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Master dynamic programming techniques for coding interviews through practical patterns and solutions in Java.

Intermediate

53 Lessons

25h

Certificate of Completion

Master dynamic programming techniques for coding interviews through practical patterns and solutions in Java.

AI-POWERED

Code Feedback

Mock Interview

Explanations

AI-POWERED

Code Feedback

Mock Interview

This course includes

133 Playgrounds

44 Challenges

This course includes

133 Playgrounds

44 Challenges

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

What You'll Learn

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

Course Content

Getting Started

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

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

Introduction to Unbounded Knapsack Solving the Unbounded Knapsack Problem Maximum Ribbon Cut Rod Cutting Minimum Coin Change Coin Change II

Recursive Numbers

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

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

Conclusion

1 Lesson

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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.