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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.

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.

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This course includes

133 Playgrounds

44 Challenges

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Course Overview

What You'll Learn

Course Content

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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

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

Course Content

Getting Started

3 Lessons

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

9 Lessons

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.

Introduction to Unbounded Knapsack

Solving the Unbounded Knapsack Problem

Recursive Numbers

12 Lessons

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

Introduction to Recursive Numbers

Fibonacci Numbers

Staircase Problem

Number Factors

Count Ways to Score in a Game

Unique Paths to Goal

Nth Tribonacci Number

The Catalan Numbers

House Thief Problem

Minimum Jumps to Reach the End

Minimum Jumps With Fee

Matrix Chain Multiplication

Longest Common Substring

16 Lessons

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

Introduction to Longest Common Substring

Solving the Longest Common Substring Problem

Longest Common Subsequence

Shortest Common Supersequence

Minimum Number of Deletions and Insertions

Edit Distance Problem

Longest Repeating Subsequence

Distinct Subsequence Pattern Matching

Interleaving Strings

Word Break II

Longest Increasing Subsequence

Minimum Deletions to Make a Sequence Sorted

Maximum Sum Increasing Subsequence

Longest Bitonic Subsequence

Longest Alternating Subsequence

Building Bridges

Palindromic Subsequence

6 Lessons

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

Introduction to Palindromic Subsequence

Longest Palindromic Subsequence

Minimum Deletions in a String to make it a Palindrome

Longest Palindromic Substring

Count of Palindromic Substrings

Palindromic Partitioning

Conclusion

1 Lessons

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

Where to Go from Here?

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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?

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.