Divide and Conquer

Algorithmic Thinking (Part 1)

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".

Algorithms, Part I

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.

Introduction to Algorithms

Learn divide-and-conquer strategies in this Introduction to Algorithms course by MIT's Erik Demaine and Justin Solomon.

advanced🇬🇧

Algorithms, Part I

🌐Websites(5)

scroll for more →

CP-Algorithms

Competitive programming focused D&C explanations

GeeksforGeeks

Comprehensive Divide and Conquer tutorials and problems

geeksforgeeks.org

GeeksforGeeks is a comprehensive computer science learning site offering tutorials, code examples, and practice problems on algorithms and data structures. It features extensive content on topics like Divide and Conquer, dynamic programming, and interview prep across programming languages.

algoexpert.io

AlgoExpert is an interview-prep platform offering curated algorithm problems with comprehensive explanations (including video lessons) and a built-in code editor to practice. It covers topics like divide-and-conquer and other core algorithms and data structures.

leetcode.com

LeetCode is an online platform for practicing coding problems and preparing for technical interviews. It offers a large, searchable problem set with tags (including Divide and Conquer), an interactive code editor, solution explanations, discussion forums, and regular weekly/biweekly contests.