About this course
Introduction to Data Structures and Algorithms:
- Overview of DSA and their importance in computer science.
- Basics of algorithm analysis: time complexity, space complexity, Big O notation.
- Introduction to problem-solving techniques and algorithm design paradigms.
Basic Data Structures:
- Arrays and their operations.
- Linked Lists: singly linked, doubly linked, circular linked lists.
- Stacks and Queues.
- Trees: binary trees, binary search trees, AVL trees.
- Graphs: representation, traversal algorithms.
Advanced Data Structures:
- Hash Tables: collision resolution techniques.
- Heaps: min-heap, max-heap, heap operations.
- Advanced Trees: B-trees, Red-Black trees.
- Disjoint Set Union (Union-Find) data structure.
Algorithm Design Techniques:
- Divide and Conquer.
- Greedy Algorithms.
- Dynamic Programming.
- Backtracking.
- Branch and Bound.
Sorting and Searching Algorithms:
- Sorting algorithms: Bubble sort, Selection sort, Insertion sort, Merge sort, Quick sort, Heap sort.
- Searching algorithms: Linear search, Binary search.
Graph Algorithms:
- Shortest path algorithms: Dijkstra's algorithm, Bellman-Ford algorithm.
- Minimum spanning tree algorithms: Prim's algorithm, Kruskal's algorithm.
- Depth-first search (DFS) and Breadth-first search (BFS).
Advanced Topics (optional, in some courses):
- String algorithms: pattern matching, string manipulation.
- Geometric algorithms.
- Network flow algorithms.
- Approximation algorithms.
Practical Applications and Problem Solving:
- Real-world applications of DSA.
- Solving coding problems and challenges using DSA.
- Project work or assignments involving DSA implementation.
FAQ
1) Why we have to learn DSA?
DSA helps us to improve problem-solving skills by developing a systematic approach to solving problems that can be applied to a wide range of situations in computer science.
2) What will I learn in DSA?
In this course you will learn various topics like Arrays, String, Bit Manipulation, Mathematics, Binary Search, Sorting, Tree, Graph, D.P, Backtracking, etc. along with this you will see number of practice exercise on each & every topic.
3) How can you start your DSA Learnings?
First a fall you have to learn Fundamental Concept of Programming language and then we will cover various topics of Data Structure and then Algorithms.
4) Who can enroll in this course?
Anyone can enroll in this course either they are college student or working professional who wants career growth in their life.
5) What benefits can expect from this course?
You can expect various things like recorded lectures, notes, assignments, 24x7 doubt support, 1:1 mentorship, expert industry trainers, etc.
6) What is the duration of the Course?
The duration of the course will be approximately 5-6 months.
7) Which language is used for data structures and algorithms here?
This Course is designed in such a way so that anyone can learn it in an easy way but to be more precise this course is based on Java, C++ & Python.
Comments (0)
Module 1: Fundamentals of Programming Language
4 Parts
- 6:00 Hr
Module 2: Bit Manipulation
2 Parts
- 3:00 Hr
Module 3: Mathematics
7 Parts
- 10:30 Hr
Session 7: Divisibility & Large Numbers
1:30 Hours
Session 8: GCD & LCM, Series & Algebra
1:30 Hours
Session 9: Number digits, Number system, Prime Number
1:30 Hours
Session 10: Primality Test, Prime Factorization & Divisors
1:30 Hours
Session 11: Modular Arithmetic, Factorial, Fibonacci Number
1:30 Hours
Session 12: Set theory, Sieve Algorithms, Euler Totient Function
1:30 Hours
Session 13: nCr Computations, Catalan Numbers
1:30 Hours
Module 4: Time & Space Complexity
2 Parts
- 3:00 Hr
Session 14: Time & Space Complexity
1:30 Hours
Session 15: Examples & practice Problems
1:30 Hours
Module 5: Pattern Searching
2 Parts
- 3:00 Hr
Session 16: Algorithms used to search pattern (part 1)
1:30 Hours
Session 17: Algorithms used to search pattern (part 2)
1:30 Hours
Module 6: Arrays
5 Parts
- 7:30 Hr
Session 18: Concept & Operations on Arrays
1:30 Hours
Session 19: Practice Problems
1:30 Hours
Session 20: Practice Problems
