HomeCoursesGrokking Dynamic Programming Interview in JavaScript

Intermediate

25h

Updated 1 month ago

Grokking Dynamic Programming Interview in JavaScript

Gain insights into dynamic programming in JavaScript with strategies developed by FAANG engineers. Practice with real-world interview questions and get interview-ready in just a few hours.

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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++, Java, and Python—with more coming soon!

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

Content

53 Lessons44 Challenges

Getting Started

3 Lessons

Get familiar with dynamic programming essentials, ideal for coding interviews and runtime efficiency.

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

9 Lessons

Unpack the core of optimizing knapsack problems and similar challenges using dynamic programming.

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

Examine key strategies for solving Unbounded Knapsack, Maximum Ribbon Cut, Rod Cutting, Minimum Coin Change, and Coin Change II problems.

Introduction to Unbounded Knapsack

Solving the Unbounded Knapsack Problem

Recursive Numbers

12 Lessons

Enhance your skills in solving complex recursive problems using dynamic programming techniques.

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 solving various substring and subsequence problems 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

Follow the process of finding, optimizing palindromic subsequences, substrings, and partitioning using dynamic programming.

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

Build on improved problem-solving skills and engage in further algorithm courses.

Where to Go from Here?

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative resource is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who’ve shipped production code at scale and taught the theory behind it. The goal is to get you hands-on with the skills you need to stay ahead in today's constantly evolving tech landscape. No videos, no fluff — just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

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.

Explain with AI

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AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

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Free Resources

Frequently Asked Questions

How can I improve my intuition for solving dynamic programming problems?

To develop a strong intuition for DP problems, start by solving simpler problems like Fibonacci or coin change, focusing on how subproblems overlap. Practice breaking down problems into smaller components and recognize patterns like overlapping subproblems and optimal substructure. Gradually move on to more complex problems and study different DP patterns to build understanding.

Why is dynamic programming considered more efficient than brute-force solutions?

Dynamic programming is more efficient because it eliminates redundant computations by storing the results of subproblems. In brute-force methods, the same subproblems are often recalculated multiple times, leading to exponential time complexity. DP reduces this by solving each subproblem once and using that result whenever needed, cutting the overall time complexity.

How do I choose between memoization and tabulation in a dynamic programming problem?

The choice between memoization and tabulation depends on the problem and the desired approach. If you’re more comfortable with recursion and want to solve the problem top-down, memoization is ideal. Tabulation, however, is preferred when you can iteratively build the solution from base cases up in a bottom-up manner, and it’s often easier to visualize and manage certain problems.

What are some common mistakes to avoid when solving dynamic programming problems?

One common mistake is not correctly identifying overlapping subproblems or optimal substructure, leading to inefficient solutions. Another issue is poor table or memoization setup, such as failing to initialize base cases correctly. Also, forgetting to handle edge cases or ignoring time and space complexity optimizations can lead to suboptimal performance.

How can dynamic programming be applied in real-world applications?

Dynamic programming is used in various real-world applications, such as optimization problems in finance (e.g., portfolio optimization), operations research (e.g., supply chain management), and computer science (e.g., text processing, network routing). It’s particularly useful in problems where decisions must be made sequentially, and resource optimization is critical.