Overview

AI, Data Science & Cloud Certificates from Google, IBM & Meta — 50% Off

One plan covers every Professional Certificate on Coursera. 50% off Coursera Plus Annual for 10 days only — price increases June 17.

Unlock All Certificates

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Elevate your algorithmic expertise by mastering advanced data structures and optimization techniques used in high-level problem solving. This course focuses on recursion, trees, heaps, dynamic programming, and graph algorithms, enabling you to design efficient and scalable solutions for complex computational challenges. You will begin with recursion and backtracking, learning how to systematically explore solution spaces through problems like subsets, combination sum, and N-Queens. The course then transitions into binary trees and binary search trees, covering traversal techniques, structural properties, and real-world problem-solving patterns. As you progress, you will explore heaps for priority-based operations and dive deep into dynamic programming to optimize overlapping subproblems. You will also gain insights into greedy strategies, bit manipulation techniques, and graph algorithms, solving problems such as course scheduling and network delay time. This course is designed for learners with a solid foundation in basic data structures and algorithms who want to advance their problem-solving capabilities. It is best suited for intermediate to advanced learners preparing for technical interviews or competitive programming. By the end of the course, you will be able to design optimized algorithms using advanced data structures, apply dynamic programming and graph techniques, and confidently solve complex, real-world coding challenges.

Syllabus

Recursion & Backtracking

In this module, we will introduce recursion and backtracking, powerful techniques used for exploring multiple possibilities in problem-solving. You will learn to implement these strategies to solve problems like generating subsets and finding solutions to the N Queens puzzle.

Binary Trees

In this module, we will dive into binary trees, covering essential concepts such as tree traversals, depth, and balancing. By solving problems like maximum depth and symmetric trees, you'll build a strong foundation in binary tree manipulations.

Binary Search Tree

In this module, we will explore binary search trees (BST), focusing on their unique properties and efficient search methods. You'll learn to implement key BST operations and solve related problems like validating BSTs and finding common ancestors.

Heaps

In this module, we will introduce heaps and their properties, discussing both max-heaps and min-heaps. You’ll practice implementing heaps and using them to solve problems like finding the kth largest element in an array.

Dynamic Programming

In this module, we will dive into dynamic programming techniques, learning how to optimize solutions for problems with overlapping subproblems. You’ll tackle various DP problems like finding the longest increasing subsequence and minimizing costs.

Greedy Algorithms

In this module, we will explore greedy algorithms, focusing on problems where making local optimal choices leads to an overall optimal solution. You’ll understand the power of greedy strategies and apply them to practical problems.

Bit Manipulation

In this module, we will explore bit manipulation techniques, which are crucial for optimizing algorithmic performance. You’ll learn to use bitwise operators to solve problems like dividing integers and finding missing numbers efficiently.

Graphs

In this module, we will dive into graph algorithms, covering techniques for solving problems like counting islands and determining network delay. You’ll learn to apply graph traversal methods to address real-world challenges in various domains.