Why Practice on LeetCode – Beginner DSA Problems

If you’re just starting with data structures and algorithms (DSA), the tutorial by Nikhil Kumar in Hindi offers a friendly, beginner-centric walkthrough of solving easy array problems on LeetCode. The emphasis is not on super-optimized code right away—but on building the correct logic, developing a problem-solving habit, and recognizing patterns. Here’s what we learn.

What is LeetCode and Why Use It?

Many learners know how to read about linked lists, stacks, queues, and other data structures—but that alone isn’t enough for job interviews or competitive programming. Practising real problems is essential. In the tutorial, Nikhil stresses:

• DSA practice on array problems builds core habits and intuition.

• Real-world style problems on LeetCode simulate what you’ll face in interviews.

• Begin with “easy” array problems and first get a correct, working solution, then worry about optimization.

• Always read and understand the problem statement and sample input/output before coding.

This approach helps you move beyond theory into applied problem-solving—something many beginners struggle with.

Problem-Solving Approach – Understand → Build → Optimize Later

In the tutorial, you’ll see the sequence:

1. Understand the statement (what is being asked, sample I/O, constraints).

2. Build a working solution (even if brute-force).

3. Optimise later (if needed, once the logic is clear).

Some pointers from this approach:

• Don’t rush to fancy solutions; a simple, correct solution is far better than a wrong, optimized one.

• Use nested loops for brute force (especially when you’re practising) so you understand the flow.

• After you solve, reflect: can I reduce time, space? Are there patterns I can reuse?

• Recognise recurring patterns (two-sum, sliding window, prefix products etc). Once you see them, many problems become easier.

Example Problem 1: Two Sum

Problem: Given array nums and integer target, return indices of two numbers such that they add up to target.
Example: nums = [2,7,11,15], target = 9 → return [0,1].

Brute-force idea:

• Outer loop i from 0 to n-1.

• Inner loop j from i+1 to n-1 (so you don’t pair the same element with itself).

• If nums[i] + nums[j] == target, return [i, j].

• Time complexity O(n²), space O(1) extra (besides output).

• Why j = i+1? Because you avoid pairing i with itself, and you avoid duplicate pairs.

In the tutorial Nikhil walks step-by-step, shows how you pick the loops, check the sum, and return when found. The key is logic clarity, not yet optimization.

Example Problem 2: Best Time to Buy and Sell Stock (one transaction)

Problem: Given array prices where prices[i] is the stock price on day i, find maximum profit from one buy and one sell. If no profit, return 0.
Example: prices = [7,1,5,3,6,4] → max profit = 5 (buy at 1, sell at 6).

Brute-force idea:

• Outer loop over buy day i from 0 to n-2.

• Inner loop over sell day j from i+1 to n-1.

• Compute profit = prices[j] – prices[i].

• Track maxProfit = max(maxProfit, profit), but if negative you just ignore (or treat as 0).

• Complexity O(n²), space O(1) extra.

Again, the tutorial shows how you step through each pair, update the max profit, ignore bad buys/sells. Later you can optimise using O(n) scan tracking min price so far—but first get brute correct.

Example Problem 3: Product of Array Except Self (without division)

Problem: Given nums, return an array answer where answer[i] is the product of all elements except nums[i]. You may not use division.
Example: nums = [1,2,3,4] → answer = [24,12,8,6].

Brute-force idea:

• For each index i, initialise product = 1.

• Loop j from 0 to n-1:

  • If j == i, skip (use continue).

  • Else product *= nums[j].

• Set answer[i] = product.

• Time complexity O(n²), space O(n) for answer (plus O(1) extra).

• Instructor emphasises using product = 1 (not zero) so you don’t nullify things.

The tutorial shows that this brute approach correctly builds the logic. Later you can do prefix & suffix products to optimise to O(n) time & O(1) extra space (excluding the answer). But understanding brute is crucial.

Key Teaching Style & Advice

• Start with a correct, simple solution (even if brute-force) to ensure you understand.

• Then think: can I reduce complexity/time/space?

• Recognise problem-patterns: many easy problems are essentially “compare two elements”, “aggregate with loop”, “prefix/suffix”.

• Practice consistently — using platforms like LeetCode regularly builds intuition.

• Always read problem statement fully, understand sample I/O, edge cases, constraints.

• Code slowly, trace with example input to verify logic.

• Over time you’ll notice that many problems reduce to a small set of patterns (two-pointers, sliding window, hash-map two sum, prefix sum etc).

Why This Video Matters for Beginner DSA Learners

If you’re new to DSA and coding interviews, this tutorial is helpful because:

• It uses Hindi language, making it accessible for many Indian learners.

• It emphasises logic over optimization—which many beginners skip and end up stuck because they try advanced methods before understanding basics.

• It covers array problems, which are foundational and appear often in interviews.

• It aligns with the “first solve, then optimise” mindset—a sustainable growth path.

Skills For Everyone — Growing Your Problem-Solving & Tech Career

Let’s broaden from just solving LeetCode problems to skills you need for everyone (especially in tech). And I’ll also mention a platform making this accessible.

What skills should everyone build?

1. Problem-Solving & Logical Thinking

   • Breaking down a statement into input/output, constraints, edge cases.

   • Building a working logic (e.g., loops, conditions) before worrying about fancy methods.

   • Pattern recognition (seeing that “two sum” style appears again in different guises).
     
     

2. Coding & Data Structures/Algorithms Fundamentals

   • Arrays, strings, linked lists, stacks, queues, trees, graphs.

   • Writing clean, bug-free code—especially for simple problems first.

   • Then optimising: time/space trade-offs.

     
     

3. Consistency & Habit Building

   • Regular practice matters more than occasional bursts.

   • Use platforms like LeetCode to keep the habit.

     
     

4. Learning How to Learn

   • Read problem statement fully.

   • Do dry-runs on sample inputs.

   • Trace code manually.

   • After solving, reflect: could I do it faster? Use less memory?
     
     

5. Communication & Code Readability

   • Even if solution works, code should be readable. Comments, meaningful variable names, clarity.

   • In interviews you may have to explain your logic—so verbal clarity matters.
     
     

6. Soft Skills and Growth Mindset

   • Accept that initial solutions will be slow/inefficient—that’s OK. You will improve.

   • Be curious to optimise further or understand why better solutions work.

   • Resilience: you’ll see hard problems; keep going.

How Platforms Help — Example: Skills for Everyone

The platform Skills for Everyone offers online courses in web development, cloud computing, cyber security, data science, digital marketing and more.
Key features of such platforms:

• Industry-expert instructors and structured curriculum.

• Hands-on training, practical projects and job-readiness emphasis.

• Affordable access and accessibility (“making education accessible and affordable worldwide”) as stated by Skills for Everyone.

If you’re building DSA skills and want to complement them with full-stack or cloud or other tech skills, such a platform can help broaden your capabilities and make you job-ready.

Conclusion: Build the Habit, Grow the Skills

To wrap up, this tutorial by Skills for Everyone is an excellent starting point for anyone looking to build a strong foundation in solving DSA problems on LeetCode. The key takeaways are:

• Don’t skip the basics. Understand the problem, build a correct solution—even if brute.

• Once you’re comfortable, gradually optimise your solutions.

• Practice consistently; recognise patterns across problems.

• Embed wider skills: problem-solving, coding fundamentals, learning habits, and soft skills like communication and resilience.

• Use platforms (like Skills for Everyone) to broaden your tech stack and improve job readiness.