About Data Structures & Algorithms
A data structure is a way to store data. We structure data in different ways depending on what data we have, and what we want to do with it. If we want to store data about people we are related to, we use a family tree as the data structure. We choose a family tree as the data structure because we have information about people we are related to and how they are related, and we want an overview so that we can easily find a specific family member, several generations back.
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Technology Training
Practice Assignments
1:1 Doubt solving
Learn to Crack Interviews
Panel Mock Interview
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DSA Course
Breakdown
100%
Hands-on Training
70-100
Practical Assignments
3+
Assessments / Mock Interviews
Mon - Fri
Online/Offline
1 Hour Real time interactive teaching
2 Hours Practice Time
Course Syllabus
- What are Data Structures?
- What is an algorithm?
- Why are Data Structures and Algorithms important?
- Types of Data Structures Types of Algorithms
- Big O, Big Theta and Big Omega
- Time complexity examples
- Space Complexity
- How to measure the codes using Big O?
- Relationships Between Various Notations
- Best, Worst and Average Case Analysis
- Array
- One-Dimensional Array
- Multi-Dimensional Array
- Dynamic Arrays
- Why do Arrays fail?
- Problems Solving in Array
- Singly Linked List
- List(Insertion, Deletion and Traversal)
- Doubly Linked List
- Circular Linked List
- Introduction to Stack
- Push and Pop
- Expression Evaluation (Infix,Prefix and Postfix)
- Why do we need Queue?
- Enqueue and Dequeue
- Linear and Circular Queue
- What is Sorting?
- Types of Sorting
- Sorting Terminologies
- Bubble Sort
- Time Complexity of Bubble Sort
- Selection Sort
- Time Complexity of Selection Sort
- Insertion Sort
- Time Complexity of Insertion Sort
- Merge Sort
- Time Complexity of Merge Sort
- Quick Sort
- Time Complexity of Quick Sort
- Heap Sort
- Time Complexity of Heap Sort
- Comparison of Sorting Algorithms
- Introduction to Searching Algorithms Linear Search
- Time Complexity of Linear Search
- Binary Search
- Time Complexity of Binary Search
- Structure and Implementations
- Terminologies and Traversals
- Types of Binary Search Tree
- Properties of a Tree: Depth, Nodes and Leafs Maximum Depth of a Binary tree
- AVL Trees: What and Why?
- Hashing
- Hash Tables
- Direct access table
- Hash Function and collisions
- Chaining and Load Factor
- Graph Representation Graph Traversal
- Adjacency Matrix
- Graph traversal - BFS
- Graph Traversal - DFS
- BFS Traversal vs DFS Traversal
- Minimum Spanning Tree
- Kruskal's Algorithm
- Prim's Algorithm
- Dijikstra's Algorithm
- Bellman Ford Algorithm
- Recursion Programming
- Recursion Tree Method
- Master Theorem
- Extended Master Theorem
- Cracking Recursion Interview Question
- What is Dynamic Programming? (Overlapping property)
- Where does the name of DC come from?
- Top Down with Memoization
- Bottom Up with Tabulation
- Top Down vs Bottom Up
- Is Merge Sort Dynamic Programming?
- Number Factor Problem using Dynamic Programming
- Longest Common Subsequence
- Knapsack
- Bellman Ford Algorithm as Dynamic Programming
- Matrix Chain Multiplication
- Subset Sum Problem
- Floyd Marshall Algorithm
- Introduction
- Activity Selection Problem
- Fractional Knapsack Problem
- Huffman Coding For Data Compression
- Minimum Spanning Tree: Greedy Kruskal's Algorithm
- Greedy Algorithm: Dijkstra's Algorithm
Course FAQ’S
The best way to start learning data structures and algorithms is by understanding the basics of arrays and linked lists. Once you grasp these, you can move on to more complex structures like stacks, queues, trees, and graphs. Practice implementing these structures and solving problems on platforms like LeetCode, HackerRank, and GeeksforGeeks.
Data structures are important because they provide efficient ways to store and manage data, enabling faster access, modification, and processing. They are fundamental for solving complex problems and are essential for writing efficient code in software development.
Choosing the right data structure depends on the operations you need to perform and their frequency. For example, if you need quick access to elements, arrays or hash tables are suitable. For dynamic data with frequent insertions and deletions, linked lists or trees are better. Analyzing the time and space complexity of different data structures can help you make an informed choice.
Linear data structures, such as arrays, linked lists, stacks, and queues, store data in a sequential manner. Non-linear data structures, like trees and graphs, store data in a hierarchical or interconnected manner. Linear structures are easier to implement but may not be as efficient for complex relationships as non-linear structures.
Algorithm complexity is analyzed using Big O notation, which describes the upper bound of an algorithm's running time or space requirement in terms of the input size. To understand it, study common complexities like O(1), O(n), O(log n), O(n log n), and O(n^2). Practice by analyzing different algorithms to determine their time and space complexity.
Learning sorting algorithms is important because sorting is a fundamental operation in many applications. Understanding different sorting algorithms, such as Bubble Sort, Insertion Sort, Quick Sort and Merge Sort helps you choose the most efficient one for your specific use case. Sorting algorithms also help you understand algorithm design and analysis.
A recursive algorithm is one that solves a problem by calling itself with smaller subproblems. Use recursion when a problem can be divided into similar subproblems, such as in tree traversals, factorial computation, and certain dynamic programming problems. However, be mindful of stack overflow issues and consider iterative solutions for large inputs.
