This module discusses validating simulation results, emphasizing statistical techniques such as curve fitting and linear regression. Students will learn:
Through practical exercises, students will develop skills to ensure the reliability of their simulation results.
This module introduces the course objectives and fundamental concepts of computation. Students will learn:
By the end of this module, students will have a clear understanding of what to expect and the foundational knowledge necessary for future topics.
This module focuses on operators and operands, which are crucial for writing effective programs. Key topics include:
Students will gain practical experience in writing conditional statements and loops, enabling them to control the flow of their programs.
This module introduces common code patterns, particularly focusing on the development of iterative programs. Students will learn:
By recognizing these patterns, students will improve their coding efficiency and problem-solving abilities.
This module covers decomposition and abstraction through functions. Students will explore:
Mastering these concepts will enable students to write cleaner, more organized code while tackling complex problems.
This module focuses on floating-point numbers and mathematical concepts. Key learning points include:
Students will gain insights into numerical methods and the precision of calculations, which are essential in programming.
This module introduces bisection methods and Newton/Raphson techniques, emphasizing their applications in programming. Students will learn:
Through practical examples, students will apply these methods to solve mathematical problems programmatically.
This module explores lists and mutability, two fundamental concepts in Python. Students will discover:
Understanding these concepts will help students manage data structures effectively in their programs.
This module focuses on algorithm complexity, an essential aspect of programming. Key topics include:
By mastering these concepts, students will be able to evaluate and choose appropriate algorithms for their programming tasks.
This module covers binary search and sorting algorithms such as bubble and selection sorts. Students will learn:
Students will practice implementing these algorithms in Python and analyzing their performance.
This module introduces divide and conquer methods, focusing on efficient problem-solving strategies. Key concepts include:
Students will gain insights into when to apply these methods for optimal results in programming tasks.
This module emphasizes testing and debugging, crucial skills for successful programming. Students will learn:
By the end of this module, students will be equipped to identify and fix errors in their programs effectively.
This module dives into the knapsack problem, a classic optimization challenge in computer science. Key topics include:
Students will engage with real-world examples to understand how dynamic programming can optimize solutions to the knapsack problem.
This module delves deeper into dynamic programming, emphasizing overlapping subproblems and optimal substructure. Students will learn:
Through examples and exercises, students will develop practical skills in implementing dynamic programming solutions.
This module introduces the principles of object-oriented programming (OOP) using the analysis of the knapsack problem as a context. Key concepts include:
Students will gain valuable experience in creating programs that utilize OOP concepts effectively.
This module focuses on abstract data types (ADTs), classes, and methods in programming. Key learning points include:
Students will develop skills to create modular and maintainable code through effective use of classes and methods.
This module delves into encapsulation, inheritance, and shadowing in object-oriented programming. Key concepts covered include:
Students will learn how to implement these principles to create robust and flexible programs.
This module presents computational models through random walk simulations. Students will learn:
Theoretical and practical aspects will be covered, helping students understand the relevance of computational models in real-world scenarios.
This module focuses on presenting simulation results effectively using Pylab for plotting. Students will explore:
By mastering these skills, students will enhance their ability to communicate findings from simulations to diverse audiences.
This module offers an overview of digital communication and the role of computer scientists. Key topics include:
Students will gain insights into the broad applications of computer science and its significance in the digital age.
This module explores biased random walks and their associated distributions. Students will learn:
This module will enhance students' understanding of randomness and its applications in computational models.
This module introduces Monte Carlo simulations, focusing on practical applications such as estimating the value of pi. Key topics include:
Students will engage with hands-on projects to apply Monte Carlo simulations in various contexts.
This module discusses validating simulation results, emphasizing statistical techniques such as curve fitting and linear regression. Students will learn:
Through practical exercises, students will develop skills to ensure the reliability of their simulation results.
This module explores normal, uniform, and exponential distributions, along with the misuse of statistics. Key concepts include:
Students will learn to interpret statistical data accurately and recognize potential misapplications.
This module presents a stock market simulation, allowing students to apply programming skills in a real-world context. Key learning points include:
Through this practical approach, students will gain insights into financial modeling and its relevance to computer science.