Overview

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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. In this course, you will master advanced problem-solving techniques and learn to approach algorithmic challenges with a structured mindset. By covering key techniques like dynamic programming, greedy algorithms, and graph theory, this course will equip you with the skills to solve complex problems with confidence. You'll learn how to break down large problems into smaller subproblems and optimize your solutions for efficiency and scalability. The course starts with an in-depth exploration of dynamic programming, where you will implement various problems and learn how to select the most effective strategies for solving them. Then, you'll move into greedy algorithms and their applications, followed by a deep dive into graph algorithms. The course covers graph traversal methods like BFS and DFS, as well as more complex topics like Dijkstra's algorithm, Bellman-Ford, and minimum spanning trees. Each section is accompanied by practical problems to reinforce your learning. As you progress, you'll develop critical thinking skills and a deep understanding of algorithmic principles. This course encourages hands-on problem-solving, with each problem designed to stretch your ability to approach problems from multiple angles. You'll gain practical experience in coding and problem-solving, ensuring you're well-prepared for real-world technical challenges. The course is ideal for those who already have basic programming knowledge and want to deepen their understanding of algorithms and problem-solving techniques. Whether you’re preparing for technical interviews or looking to enhance your algorithmic thinking, this course is tailored to boost your skill set. It is especially suitable for computer science enthusiasts, developers, and those aspiring to excel in technical fields. By the end of the course, you will be able to apply dynamic programming, greedy algorithms, graph traversal methods, and backtracking to solve complex problems, optimize algorithms, and enhance your problem-solving efficiency.

Syllabus

Dynamic Programming

In this module, we will introduce you to dynamic programming as a technique to break complex problems into simpler subproblems. You will learn how to apply both top-down and bottom-up approaches to solve various challenges, optimizing solutions using previously computed results.

Greedy Algorithms

In this module, we will explore the concept of greedy algorithms, a class of algorithms that make locally optimal choices at each step. You will learn to identify when greedy strategies can provide optimal or near-optimal solutions to a problem.

Graphs

In this module, we will dive into the world of graphs, examining their various types and structures. You will learn graph traversal techniques such as BFS and DFS and explore advanced algorithms like Dijkstra’s and Bellman-Ford for shortest paths, as well as Prim’s for minimum spanning trees.

Backtracking

In this module, we will cover the powerful backtracking technique, enabling you to explore all potential solutions to problems by undoing steps when necessary. Through hands-on examples like N Queens and Combination Sum, you will develop the skills to apply backtracking in algorithm design.

Bitmagic

In this module, we will introduce you to bitwise operations, which allow you to manipulate numbers at the bit level. You will learn how to leverage bit magic to solve problems efficiently, including detecting duplicates, reversing integers, and counting bits.