University of Central Lancashire Preston (UCLAN)

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 9 of the CO2412 course on Computational Thinking. The lecture provides an in-depth exploration of Binary Search Trees (BSTs) and Heaps, two fundamental data structures in computer science.

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 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 detailed notes from Lecture 5 of the CO2412 course on Computational Thinking. The lecture provides a comprehensive review of Big O notation, a crucial concept for estimating the time and space efficiency of algorithms, particularly in worst-case scenarios. The notes focus on simplifying complexity estimations by highlighting the fastest-growing terms and discarding less significant details.

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 3 of the CO2412 course on Computational Thinking. The lecture focuses on the concept of decomposition, which involves breaking down complex problems into smaller, more manageable parts. This approach is crucial for effective problem-solving in programming, as it leads to better organization, modularity, and easier debugging.

This document contains detailed notes from Lecture 2 of the CO2412 course on Computational Thinking. The lecture provides an introduction to the Python programming language, focusing on its simplicity, readability, and wide usage in fields such as data science and web development. The notes compare Python with C++, highlighting the differences in execution speed, typing, and learning curve.

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.

These notes cover the essential histology conditions