Lesson 1.1: What is DSA?
DSA (Data Structures and Algorithms) is the brain of computer science. It's what gives software the ability to process, store, retrieve, and manipulate data efficiently.
📦 What are Data Structures?
They are methods of organizing and storing data so operations like search, insert, delete, and update can be performed efficiently.
- 🔢 Arrays: Contiguous memory, fast access by index
- 🔗 Linked Lists: Dynamic memory, efficient insertion/deletion
- 🌳 Trees: Hierarchical data models (like file systems)
- 🌐 Graphs: Represent networks, relationships, maps
- 🧩 Hash Tables: Key-value access, used in dictionaries
🔄 What are Algorithms?
Algorithms are defined steps to solve a specific problem — think of them as the "recipes" of computing.
- 🔍 Searching (e.g., binary search)
- 🎲 Sorting (e.g., quick sort, merge sort)
- 🧠 Dynamic Programming (e.g., Fibonacci, knapsack)
- ⛓️ Backtracking (e.g., N-Queens, maze paths)
- 📈 Greedy Techniques (e.g., coin change, scheduling)
📌 Why Is DSA Important?
- 🧠 Helps you think logically and solve real problems
- ⚡ Makes code faster and more efficient
- 💼 Essential for cracking coding interviews
- 🔬 Core of AI, machine learning, and system design
🧑🏫 Who Pioneered DSA?
- Al-Khwarizmi (9th century) — father of algorithms
- Alan Turing — formalized computation and logic
- Donald Knuth — author of "The Art of Computer Programming"
💡 Fun Fact: Your favorite social media platform uses graphs to recommend friends and content!
🧪 Quick Check
- ✅ What’s the difference between a data structure and an algorithm?
- ✅ Which data structure is best for representing a map or network?
🔑 Key Takeaways (Lesson 1.1)
- DSA = Data Structures + Algorithms.
- Data Structures manage data, Algorithms solve problems.
- Used in real-world software like maps, games, and apps.
- Core to interview prep and computer science fundamentals.
- Mastering DSA improves speed, clarity, and efficiency in code.
Lesson 1.2: Time & Space Complexity
How do we measure how good an algorithm is? Two words: Time and Space.
⏱️ Time Complexity
Describes how the execution time grows with input size. It’s about speed.
- O(1) — Constant time
- O(n) — Linear time
- O(log n) — Logarithmic time
- O(n²) — Quadratic time
📦 Space Complexity
Describes how much memory the algorithm uses while running.
- Extra arrays, recursive calls impact space usage
- Ideal algorithms are optimized for both space and time
⚙️ Example: Binary Search
function binarySearch(arr, target) {
let left = 0, right = arr.length - 1;
while (left <= right) {
let mid = Math.floor((left + right) / 2);
if (arr[mid] === target) return mid;
if (arr[mid] < target) left = mid + 1;
else right = mid - 1;
}
return -1;
}
This algorithm cuts the array in half each time → O(log n)
📊 Visual Analogy
- 📚 Searching 100 books one by one = O(n)
- 📖 Using a sorted index = O(log n)
- 🎯 Knowing the exact page = O(1)
🔁 Trade-off: Faster code may use more memory — balance is key!
🧪 Quick Check
- ✅ What is the time complexity of a nested loop?
- ✅ When would you prefer O(n log n) over O(n²)?
🔑 Key Takeaways (Lesson 1.2)
- Time complexity → execution speed; Space complexity → memory usage.
- Big-O notation is used to express performance.
- O(log n) is faster than O(n), especially for large data.
- Choose the most optimal algorithm for your constraints.
Lesson 1.3: Programming Language Tools
To implement DSA concepts, you need to know basic programming constructs that support them. Here's what matters most:
🔢 Arrays
Arrays are collections of elements stored in a continuous block of memory. They allow:
- ⚡ Fast access via index (O(1))
- 📏 Fixed size after creation
let arr = [10, 20, 30];
console.log(arr[1]); // Output: 20
📌 Pointers (C/C++)
Pointers store memory addresses — essential for building linked lists, trees, etc.
int a = 42;
int *p = &a;
printf("%d", *p); // Output: 42
🧱 Classes (Object-Oriented Programming)
Used to model real-world entities and group data + behavior.
class Student {
constructor(name) {
this.name = name;
}
}
const s1 = new Student("Alice");
console.log(s1.name); // Output: Alice
🌐 Common Tools by Language
| Language |
Tools |
| C++ |
Pointers, STL (Vectors, Maps), Classes |
| Java |
Objects, Arrays, Collections (List, Map) |
| Python |
Lists, Dictionaries, Classes |
🧠 Tip: Choose the language that feels comfortable — DSA logic stays the same across languages.
🧪 Quick Check
- ✅ When do you use pointers?
- ✅ What’s the difference between an array and a list?
🔑 Key Takeaways (Lesson 1.3)
- Arrays and lists are foundational tools for DSA.
- Pointers are powerful in low-level memory control (C/C++).
- OOP and classes help structure DSA problems cleanly.
- DSA can be learned in any language — choose your favorite!