Grokking Dynamic Programming Patterns for Coding Interviews

It is a course that treats dynamic programming as five recurring patterns rather than as a hundred unrelated problems. Each pattern gets a chapter, and each problem inside it is solved three ways, starting from plain recursion, then adding memoization, then rebuilding it bottom-up, so you see exactly where the speedup comes from.

Engineers who can handle most coding interview topics and lose their nerve on dynamic programming. That covers a lot of people: DP is the topic candidates most often skip entirely and then meet in a final round. You should be comfortable with recursion and Big-O before starting.

0/1 knapsack, unbounded knapsack, Fibonacci numbers, palindromic subsequence, and longest common substring. Those five account for the overwhelming majority of dynamic programming questions asked in interviews, which is why the course spends its time going deep on them instead of cataloguing every variant that exists.

Because that progression is the actual skill. In an interview you will almost never leap straight to an optimal bottom-up table. You will write the brute force recursion, spot the repeated subproblems, add memoization, and only then decide whether to invert it. Practising the whole chain means you always have a correct answer on the board, even when the clock beats you to the elegant one.

Among the 35: subset sum, equal subset sum partition, minimum subset sum difference, target sum, coin change and minimum coin change, rod cutting, maximum ribbon cut, staircase, house thief, minimum jumps to reach the end, longest palindromic subsequence and substring, palindromic partitioning, longest common subsequence, edit distance, longest increasing subsequence, shortest common super-sequence, and strings interleaving.

Python, Java, C++, JavaScript, Go, and C#. Every solution is given in all six at each of the three stages, so you can follow the recursion to memoization to tabulation progression in whichever language you will interview in.

No, this course stands on its own, and dynamic programming is self-contained enough that you can take it in isolation. If you are working through both, the natural order is patterns first and this one second, since it goes considerably deeper on DP than a single chapter of a general patterns course can.

Comfort with recursion, arrays, and basic complexity analysis. You do not need any prior exposure to dynamic programming: the course opens by explaining what it is and why overlapping subproblems make it work, before the first pattern begins.

Text-based, with diagrams for the tables and runnable code at each of the three solution stages. That suits DP unusually well, because most of the learning happens while comparing a memoized version against a tabulated one line by line, which is painful to do in a video.

Yes. Design Gurus issues a certificate once you have completed the course, and you can share the link directly or add it to your LinkedIn profile.