Overview

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 problem-solving skills with advanced techniques in dynamic programming, greedy algorithms, and bit manipulation. This course covers essential strategies like memoization and tabulation in dynamic programming to solve complex problems such as Fibonacci numbers, coin change, and palindromic substrings. You’ll also master greedy algorithms with problems like maximizing subarrays and minimizing the number of boats needed, enhancing your ability to approach optimization problems efficiently. Starting with dynamic programming, you will tackle various problems, from decoding strings to finding the longest increasing subsequence. As you advance, the course dives into greedy algorithms and the efficient merging of intervals to handle overlapping schedules or spaces. The course also explores matrix manipulations and bit-level operations, enabling you to solve problems related to bit counting, missing numbers, and binary representations. Perfect for those with a solid understanding of algorithms, this course prepares you to solve complex real-world challenges using advanced techniques. By the end, you’ll be equipped to tackle high-level problems that require both creative and optimized solutions. By the end of the course, you will be able to efficiently apply dynamic programming, greedy algorithms, bit manipulation, and matrix operations to solve advanced algorithmic challenges in both interviews and projects.

Syllabus

Dynamic Programming: Memoization / Tabulation

In this module, we will explore dynamic programming techniques, such as memoization and tabulation, to solve common problems. You’ll tackle challenges like the Fibonacci sequence, coin change, palindromic substrings, and more to enhance your problem-solving skills.

Greedy

In this module, we will dive into greedy algorithms, focusing on efficient solutions for problems like minimizing boat usage, maximizing subarray sums, and solving jump game challenges with a greedy approach.

Merge Intervals

In this module, we will explore how to merge and insert intervals, solving common problems such as managing meeting schedules and optimizing room usage by merging and removing overlapping intervals.

Matrix

In this module, we will tackle matrix manipulation problems, including rotating matrices, traversing in spiral order, and handling zeroes in matrices, all using optimized approaches.

Binary: Bit Manipulation

In this module, we will explore bit manipulation techniques to optimize solutions for problems like counting 1 bits, reversing bits, and performing efficient calculations using XOR.

Bonus

In this module, we will provide advanced strategies and additional resources to help solidify your understanding of data structures and algorithms, ensuring you're fully prepared for your interview challenges.