DSA Introduction

DSA stands for Data Structures and Algorithms. It sounds scary, but you already use both every day. A to-do list is a data structure. “Look through my bag until I find my keys” is an algorithm. This course turns those everyday ideas into tools that make programs fast, and that get you through coding interviews at companies like Google, Amazon and Microsoft.

Two simple definitions#

• A data structure is a way to store and organise data so it is easy to use. Examples: a list, a stack of plates, a phone book, a family tree, a map of cities and roads.
• An algorithm is a step-by-step recipe for solving a problem. Examples: “check every item”, “always look in the middle”, “follow the roads from city to city”.

The right data structure makes the algorithm easy. Finding a word in a dictionary is fast because the words are sorted: you open the middle, see if your word comes before or after, and throw away half the book. That trick has a name, binary search, and you can watch it work right now:

Press Play, then change the numbers and the target and press Visualize. Every pattern in this course has a live animation like this one.

Why DSA matters#

Speed that users can feel#

Searching 1,000,000 sorted items one by one can take up to 1,000,000 checks. Binary search needs 20. The same computer, the same data, a 50,000× difference, just from choosing a better algorithm.

Interviews at almost every tech company#

Big tech companies (Google, Amazon, Microsoft, Meta, Apple, Adobe, Flipkart, Uber and many startups) use DSA problems in interviews because they show how you think: can you break a problem down, choose the right tool, and explain the trade-offs?

Better everyday code#

Knowing that a set checks membership in O(1) while a list needs O(n) changes how you write ordinary programs, not just interview answers.

The secret: patterns, not problems#

There are thousands of practice problems online. Nobody memorises them all, and you don’t need to. Most interview problems are variations of about 15 patterns:

Pattern	Use it when you see…	Lesson
Hash map / set	“Have I seen this before?”, counting, pairs	Hashing
Two pointers	Sorted array, pairs, palindromes	Two Pointers
Sliding window	Longest / shortest contiguous part	Sliding Window
Prefix sums	Many range-sum questions	Prefix Sums
Binary search	Sorted data, “find the smallest x that works”	Binary Search
Stack	Matching brackets, “most recent”	Stacks
Monotonic stack	“Next greater / smaller element”	Monotonic Stack
Linked list tricks	Reverse, merge, find the middle	Linked Lists
Fast & slow pointers	Cycles, middle of a list	Fast & Slow Pointers
Tree DFS / BFS	Anything with a tree	Binary Trees
Graph BFS / DFS	Networks, mazes, grids	Graphs
Heap	“Top k”, “k-th largest”, scheduling	Heaps
Backtracking	“All combinations / permutations”	Backtracking
Dynamic programming	“Number of ways”, “minimum cost” with overlapping choices	Dynamic Programming
Greedy + sorting	Intervals, scheduling	Greedy and Intervals

Learn the pattern once, and you can solve dozens of problems with it.

The 6-step method (use it every time)#

Good problem solvers follow the same steps. Interviewers love to see them out loud.

1. Understand. Repeat the problem in your own words. What is the input? What must you return?
2. Examples. Work through a small example by hand. Add tricky ones: empty input, one item, duplicates, negatives.
3. Brute force. What is the simplest correct solution, even if it is slow? Say its complexity.
4. Optimise. Which pattern removes the wasted work? (This course teaches you to spot it.)
5. Code. Write clean code with good names.
6. Test. Walk through your examples with the code, line by line. Fix bugs. State the final time and space complexity.

A tiny example of the method#

Problem: return True if a list contains any duplicate number.

• Understand: input a list of numbers; output True/False.
• Examples: [1, 2, 3, 1] → True. [1, 2, 3] → False. [] → False.
• Brute force: compare every pair: two loops, O(n²).
• Optimise: “Have I seen this before?” → that’s the hash set pattern: O(n).

def contains_duplicate(nums):
    seen = set()
    for x in nums:
        if x in seen:
            return True
        seen.add(x)
    return False

print(contains_duplicate([1, 2, 3, 1]))  # True
print(contains_duplicate([1, 2, 3]))     # False
print(contains_duplicate([]))            # False

That is the whole journey of this course in miniature: spot the pattern, write a few clean lines.

How this course works#

Every lesson has:

• A real-life analogy so the idea sticks.
• A live visualizer: play it, step through it, and try your own input.
• Python code that you can copy and run. (Python reads almost like English, which keeps the focus on the idea. The same patterns work in Java, C++ or JavaScript.)
• Solved problems, from easy to interview-level.
• An interview corner: where the pattern shows up and what interviewers look for.
• An exercise and a short summary.

What you need first#

• Basic Python: variables, lists, loops, functions and dictionaries. If you are new, do the first 13 lessons of our Python course.
• Patience. Everyone finds DSA hard at first. Every expert was once confused by recursion.

Common mistakes when learning DSA#

• Memorising solutions instead of understanding the pattern. A small twist then breaks everything.
• Jumping straight to code without examples. Most bugs come from skipping steps 1–3.
• Grinding hundreds of random problems. 3–5 problems per pattern, understood deeply, beat 300 rushed ones.
• Ignoring complexity. “It works” is only half an answer in interviews.

Exercise#

1. Write down two data structures and two algorithms you use in daily life.
2. Play the binary search visualizer above with target 21, then with a number that is not in the list. How many steps did each take?
3. Use the 6-step method to write count_evens(nums), returning how many numbers are even. State its complexity.

Summary#

• Data structures store data; algorithms are step-by-step recipes.
• The right choice can make code thousands of times faster.
• About 15 patterns cover most interview problems.
• Use the 6 steps every time: understand, examples, brute force, optimise, code, test.

Next, you’ll learn Big O notation, the language for describing how fast an algorithm is.