1:30 Hours
Session 21: Practice Problems
1:30 Hours
Session 22: Practice Problems
1:30 Hours
Module 7: 2D Array
2 Parts
- 3:00 Hr
Session 23: Basic concept of 2D Array
1:30 Hours
Session 24: Practice Problems
1:30 Hours
Module 8: Strings
5 Parts
- 7:30 Hr
Session 25: Concept & Operations on Strings
1:30 Hours
Session 26: Practice Problems
1:30 Hours
Session 27: Practice Problems
1:30 Hours
Session 28: Practice Problems
1:30 Hours
Session 29: Practice Problems
1:30 Hours
Module 9: Recursion
6 Parts
- 9:00 Hr
Session 30: Recursion concept (part 1)
1:30 Hours
Session 31: Recursion concept (part 2)
1:30 Hours
Session 32: Practice Problems
1:30 Hours
Session 33: Practice Problems
1:30 Hours
Session 34: Practice Problems
1:30 Hours
Session 35: Practice Problems
1:30 Hours
Module 10: Searching Algorithms
6 Parts
- 9:00 Hr
Session 36: Linear Search
1:30 Hours
Session 37: Binary Search
1:30 Hours
Session 38: Practice Problem on Linear search
1:30 Hours
Session 39: Practice Problem on Binary search
1:30 Hours
Session 40: Practice Problem on Binary Search
1:30 Hours
Session 41: Practice Problem on Binary Search
1:30 Hours
Module 11: Sorting Algorithms
10 Parts
- 15:00 Hr
Session 42: Selection Sort
1:30 Hours
Session 43: Bubble Sort
1:30 Hours
Session 44: Insertion Sort
1:30 Hours
Session 45: Merge Sort
1:30 Hours
Session 46: Quick Sort
1:30 Hours
Session 47: Heap & Count Sort
1:30 Hours
Session 48: Redix & Bucket Sort
1:30 Hours
Session 49: Practice Problems
1:30 Hours
Session 50: Practice Problems
1:30 Hours
Session 51: Practice Problems
1:30 Hours
Module 12: Stack
4 Parts
- 6:00 Hr
Session 52: Stack & its operations
1:30 Hours
Session 53: Stack implementation & time complexity
1:30 Hours
Session 54: Practice problems
1:30 Hours
Session 55: Practice problems
1:30 Hours
Module 13: Queue
4 Parts
- 6:00 Hr
Session 56: Queue & its operations
1:30 Hours
Session 57: Queue implementation & time complexity
1:30 Hours
Session 58: Practice Problems
1:30 Hours
Session 59: Practice problems
1:30 Hours
Module 14: Object-oriented programming
4 Parts
- 6:00 Hr
Session 60: Basics of Oops concept
1:30 Hours
Session 61: Encapsulation & Abstraction
1:30 Hours
Session 62: Polymorphism
1:30 Hours
Session 63: Inheritance
1:30 Hours
Module 15: Linked List
6 Parts
- 9:00 Hr
Session 64: Linked list Concept
1:30 Hours
Session 65: Operations of Linked list
1:30 Hours
Session 66: Types of linked list
1:30 Hours
Session 67: Practice Problems
1:30 Hours
Session 68: Practice Problems
1:30 Hours
Session 69: Practice Problems
1:30 Hours
Module 16: Heap
5 Parts
- 7:30 Hr
Session 70: Heap & its operations
1:30 Hours
Session 71: Types of Heap structure
1:30 Hours
Session 72: Practice Problems
1:30 Hours
Session 73: Practice Problems
1:30 Hours
Session 74: Practice Problems
1:30 Hours
Module 17: Hash
5 Parts
- 7:30 Hr
Session 75: Hash Table, Hash Function & Hash Collision
1:30 Hours
Session 76: Collision handling & load factor
1:30 Hours
Session 77: Practice Problems
1:30 Hours
Session 78: Practice Problems
1:30 Hours
Session 79: Practice Problems
1:30 Hours
Module 18: Matrix
5 Parts
- 7:30 Hr
Session 80: Matrix & its Operation
1:30 Hours
Session 81: Searching in Matrix
1:30 Hours
Session 82: Sorting Matrix
1:30 Hours
Session 83: Practice Problem
1:30 Hours
Session 84: Practice Problem
1:30 Hours
Module 19: Tree
11 Parts
- 16:30 Hr
Session 85: Introduction to Tree data structure
1:30 Hours
Session 86: Types of Tree data structure
1:30 Hours
Session 87: Binary Tree concept
1:30 Hours
Session 88: Binary Search Tree
1:30 Hours
Session 89: AVL Tree
1:30 Hours
Session 90: Segment, Ternary & other tree structure
1:30 Hours
Session 92: Practice Problems
1:30 Hours
Session 93: Practice Problems
1:30 Hours
Session 94: Practice Problems
1:30 Hours
Session 95: Practice Problems
1:30 Hours
Session 96: Practice Problems
1:30 Hours
Module 20: Graph
9 Parts
- 13:30 Hr
Session 97: Introduction to Graph
1:30 Hours
Session 98: working & representation of Graph data structure
1:30 Hours
Session 99: Implementation & Operations on Graph
1:30 Hours
Session 100: Practice Problems
1:30 Hours
