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.

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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...

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

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Course Content

1.

Getting Started

2.

0/1 Knapsack

Introduction to 0/1 KnapsackSolving the 0/1 Knapsack ProblemTarget SumSubset SumCount of Subset SumPartition Array Into Two Arrays to Minimize Sum DifferenceMinimum Number of Refueling StopsEqual Sum SubarraysCount Square Submatrices

3.

Unbounded Knapsack

Introduction to Unbounded KnapsackSolving the Unbounded Knapsack ProblemMaximum Ribbon CutRod CuttingMinimum Coin ChangeCoin Change II

4.

Recursive Numbers

Introduction to Recursive NumbersFibonacci NumbersStaircase ProblemNumber FactorsCount Ways to Score in a GameUnique Paths to GoalNth Tribonacci NumberThe Catalan NumbersHouse Thief ProblemMinimum Jumps to Reach the EndMinimum Jumps With FeeMatrix Chain Multiplication

5.

Longest Common Substring

Introduction to Longest Common SubstringSolving the Longest Common Substring ProblemLongest Common SubsequenceShortest Common SupersequenceMinimum Number of Deletions and InsertionsEdit Distance ProblemLongest Repeating SubsequenceDistinct Subsequence Pattern MatchingInterleaving StringsWord Break IILongest Increasing SubsequenceMinimum Deletions to Make a Sequence SortedMaximum Sum Increasing SubsequenceLongest Bitonic SubsequenceLongest Alternating SubsequenceBuilding Bridges

6.

Palindromic Subsequence

6 Lessons

7.

Conclusion

1 Lesson

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Souvik Kundu

Front-end Developer

Eric Downs

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Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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Frequently Asked Questions

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

How can I practice dynamic programming to improve interviews?

How can I recognize if a problem is suited for dynamic programming?

Why is dynamic programming emphasized in technical interviews?