Foundations of Algorithmic Thinking

This unit introduces core algorithm concepts, basic mathematical tools, and practical implementation skills needed to analyze and build efficient solutions. Students will learn to implement and compare fundamental algorithms (sorting, graph traversal), use Google Colab for development without relying on high-level libraries, and evaluate common algorithmic paradigms for real-world data and scalability challenges.

Learning Objectives

• Analyze problem statements and describe algorithms using clear pseudocode and basic efficiency measures (time and space complexity).
• Apply vector, matrix, and linear-function computations in Google Colab and implement foundational operations without using NumPy or similar high-level libraries.
• Demonstrate implementations of fundamental algorithms (merge sort, quick sort, BFS/DFS) and measure their correctness and performance on representative datasets.
• Evaluate and compare algorithmic design strategies (greedy methods, dynamic programming, randomization, local search, heuristics) to select appropriate approaches for data-intensive and networked problems.