Data Structures and Algorithms Specialization

Description

Key Topics Covered:

1. Introduction to Data Structures
   • Basics of data structures: arrays, stacks, queues, and linked lists.
   • Understanding the need for data structures in organizing and storing data efficiently.
   • How to implement basic data structures in programming.
2. Complexity and Big-O Notation
   • Introduction to algorithm analysis, including time and space complexity.
   • Understanding Big-O notation to evaluate the efficiency of algorithms.
   • Best, worst, and average-case analysis of algorithms.
3. Arrays and Linked Lists
   • Advanced use of arrays and linked lists to store and manage data.
   • Implementing operations such as insertion, deletion, searching, and sorting on arrays and linked lists.
   • Understanding the differences between static and dynamic data structures.
4. Stacks and Queues
   • Introduction to stack and queue data structures and their practical applications (e.g., undo functionality, job scheduling).
   • Implementing and working with stack operations like push, pop, and peek.
   • Exploring different types of queues: FIFO (First In, First Out), priority queues, and circular queues.
5. Trees and Graphs
   • Understanding tree data structures: binary trees, binary search trees, AVL trees, and heaps.
   • Graph theory fundamentals: graph traversal algorithms (BFS, DFS), directed and undirected graphs.
   • Tree and graph algorithms for searching, traversal, and optimization.
6. Hash Tables and Hash Maps
   • Introduction to hashing concepts and how hash tables store data efficiently.
   • Hash functions and handling collisions in hash tables.
   • Implementing hash maps for fast lookups and key-value pair storage.
7. Sorting Algorithms
   • Common sorting algorithms: bubble sort, merge sort, quicksort, and heap sort.
   • Understanding how each sorting algorithm works and its efficiency.
   • Choosing the best sorting algorithm based on the problem context.
8. Advanced Data Structures
   • More complex data structures like tries, segment trees, and suffix arrays.
   • Applications of advanced data structures in computational problems like text processing and dynamic programming.
   • Understanding how these structures improve algorithmic efficiency in real-world problems.
9. Graph Algorithms
   • Shortest path algorithms (Dijkstra, Bellman-Ford).
   • Minimum spanning tree algorithms (Kruskal, Prim).
   • Advanced graph algorithms like Floyd-Warshall, and network flow algorithms.
10. Dynamic Programming and Greedy Algorithms
    • Understanding dynamic programming: memoization and tabulation techniques.
    • Solving optimization problems using dynamic programming.
    • Greedy algorithms: how they differ from dynamic programming and when to apply them.

Course Objectives:

• Master Fundamental Data Structures: Learn the core data structures (arrays, stacks, queues, linked lists) and understand their role in organizing and managing data for optimal performance.
• Evaluate Algorithm Efficiency: Gain proficiency in analyzing algorithms using Big-O notation and understanding their time and space complexities to choose the best approach.
• Implement Advanced Data Structures: Explore and implement more complex data structures, including trees, graphs, and hash tables, for more efficient data storage and retrieval.
• Learn Sorting and Searching Algorithms: Understand the inner workings of sorting algorithms and how to choose the most efficient one for different problem types.
• Solve Problems Using Graph Algorithms: Learn and apply graph traversal and pathfinding algorithms to real-world problems, such as network routing and shortest path finding.
• Apply Dynamic Programming: Learn how to break down complex problems into simpler subproblems and use dynamic programming to solve them efficiently.
• Use Greedy Algorithms: Understand the principles behind greedy algorithms and how they can be applied to solve optimization problems in a simpler and faster way.
• Develop Strong Problem-Solving Skills: Strengthen your ability to approach computational problems logically and effectively, applying the right data structure or algorithm to solve the task at hand.