NaN.

Learn Data Structures and Algorithms

2703 students enrolled

This course consists of:

12 Tutorials

7 Projects

7 Quizzes

Certificate of Completion

This 26-part course consists of tutorials on data structures and algorithms. It alternates between tutorials and implementation, and you get to implement every algorithm. You can think of this course as a "Free Online Nano Book".

This course teaches algorithms and data structures from the ground-up. It starts with why you need algorithms, and then proceeds to teach you sorting algorithms, dynamic programming, and graph algorithms.

The primary objectives of this course are:

Learn efficient algorithms for sorting and searching, such as merge-sort, quick-sort, and binary search.
Learn problem solving techniques such as recursion and divide-and-conquer.
Learn dynamic programming and solve a variety of dynamic programming problems.
Learn data structures such as heaps and disjoint set data structure.
Learn about graphs and graph algorithms such as graph search algorithms, shortest path algorithms, minimum spanning tree.
Implement each of the learnt algorithms and data structures from scratch in Python, C++, Java or any other programming language of your choice.

Most of the tutorials are a combination of video, text and code! The tutorials have been edited and curated meticulously and are some of the best tutorials on each topic available online.

Syllabus

Why algorithms?

This section introduces the concept of algorithms, and defines what it means when we talk about an algorithm's efficiency.

Why do we need (efficient) algorithms?
9 mins

Sorting algorithms

Sorting algorithms are a set of algorithms that all solve the same problem (sorting) but using very different strategies. These algorithms will introduce you to techniques such as recursion, divide-and-conquer, heaps, etc.

Binary search

Binary search is a search algorithm which runs in O(n \log n) time. It's another example of a divide-and-conquer algorithm, similar to merge-sort.

Dynamic Programming

Dynamic programming is a very versatile method, used for solving a wide variety of problems. We'll introduce dynamic programming by applying it to a problem, and then solve a range of problems using dynamic programming.

Graph Algorithms

In this section, we introduce graphs. Like dynamic programming, graphs are also very versatile. We'll discuss graph search algorithms like breadth-first search and depth-first search, shortest path algorithms like Dijkstra, and minimum spanning tree algorithms such as Prim's and Kruskal's.

Start this course

Why do we need (efficient) algorithms?

80

Copied

Reply

Prerequisites

Beginner level algebra. Any person from high school (class 9) or above, should be able to understand all the material in the course.

Frequently Asked Questions

What does it cost?

Every tutorial on Commonlounge is available for free, and will always be. We only ask you to come back and contribute and make Commonlounge better for everyone once you are an expert. We offer premium content like quizzes, hands-on projects and certificate of completion as part of our Pro Plan. We also offer one-on-one mentorship and feedback on real-world projects from industry-experts as part of our Plus Plan.

When can I start?

Right now. Enroll in this course and follow it at your own pace. You can also set a reminder above to send the next item to your inbox every week.