Data Structures and Algorithms Training Course

This course equips participants with strong problem-solving skills using data structures and algorithms, which are essential for efficient software development and technical interviews. It focuses on organizing data, optimizing performance, and designing algorithms to solve real-world computing problems effectively.

Target Groups

• Computer science and IT students
• Software developers and engineers
• Backend and full-stack developers
• Data science and AI beginners
• Competitive programming enthusiasts
• Tech interview candidates
• Anyone wanting strong programming foundations

Course Objectives

By the end of this course, participants will be able to:

• Understand core data structures and their use cases
• Design and analyze efficient algorithms
• Improve code performance and optimization
• Solve complex programming problems
• Apply recursion and iterative techniques
• Use sorting and searching algorithms effectively
• Understand time and space complexity (Big-O notation)
• Prepare for technical interviews and coding tests
• Build algorithmic thinking skills
• Apply DSA concepts in real-world applications

Course Modules

Module 1: Introduction to Data Structures and Algorithms

• What are data structures and algorithms
• Importance in software development
• Problem-solving approach
• Introduction to Big-O notation
• Time and space complexity basics

Module 2: Arrays and Strings

• Array operations and manipulation
• Multi-dimensional arrays
• String handling techniques
• Common array and string problems
• Optimization strategies

Module 3: Linked Lists

• Singly and doubly linked lists
• Circular linked lists
• Insertion and deletion operations
• Traversal techniques
• Practical problem solving

Module 4: Stacks and Queues

• Stack operations and applications
• Queue and circular queue
• Priority queues
• Real-world use cases
• Expression evaluation problems

Module 5: Recursion and Backtracking

• Understanding recursion
• Recursive problem solving
• Backtracking techniques
• Common recursion problems
• Optimization of recursive solutions

Module 6: Sorting and Searching Algorithms

• Bubble, selection, and insertion sort
• Merge sort and quicksort
• Linear and binary search
• Algorithm comparison
• Performance optimization

Module 7: Trees and Binary Trees

• Tree data structure basics
• Binary trees and binary search trees
• Tree traversal methods (inorder, preorder, postorder)
• Balanced trees overview
• Common tree problems

Module 8: Graphs

• Graph representation (adjacency matrix/list)
• Depth-first search (DFS)
• Breadth-first search (BFS)
• Shortest path algorithms basics
• Real-world graph applications

Module 9: Hashing and Heaps

• Hash tables and hash functions
• Collision handling techniques
• Heap data structure
• Priority queue implementation
• Efficient data retrieval techniques

Module 10: Capstone Project and Case Studies

• Solving real-world algorithmic problems
• Competitive programming exercises
• System design basics using DSA concepts
• Coding interview simulation
• Performance optimization challenges
• Emerging trends in algorithms, AI-assisted problem solving, advanced data structures, distributed computing algorithms, and optimization techniques for large-scale systems