Course Description
This course is designed to teach JavaScript developers essential data structures and algorithms needed to write efficient, scalable, and optimized code. Covering fundamental structures such as arrays, linked lists, trees, and graphs, alongside core algorithms like sorting, searching, and recursion, learners will gain both theoretical knowledge and practical coding skills applicable to interviews and real projects.
Duration: 3 Days
Format: Instructor-led, coding exercises, algorithmic problem solving, and real-world examples
Description
Course Outline
? Day 1: Fundamental Data Structures
Session 1: Introduction to Data Structures & Algorithm Complexity
- Importance of data structures in programming
- Big O notation, time and space complexity
- Analyzing algorithm efficiency
Session 2: Arrays and Linked Lists
- Arrays: implementation, operations, and JavaScript specifics
- Singly and doubly linked lists: concepts and coding
- Common problems: reversing, detecting cycles
Session 3: Stacks and Queues
- Stack and queue concepts and use cases
- Implementing stacks and queues in JavaScript
- Applications: expression evaluation, sliding window problems
Lab Activities:
- Implement and manipulate arrays and linked lists
- Build stack and queue data structures
- Solve basic problems using these structures
? Day 2: Trees, Graphs, and Recursion
Session 1: Trees and Binary Trees
- Tree terminology and traversal methods (inorder, preorder, postorder)
- Binary search trees (BST) and operations
- Balanced trees overview (AVL, Red-Black trees concept)
Session 2: Graphs and Graph Traversal
- Graph representations: adjacency list and matrix
- BFS and DFS algorithms explained and coded
- Applications of graph traversal
Session 3: Recursion and Backtracking
- Understanding recursion and call stack
- Recursive solutions for common problems
- Introduction to backtracking with examples
Lab Activities:
- Implement tree traversals and BST operations
- Write BFS and DFS for graph traversal
- Solve recursive problems and simple backtracking puzzles
? Day 3: Sorting, Searching, and Algorithm Design
Session 1: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort: divide and conquer strategies
- Comparison and performance analysis
Session 2: Searching Algorithms
- Linear search and binary search
- Search in rotated sorted arrays and string matching basics
- Hashing and hash tables introduction
Session 3: Algorithm Design Techniques
- Greedy algorithms overview
- Dynamic programming fundamentals
- Problem-solving strategies and tips
Lab Activities:
- Implement various sorting algorithms and analyze their efficiency
- Code searching algorithms and hash table usage
- Solve algorithmic challenges using greedy and dynamic programming approaches
