Is DSA 100 Days better than LeetCode?

DSA 100 Days offers unlimited AI tutoring and mock interviews at $59/year, compared to LeetCode Premium at $159/year without AI features. It's designed for focused 100-day preparation rather than overwhelming you with thousands of problems.

How long does it take to prepare for coding interviews?

With DSA 100 Days' structured curriculum, most users complete their preparation in 100 days, solving one problem per day. The AI tutor helps you understand concepts faster than self-study.

What programming languages are supported?

DSA 100 Days supports Python, JavaScript, TypeScript, Java, C++, and Go with in-browser code execution and AI tutoring in all languages.

Dynamic Programming for Beginners: From Zero to Interview Ready

Why Dynamic Programming Scares People

Dynamic Programming (DP) has a reputation for being the hardest topic in coding interviews. But here's the truth: DP is just a systematic way of breaking problems into smaller subproblems. Once you see the pattern, it clicks.

The Core Idea

DP works when a problem has:

**Optimal Substructure** - The optimal solution contains optimal solutions to subproblems

**Overlapping Subproblems** - The same subproblems are solved multiple times

Instead of solving the same subproblem repeatedly, we store results and reuse them.

The Two Approaches

Top-Down (Memoization)

Start with the main problem and recursively break it down. Cache results.

def fib(n, memo={}):

if n in memo:

return memo[n]

if n <= 1:

return n

memo[n] = fib(n-1, memo) + fib(n-2, memo)

return memo[n]

Bottom-Up (Tabulation)

Start with the smallest subproblems and build up to the main problem.

def fib(n):

if n <= 1:

return n

dp = [0] * (n + 1)

dp[1] = 1

for i in range(2, n + 1):

dp[i] = dp[i-1] + dp[i-2]

return dp[n]

The 5-Step DP Framework

**Define the state** - What does dp[i] represent?

**Define the recurrence relation** - How do we get dp[i] from smaller subproblems?

**Define base cases** - What are the smallest subproblems?

**Define the answer** - Where is our final answer?

**Optimize space** (optional) - Can we reduce from O(n) to O(1)?

Classic DP Problems to Master

Level 1: 1D DP

Climbing Stairs

House Robber

Maximum Subarray

Level 2: 2D DP

Unique Paths

Longest Common Subsequence

Edit Distance

Level 3: Advanced

Coin Change

Longest Increasing Subsequence

Partition Equal Subset Sum

The Secret: Pattern Recognition

After solving 20-30 DP problems, you'll start recognizing patterns:

Is this a knapsack problem?

Is this LCS/LIS?

Is this a grid traversal?

Is this a decision problem (take or leave)?

Practice Strategy

**Week 1**: Master 1D DP (5-7 problems)

**Week 2**: Master 2D DP (5-7 problems)

**Week 3**: Mixed practice and optimization

Don't move on until you can solve problems without looking at solutions.

Get AI Help When Stuck

The hardest part of DP is identifying the recurrence relation. Our AI tutor can give you hints that guide you to the insight without spoiling the solution. [Try it free](/signup).