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Grokking Dynamic Programming Interview

The ultimate guide to dynamic programming interviews. Developed by FAANG engineers, it equips you with strategic DP skills, practice with real-world questions, and patterns for efficient solutions.

4.6

53 Lessons

25h

Also available in

C++

Java

JavaScript

Python

Also available in

Java

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LEARNING OBJECTIVES

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets
The ability to identify and apply the underlying pattern in an interview question by assessing the problem statement
Familiarity with dynamic programming techniques with hands-on practice in a setup-free coding environment
The ability to efficiently evaluate the tradeoffs between time and space complexity in different solutions
A flexible conceptual framework for solving any dynamic programming question, by connecting problem characteristics and possible solution techniques

Learning Roadmap

53 Lessons44 Challenges

Getting Started

Learn how to use dynamic programming to solve optimization problems efficiently for coding interviews.

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

Grasp the fundamentals of solving optimization problems using dynamic programming techniques.

Introduction to 0/1 Knapsack

Solving the 0/1 Knapsack Problem

Target Sum

Subset Sum

Count of Subset Sum

Partition Array Into Two Arrays to Minimize Sum Difference

Minimum Number of Refueling Stops

Equal Sum Subarrays

Count Square Submatrices

Unbounded Knapsack

6 Lessons

Go hands-on with optimizing recursive problems using dynamic programming for maximum efficiency.

Recursive Numbers

12 Lessons

Grasp the fundamentals of recursive algorithms and optimize with dynamic programming.

Longest Common Substring

16 Lessons

Deepen your knowledge of finding common, repeating, and interleaving substrings using dynamic programming.

Palindromic Subsequence

6 Lessons

See how it works to identify palindromic subsequences, substrings, and efficient partitioning methods.

Conclusion

Piece together the parts of your learning journey and continue improving your skills.

Where to Go from Here?

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

ABOUT THIS COURSE

Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks. This course starts with an introduction to DP and thoroughly discusses five DP patterns. You’ll learn to apply each pattern to several related problems, with a visual representation of the working of the pattern, and learn to appreciate the advantages of DP solutions over naive solutions. After completing this course, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence. This course is also available in C++, JavaScript, and Python—with more coming soon!

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Frequently Asked Questions

What is the difference between memoization and tabulation in dynamic programming?

Memoization is a top-down approach in which recursive calls are made, and solutions to subproblems are stored in memory to prevent redundant calculations. Tabulation, in contrast, is a bottom-up approach in which you iteratively solve subproblems and fill out a table from the base case to the final solution. Both techniques help improve efficiency but are used based on the problem’s nature.

How can I practice dynamic programming to improve interviews?

You can practice dynamic programming problems on LeetCode and Educative. Begin with well-known DP problems such as Fibonacci, 0/1 Knapsack, and Longest Common Subsequence. Solving these basic problems will help you build a foundation before tackling more advanced problems frequently appearing in interviews.

How can I recognize if a problem is suited for dynamic programming?

It likely requires dynamic programming if a problem involves repeated subproblems or recursive calls where solutions are recomputed. Problems asking for “optimal” results, such as maximum profit, shortest path, or longest sequence, indicate that DP may be needed to avoid redundant work and improve efficiency.

Why is dynamic programming emphasized in technical interviews?

Interviewers focus on dynamic programming because it tests your ability to break complex problems into smaller, manageable parts. DP questions challenge candidates to think critically, optimize for efficiency, and use resources effectively—skills highly valued in software engineering roles.

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