This module covers floating-point numbers and their applications in programming. Students will learn about:
Understanding floating-point arithmetic is essential for accurate computations in various programming scenarios.
The first module sets the stage for the course by outlining its goals and introducing key concepts in computation. Students will explore:
This foundational knowledge prepares students for more complex programming concepts.
This module delves into the fundamental components of programming, focusing on:
By mastering these concepts, students will develop the ability to write functional programs.
This module focuses on common coding patterns, particularly iterative programs. Students will learn how to:
These skills are crucial for efficient programming and problem-solving.
This module introduces the concepts of decomposition and abstraction through functions. Key topics include:
Students will gain insights into organizing code effectively for better readability and maintainability.
This module covers floating-point numbers and their applications in programming. Students will learn about:
Understanding floating-point arithmetic is essential for accurate computations in various programming scenarios.
This module introduces students to bisection methods and Newton/Raphson techniques for solving equations. Additionally, students will explore:
These methods are vital for numerical analysis and problem-solving in programming.
This module explores lists and their mutability in Python. Key concepts include:
Students will learn how to utilize these data structures effectively in their programming projects.
This module covers algorithm complexity, focusing on:
Students will gain insights into algorithm performance and its significance in programming.
This module introduces binary search and sorting algorithms, specifically:
Understanding these algorithms is crucial for optimizing data processing in programming.
This module examines divide and conquer methods in programming. Key topics include:
Students will learn how to apply these techniques to solve complex problems effectively.
This module focuses on testing and debugging practices in programming. Students will learn:
These skills are essential for writing robust and error-free code.
This module focuses on the knapsack problem, a classic optimization problem. Topics include:
Students will learn to apply dynamic programming techniques to solve complex optimization issues.
This module dives deeper into dynamic programming, focusing on:
Students will gain a thorough understanding of how to approach problems using this powerful method.
This module introduces object-oriented programming (OOP) principles. Key topics include:
Students will learn how to design and implement programs using OOP techniques.
This module delves into abstract data types, classes, and methods. Students will explore:
By understanding these principles, students will be better equipped to design complex data structures.
This module focuses on encapsulation, inheritance, and shadowing in object-oriented programming. Key topics include:
Students will learn how these principles enhance the design and functionality of their programs.
This module explores computational models, focusing on random walk simulations. Key topics include:
Students will gain practical experience in modeling complex systems through simulations.
This module focuses on presenting simulation results using Pylab and plotting techniques. Key topics include:
Students will learn to communicate their findings clearly using graphical techniques.
This module offers an introduction to digital communication within the context of computer science. Key topics include:
Students will gain insights into the evolving field of digital communication and its implications.
This module examines biased random walks and their relation to distributions. