Data Structures and Algorithms-Deep Dive into Core Concepts

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Dr. P. Saranya Suresh

12:52:00

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1. Introduction to Data Structures

1. Introduction.mp4

18:36

2. Arrays

1. Introduction to arrays.mp4

05:31

2. Inserting an element in an array.mp4

08:40

3. Deleting an element from an Array.mp4

09:34

4. Traversal and Searching in an Array.mp4

12:27

5. Advantages and Disadvantages of arrays.mp4

06:42

3. Linked lists

1. Introduction of Linked Lists.mp4

14:54

2. Singly Linked Lists - Traversal and Searching.mp4

18:16

3. Singly Linked Lists - Insertion at the Beginning and the End.mp4

12:49

4. Singly Linked Lists - Insertion at the Middle.mp4

14:52

5. Singly Linked Lists - Deletion at the Beginning and the End.mp4

08:17

6. Singly Linked Lists - Deletion at the Middle.mp4

05:52

7. Circular Singly Linked Lists - Insertion.mp4

18:56

8. Circular Singly Linked Lists - Deletion.mp4

13:23

9. Doubly Linked Lists - Introduction.mp4

06:18

10. Doubly Linked Lists - Insertion at the beginning and the end.mp4

12:19

11. Doubly Linked Lists - Insertion at the Middle.mp4

17:34

12. Doubly Linked Lists - Deletion at the Beginning and the End.mp4

08:42

13. Doubly Linked Lists - Deletion at the Middle.mp4

13:39

14. Circular Doubly Linked Lists - Introduction.mp4

03:49

15. Circular Doubly Linked Lists - Insertion.mp4

18:22

16. Circular Doubly Linked Lists - Deletion.mp4

15:48

17. Linked Lists - Advantages, Disadvantages and Applications.mp4

15:14

4. Stacks

1. Stacks Introduction.mp4

09:52

2. Stack Implementation Using Array.mp4

12:27

3. Stack Implementation Using Array - Advantages, Disadvantages and Applications.mp4

06:53

4. Stack Implementation Using Linked Lists.mp4

24:34

5. Stack Implementation Using L L - Advantages, Disadvantages and Applications.mp4

04:17

6. Stack Applications.mp4

08:11

7. Stack Applications - Reversing a List and Balancing Symbols.mp4

19:49

8. Stack Applications - Infix to Postfix Conversion 1.mp4

26:01

9. Stack Applications - Infix to Postfix Conversion 2.mp4

14:15

10. Stack Applications - Infix to Postfix Conversion 3.mp4

06:41

11. Stack Applications - Evaluating Postfix Expression.mp4

10:44

12. Stack Applications - Recursion.mp4

07:01

5. Queues

1. Queue Introduction.mp4

06:00

2. Queue Implementation Using Array.mp4

15:04

6. Trees

1. Tree Traversals.mp4

06:44

2. Tree Traversals - In-Order Traversal.mp4

07:09

3. Tree Traversals - Pre-Order Traversal.mp4

07:16

4. Tree Traversals - Post-Order Traversal.mp4

07:15

5. AVL Tree - Rotations (LL, RR, LR, RL).mp4

15:57

6. AVL Tree - Insertion.mp4

18:43

7. Graphs

1. Graph Traversals - Breadth-First Search (BFS).mp4

10:42

2. Graph Traversals - Depth-First Search (DFS).mp4

14:49

3. Breadth-First Search (BFS) Vs Depth-First Search (BFS).mp4

07:23

4. Minimum Spannoing Tree (MST) - Prims Algorithm.mp4

26:00

5. Minimum Spannoing Tree (MST) - Kruskals Algorithm.mp4

12:55

8. Searching Algorithms

1. Linear Search.mp4

10:28

2. Binary Search - Example.mp4

16:50

3. Binary Search - Pseudocode and Time Complexity Analysis.mp4

14:19

4. Difference Between Linear Search and Binary Search.mp4

13:57

9. Sorting Algorithms

1. Bubble Sort - Example, Pseudocode and Time Complexity Analysis.mp4

18:09

2. Insertion Sort - Example.mp4

23:38

3. Insertion Sort - Pseudocode and Time Complexity Analysis.mp4

06:19

4. Quick Sort - Example.mp4

28:23

5. Quick Sort - Pseudocode and Time Complexity Analysis.mp4

16:54

6. Merge Sort - Example.mp4

17:23

7. Merge Sort - Pseudocode and Time Complexity Analysis.mp4

18:24

Description

Mastering the Essentials of Efficient Programming

What You'll Learn?

Understand the foundational data structures such as arrays, linked lists, stacks, queues, hash tables, graphs and trees,
Gain a thorough understanding of various sorting and searching algorithms, including quicksort, mergesort, and binary search
Delve into graph theory and learn essential graph algorithms, including depth-first search (DFS), breadth-first search (BFS), Dijkstra’s algorithm and MST
Develop strong problem-solving skills by applying data structures and algorithms to real-world scenarios and coding challenges.

Who is this for?

Undergraduate or graduate students studying computer science, software engineering, or related fields who want to deepen their understanding of fundamental algorithms and data structures.

Individuals with a passion for coding and problem-solving who are eager to explore advanced topics in algorithms and data structures to improve their programming skills.

Enthusiasts or participants in programming competitions like ACM ICPC, Google Code Jam, or LeetCode who want to sharpen their algorithmic skills and improve their performance in competitive programming.

Individuals transitioning into careers in tech who want to build a strong foundation in algorithms and data structures to pursue roles in software development or data science.

What You Need to Know?

Proficiency in at least one programming language (such as Python, Java, C++, or similar), including an understanding of syntax, control structures (if-else, loops), and basic data types.

Experience with solving basic coding problems and an ability to think algorithmically.

Students should have access to a computer with an internet connection to participate in online lectures, complete assignments, and access course materials and coding platforms.

More details

Description
"Data Structures and Algorithms: Mastering the Essentials of Efficient Programming" is a meticulously crafted course designed to provide students with a comprehensive understanding of the foundational concepts crucial for proficient coding and problem-solving in software development.
Throughout this course, participants will embark on an enriching journey through a diverse array of topics, immersing themselves in the intricate realm of data structures. From the rudimentary structures like arrays and linked lists to the more complex entities such as stacks, queues, hash tables, trees, graphs, heaps, and balanced trees, every facet is meticulously explored. Through a blend of interactive lectures, engaging discussions, and hands-on exercises, students not only grasp the theoretical underpinnings but also gain practical experience in implementing these structures efficiently.
The curriculum extends beyond mere data structures to encompass algorithmic design principles, equipping students with a diverse toolkit of problem-solving techniques. Furthermore, the course covers a broad spectrum of essential sorting and searching algorithms, empowering students with the ability to tackle diverse computational challenges. Additionally, graph algorithms like depth-first search (DFS), breadth-first search (BFS), and Dijkstraâ€™s algorithm are explored in depth, with a focus on understanding their practical applications in software engineering.
By the culmination of the course, students will emerge equipped with a robust foundation in data structures and algorithms, enabling them to write elegant, scalable code and navigate complex programming tasks with confidence. Whether they are aspiring software engineers seeking to kickstart their careers, seasoned developers aiming to refine their skill set, or individuals preparing for technical interviews or competitive programming competitions, this course caters to a wide spectrum of skill levels and career objectives.
Who this course is for:
Undergraduate or graduate students studying computer science, software engineering, or related fields who want to deepen their understanding of fundamental algorithms and data structures.
Individuals with a passion for coding and problem-solving who are eager to explore advanced topics in algorithms and data structures to improve their programming skills.
Enthusiasts or participants in programming competitions like ACM ICPC, Google Code Jam, or LeetCode who want to sharpen their algorithmic skills and improve their performance in competitive programming.
Individuals transitioning into careers in tech who want to build a strong foundation in algorithms and data structures to pursue roles in software development or data science.

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Algorithms

Data Structures

Dr. P. Saranya Suresh

Instructor's Courses

Dr. P. Saranya is an Associate Professor at SRM Institute of Science and Technology. With a Ph.D. in Computer Science, her research focuses on Medical Image Processing and Deep learning. She is also interested in advanced topics in algorithms, data structures, and formal language automata. Driven by a passion for education, she dedicates herself to mentoring students and advancing knowledge in her field.

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