Session 101: Practice Problems
1:30 Hours
Session 102: Practice Problems
1:30 Hours
Session 103: Practice Problems
1:30 Hours
Session 104: Practice Problems
1:30 Hours
Session 105: Practice Problems
1:30 Hours
Module 21: Backtracking Algorithms
6 Parts
- 9:00 Hr
Session 106: Backtracking & its working concept
1:30 Hours
Session 107: Types of Backtracking
1:30 Hours
Session 108: Practice Problems
1:30 Hours
Session 109: Practice Problems
1:30 Hours
Session 110: Practice Problems
1:30 Hours
Session 111: Practice Problems
1:30 Hours
Module 22: Greedy Algorithms
3 Parts
- 4:30 Hr
Session 112: Greedy Algorithm concept
1:30 Hours
Session 113: Practice Problems
1:30 Hours
Session 114: Practice Problems
1:30 Hours
Module 23: Divide & Conquer Algorithms
3 Parts
- 4:30 Hr
Session 115: Divide & Conquer Algorithm Concept
1:30 Hours
Session 116: Practice Problems
1:30 Hours
Session 117: Practice Problems
1:30 Hours
Module 24: Geometric Algorithms
2 Parts
- 3:00 Hr
Session 118: Concept of Geometric Algorithms
1:30 Hours
Session 119: Practice Problems
1:30 Hours
Module 25: Dynamic programming Algorithms
6 Parts
- 9:00 Hr
Session 120: Working of Dynamic Programming
1:30 Hours
Session 121: Practice Problems
1:30 Hours
Session 122: Practice Problems
1:30 Hours
Session 123: Practice Problems
1:30 Hours
Session 124: Practice Problems
1:30 Hours
Session 125: Practice Problems
1:30 Hours
5.00
10 Reviews
Reviews (10)
Anvi Joshi
5 Jan 2024 | 13:12
The content of the DSA course is comprehensive and logically organized, covering a wide range of topics from basic to advanced concepts. It is presented in an accessible way with practical examples, making it suitable for learners at all levels
Aarav Patel
12 Jan 2024 | 18:27
I was extremely impressed by the level of doubt support provided in the Qualified DSA course. The instructors are not only knowledgeable, but also friendly and helpful. They encourage students to ask questions and provide detailed explanations, making sure everyone feels comfortable expressing their doubts.
Ananya Trivedi
13 Jan 2024 | 18:59
A qualified DSA course provides a thorough program that teaches basic data structures and complex algorithms. The course material is easy to understand, with real-life examples and a focus on practical uses, which is very beneficial for us.
Avvya Bhatia
19 Jan 2024 | 19:35
In simpler terms: The tutor not only knew a lot but also gave me a lot of support and encouragement during our sessions.
Advay Mehta
26 Jan 2024 | 10:28
I was extremely impressed by the level of doubt support provided in the Qualified DSA course. The instructors are not only knowledgeable, but also friendly and helpful.
Aaradhya Singh
10 Feb 2024 | 13:28
Taking the 'Data Structures and Algorithms Mastery' course with Qualifyed changed my learning in a big way. This course really went deep into DSA stuff, starting from the basics and going all the way to the tough stuff. It was set up really well too, with each lesson building on what came before, so I never felt lost. It made sure I really got the important stuff.
Arjun Joshi
6 Feb 2024 | 13:30
The DSA course by Qualifyed provides a well-structured curriculum covering essential topics with clear explanations and practical exercises. From basic data structures to advanced algorithms, it offers a thorough understanding of DSA principles
Zoya Khanna
16 Feb 2024 | 20:39
Enrolling in Qualifyed's 'Data Structures and Algorithms Mastery' course was a smart move. With lifetime access, I have the flexibility to learn at my own pace, ensuring I master DSA concepts thoroughly.
Kiara Mehta
23 Feb 2024 | 13:43
Qualifyed DSA course exceeded my expectations. Having tutors accessible anytime was a game changer for me. Whether it was day or night, there was always someone there to clarify doubts and provide guidance, making my learning experience truly seamless.
Kabir Verma
29 Feb 2024 | 21:46
The 'Data Structures and Algorithm Mastery' course changed the mindset for me. I can go back to the content whenever I want because I have lifetime access. This makes learning easy and stress free.
