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Grokking Dynamic Programming Interview in Python

The ultimate dynamic programming guide by FAANG engineers. Structured prep with real-world DP questions to get interview-ready in hours!

4.9

53 Lessons

25h

Also available in

C++

Java

JavaScript

Python

Also available in

Python

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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, building concepts, and problem-solving skills for coding interviews.

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

Solve challenges with optimized dynamic programming techniques for various combinatorial problems.

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 dynamic programming strategies for optimizing unbounded knapsack, ribbon cut, rod cutting, and coin change problems.

Recursive Numbers

12 Lessons

Enhance your skills in recursive problem-solving with optimized dynamic programming techniques.

Longest Common Substring

16 Lessons

Solve problems in dynamic programming for various string and sequence optimization tasks.

Palindromic Subsequence

6 Lessons

See how it works to find, optimize, and partition palindromic subsequences and substrings.

Conclusion

Gain insights into coding interviews, explore advanced topics, practice, and engage with the community.

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 JavaScript, C++, and Java—with more coming soon!

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Frequently Asked Questions

What is dynamic programming, and why is it important for coding interviews?

Dynamic programming (DP) solves complex problems by breaking them into simpler overlapping subproblems and storing solutions to avoid redundant calculations. It’s important for coding interviews because many optimization and combinatorial problems can be efficiently solved using DP, and interviewers often test candidates on their ability to apply it.

How can I recognize if a problem should be solved using dynamic programming?

Look for problems that involve decision-making with overlapping subproblems or problems that can be broken into smaller, repeatable tasks. Common indicators include terms like “maximum,” “minimum,” “longest,” or “shortest” in the problem description or problems involving subsets, partitions, or sequences.

How can mastering dynamic programming help me in technical interviews?

Mastering DP improves your ability to handle optimization problems and shows interviewers you can solve complex challenges efficiently. Many FAANG and other top-tier companies ask DP questions because they require a combination of logical thinking, optimization, and coding skills.

What is the difference between memoization and tabulation in dynamic programming?

Memoization involves solving a problem recursively and storing the results of subproblems to avoid redundant calculations. Conversely, Tabulation uses an iterative approach to solve the problem and fills up a table from the base case to the final solution. Both techniques are crucial for coding interviews, as different problems may be better suited to one approach.

What’s the best way to explain a dynamic programming solution during an interview?

Start by explaining the problem and the brute-force solution. Then, highlight the inefficiencies and introduce the concept of overlapping subproblems. Finally, explain your dynamic programming approach (memoization or tabulation), emphasizing how it optimizes the solution. Walk through the key steps of your solution clearly while considering edge cases and time complexity.

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