What Are Data Structures?

Data structures are simple ways to organise and store information so it’s easy to find and use.
Example:- lists, tables, and tree.
They're important for building fast and efficient startup products.

Why Are Data Structures Important?

Data structures help you organise information in smart ways, making apps faster, saving memory, and helping your startup scale smoothly as you grow.

Real-World Applications

Data structures power the apps and tools you use every day:

• Social media feeds → Queues, Trees.

• Search engines → Graphs, Hash Tables

• Navigation apps → Graphs (for finding shortest paths)

• Online shopping carts → Arrays, Linked Lists

---

Why Data Structures Matter

1. Efficiency

Good data structures make programs faster and use less memory.
Example:

• Searching for a name in a list of a million entries can be slow.

• But searching in a well-organised tree or hash table can be lightning-fast.

2. Scalability

As the size of your data grows, a bad structure can make a program slow or even unusable.
A good data structure keeps your program running smoothly even with big data.

3. Problem Solving

Many coding problems (especially in interviews) boil down to choosing the right data structure:

• Need to find the shortest path? Use a graph.

• Need to undo actions? Use a stack.

• Need to manage tasks by importance? Use a priority queue.

4. Organisation

Data structures help you organise information logically, making code cleaner, easier to read, and easier to debug.

5. Real-World Applications

Almost everything you use daily depends on the smart use of data structures:

• Social media feeds use queues and heaps.

• Google Maps uses graphs to find routes.

• Databases use trees (like B-trees) to find information quickly.

---

Basic Concepts of Data Structures

1. Space Complexity

• What does it mean:
  How much memory (RAM) does a data structure need to store data?

• Why it matters:
  Some data structures use extra memory to store additional information, like pointers or indices, to make certain operations faster.

• Example:
  A simple array takes less space than a linked list because linked lists store extra references ( "links") for each element.

---

2. Time Complexity

• What does it mean:
  How much time does it take to perform basic operations, such as:

  • Accessing an element

  • Searching for an element

  • Inserting a new element

  • Deleting an element

• How it's measured:
  Using Big O notation ( O(n), O(log n), O(1)), which describes how the performance grows as the data gets bigger.

• Example:-

  • Accessing an element by index in an array is very fast (O(1)).

  • Searching for an item in an unsorted linked list could take longer (O(n)).

---

3. Interface

• What does it mean:
  The set of operations a data structure provides → how you can interact with it.

• Common operations include:

  • Adding elements

  • Removing elements

  • Searching for elements

  • Updating elements

• Example:
  A stack has a simple interface with two main operations:

  • Push (add to the top)

  • Pop (remove from the top)

---

Common Data Structures (Explained Simply)

1. Arrays

• What it is:
  A collection of elements stored in a continuous block of memory.

• Key Points:

  • Fixed size (in most languages).

  • Fast access using an index (O(1)).

  • Slow at inserting/deleting in the middle (O(n)), because elements must shift.

• Real-World Example:
  A row of lockers numbered 0, 1, 2, 3...

---

2. Queues

• What it is:
  A First-In-First-Out (FIFO) structure, like a real-life line or queue.

• Key Points:

  • Add elements at the back (enqueue).

  • Remove elements from the front (dequeue).

  • Used when order matters.

• Real-World Example:
  Waiting in line at a coffee shop.

---

3. Graphs

• What it is:
  A collection of nodes (vertices) connected by edges.

• Key Points:

  • Used to represent relationships (connections between points).

  • Can be directed (one-way) or undirected (two-way).

  • Common for modelling networks.

• Real-World Example:
  Google Maps (locations as nodes, roads as edges).

---

4. Tree

• What it is:
  A hierarchical structure with a root node and child nodes.

• Key Points:

  • One element ( "root") at the top.

  • Each element can have multiple children, but only one parent.

  • Special types like Binary Trees, Binary Search Trees (BSTs).

• Real-World Example:
  A company's organisational chart.

---

5. Stacks

• What it is:
  A Last-In-First-Out (LIFO) structure → the last thing added is the first one removed.

• Key Points:

  • Add elements to the top (push).

  • Remove elements from the top (pop).

  • Useful for tracking history or nested tasks.

• Real-World Example:
  A stack of plates: you always remove the top plate first.

---

6. Linked Lists

• What it is:
  A series of nodes, where each node contains the data and a pointer to the next node.

• Key Points:

  • Dynamic size grows and shrinks easily.

  • Slower to access elements (O(n)) because you have to move through nodes one by one.

  • Great for frequent insertion/deletion.

• Real-World Example:
  A chain of train cars is connected.

---

Quick Summary Table

Summary

Data structures are essential tools for organizing and storing information efficiently to enhance the performance and scalability of software applications. They play a crucial role in making apps faster, saving memory, and solving various coding problems. Key data structures like arrays, queues, graphs, trees, stacks, and linked lists have real-world applications such as social media feeds, search engines, and navigation apps. Understanding space and time complexity, as well as interfaces, is vital for choosing the right data structure for a given problem, ensuring efficient data management and operation execution.

Thank You

linkedin