understand and solve a problem systematically
• Implement linear search and analyze it
• Optimize the solution using binary search

Lesson 2 - Binary Search Trees, Traversals and Recursion

• Binary trees, traversals, and recursion
• Binary search trees & common operations
• Balanced binary trees and optimizations

Assignment 2 - Hash Tables and Python Dictionaries

• Hash tables from scratch in Python
• Handling collisions using linear probing
• Replicating Python dictionaries

Lesson 3 - Sorting Algorithms and Divide & Conquer

• Bubble sort and Insertion Sort
• Merge sort using Divide & Conquer
• Quicksort and average complexity

Assignment 3 - Divide and Conquer Practice

• Implement polynomial multiplication
• Optimize using divide and conquer
• Analyze time and space complexity

Lesson 4 - Recursion and Dynamic Programming

• Recursion and memoization
• Subsequence and knapsack problems
• Backtracking and pruning

Lesson 5 - Graph Algorithms (BFS, DFS & Shortest Paths)

• Graphs, trees, and adjacency lists
• Breadth-first and depth-first search
• Shortest paths and directed graphs

Project - Step-by-Step Solution to a Programming Problem

• Pick an interesting coding problem
• Solve the problem step-by-step
• Document and present the solution

Lesson 6 - Python Interview Questions, Tips & Advice

• Practice questions and solutions
• Tips for solving coding challenges
• Advice for cracking coding interviews

Watch the course below or on the freeCodeCamp.org YouTube channel (13-hour watch).

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Learn Data Structures and Algorithms with Python

Learn what data structures and algorithms are, why they are useful, and how you can use them effectively in Python.

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At the backbone of every program or piece of software are two entities: data and algorithms. Algorithms transform data into something a program can effectively use. Therefore, it is important to understand how to structure data so algorithms can maintain, utilize, and iterate through data quickly.

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Take your first steps into data structures and algorithms in Python!

Learn about nodes, the building-block data structure.

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Practice projects, assessments, certificate of completion.

Data Structures and Algorithms in Python

This course is a beginner-friendly introduction to common data structures (linked lists, stacks, queues, graphs) and algorithms (search, sorting, recursion, dynamic programming) in Python. This course will help you ace coding assessments and technical interviews.

• Watch live hands-on coding-focused video tutorials
• Practice coding with cloud Jupyter notebooks
• Solve questions from real programming interviews
• Earn a verified certificate of accomplishment

Lesson 1 - Binary Search, Linked Lists and Complexity Preview

• Linear and Binary Search
• Complexity and Big O Notation
• Linked Lists using Python Classes

Assignment 1 - Binary Search Practice Preview

• Understand and solve a problem systematically
• Implement linear search and analyze it
• Optimize the solution using binary search

Lesson 2 - Binary Search Trees, Traversals and Recursion Preview

• Binary trees, traversals, and recursion
• Binary search trees & common operations
• Balanced binary trees and optimizations

Assignment 2 - Hash Tables and Python Dictionaries

• Hash tables from scratch in Python
• Handling collisions using linear probing
• Replicating Python dictionaries

Lesson 3 - Sorting Algorithms and Divide & Conquer

• Bubble sort and Insertion Sort
• Merge sort using Divide & Conquer
• Quicksort and average complexity

Assignment 3 - Divide and Conquer Practice

• Implement polynomial multiplication
• Optimize using divide and conquer
• Analyze time and space complexity

Lesson 4 - Recursion and Dynamic Programming

• Recursion and memoization
• Subsequence and knapsack problems
• Backtracking and pruning

Lesson 5 - Graph Algorithms (BFS, DFS & Shortest Paths)

• Graphs, trees, and adjacency lists
• Breadth-first and depth-first search
• Shortest paths and directed graphs

Project - Solve a Programming Problem Step-by-Step

• Pick an interesting coding problem
• Solve the problem step-by-step
• Document and present the solution

Lesson 6 - Python Interview Questions, Tips & Advice

• Practice questions and solutions
• Tips for solving coding challenges
• Advice for cracking coding interviews

Certificate of Accomplishment

Earn a verified certificate of accomplishment ( sample ) by completing all weekly assignments. The certificate can be added to your LinkedIn profile, linked from your Resume, and downloaded as a PDF.

Instructor - Aakash N S

Aakash N S is the co-founder and CEO of Jovian . Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.

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Python version, reading time [], publication year.

Grokking Algorithms: An illustrated guide for programmers and other curious people

By aditya bhargava.

The Recursive Book of Recursion: Ace the Coding Interview with Python and JavaScript

By al sweigart.

The Self-Taught Computer Scientist: The Beginner's Guide to Data Structures & Algorithms

By cory althoff.

Hands-On Data Structures and Algorithms with Python: Store, manipulate, and access data effectively and boost the performance of your applications

By dr. basant agarwal.

Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner

By bradford tuckfield.

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By christian hill.

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By john canning.

50 Algorithms Every Programmer Should Know: An unbeatable arsenal of algorithmic solutions for real-world problems

By imran ahmad.

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By gonczarowski yannai a..

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By kenneth lambert.

Data Structure and Algorithmic Thinking with Python: Data Structure and Algorithmic Puzzles

By narasimha karumanchi.

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By jeremy g. siek.

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Most Asked Problems in Data Structures and Algorithms | Beginner DSA Sheet

In this Beginner DSA Sheet for Data Structures and Algorithms, we have curated a selective list of problems for you to solve as a beginner for DSA. After learning the fundamentals of programming, choosing a programming language, and learning about Data Structure and Algorithms and their space-time complexity, it becomes necessary to practice the problem based on different data structures and algorithms. 

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The problem on the sheet includes:

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Improving the artificial bee colony algorithm with a proprietary estimation of distribution mechanism for protein–ligand docking

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The protein–ligand docking problem plays a crucial role in the drug discovery process and remains challenging in bioinformatics. A successful protein–ligand docking approach depends on two key factors: an efficient search strategy and ...

Protein-ligand docking based on beta-shape

Protein-ligand docking is to predict the location and orientation of a ligand with respect to a protein within its binding site, and has been known to be essential for the development of new drugs. The protein-ligand docking problem is usually ...

A biased random key genetic algorithm for the protein---ligand docking problem

Molecular docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. ...

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