Computational Thinking (CO2412)

This document provides detailed notes from Lecture 1 of the CO2412 course on Computational Thinking. The lecture introduces the concept of computational thinking, defining it as a universally applicable skill set that goes beyond computer science. The notes cover the four key aspects of computational thinking: decomposition, pattern recognition, abstraction, and algorithm development, explaining how these concepts are essential for problem-solving in various domains.

This document contains comprehensive notes from Lecture 7 of the CO2412 course on Computational Thinking. The lecture explores two important strategies for solving optimization problems: Greedy Algorithms and the Branch and Bound method. It provides an in-depth understanding of how these approaches work, their characteristics, and when to use them.

This document contains comprehensive notes from Lecture 8 of the CO2412 course on Computational Thinking. The lecture focuses on the concept of backtracking, a powerful problem-solving technique that incrementally builds solutions and backtracks when constraints are violated.

This document contains comprehensive notes from Lecture 4 of the CO2412 course on Computational Thinking. The lecture focuses on the crucial topic of performance evaluation, highlighting the importance of understanding algorithm efficiency to make informed decisions during algorithm design and optimization. The notes outline the steps involved in creating efficient programs, from selecting the best algorithm and data structure to identifying bottlenecks and optimizing relevant parts of the code.

This document contains detailed notes from Lecture 12 of the CO2412 course on Computational Thinking. The lecture focuses on pathfinding algorithms, essential tools in computer science for determining the shortest or most efficient path between two points within a graph structure. These algorithms have broad applications, including robotics, logistics, game development, and network routing.

This document contains comprehensive notes from Lecture 10 of the CO2412 course on Computational Thinking. The lecture introduces the foundational concepts of Graph Theory, a crucial area in computer science for modeling and solving complex problems involving networks of connected objects.

This document contains detailed notes from Lecture 13 of the CO2412 course on Computational Thinking. The lecture introduces the principles of Object-Based Programming (OBP), a component-based approach to software development that focuses on defining and simulating real-world objects through classes.

This document contains comprehensive notes from Lecture 16 of the CO2412 course on Computational Thinking, focusing on numeric data analysis. The lecture provides an in-depth overview of statistical measures used to summarize and interpret data, helping to understand the distribution and key characteristics of a data set.

This document contains detailed notes from Lecture 18 of the CO2412 course on Computational Thinking, focusing on key data structures and algorithms essential for exam preparation. The lecture provides an in-depth review of Binary Trees, Binary Search Trees (BSTs), Heaps, and Graph Theory, along with an introduction to Greedy Algorithms.

This document contains comprehensive notes from Lecture 15 of the CO2412 course on Computational Thinking. The lecture focuses on the importance of organizing and presenting data graphically, which is essential for making raw data more interpretable and actionable. The notes cover various techniques for visually representing data, such as bar charts, pie charts, histograms, frequency distributions, and more.