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

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

Course Content

1.Getting Started

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

2.0/1 Knapsack

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

3.Unbounded Knapsack

Go hands-on with optimizing recursive problems using dynamic programming for maximum efficiency.

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

4.Recursive Numbers

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

5.Longest Common Substring

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

6.Palindromic Subsequence

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

7.Conclusion

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

• Where to Go from Here?