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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.

53 Lessons

25h

Also available in

C++

Java

JavaScript

Python

Also available in

JavaScript

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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

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

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.

Recursive Numbers

12 Lessons

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

Longest Common Substring

16 Lessons

Deepen your knowledge of solving various substring and subsequence problems using dynamic programming.

Palindromic Subsequence

6 Lessons

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

Conclusion

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

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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.

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