Home>Courses>Grokking Dynamic Programming Interview

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.

Intermediate

53 Lessons

25h

Certificate of Completion

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.
AI-POWERED

Code Feedback

Mock Interview

Explanations

AI-POWERED

Code Feedback

Mock Interview

This course includes

133 Playgrounds
44 Challenges
Learn in a different language:
C++ Courses & Guides
Java Courses & Guides
JavaScript Courses & Guides
Python Courses & Guides
Switch Language
Java Courses & GuidesJava Courses & Guides
Course Overview
What You'll Learn
Course Content
Recommendations

Course Overview

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 pa...Show More
Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an adv...Show More

What You'll Learn

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
A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

Show more

Course Content

1.

Getting Started

3 Lessons

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

3.

Unbounded Knapsack

6 Lessons

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

7.

Conclusion

1 Lessons

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

Trusted by 2.5 million developers working at companies

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Adaptive Learning

Explain with AI

AI Code Mentor

Free Resources

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?

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

Why is dynamic programming emphasized in technical interviews?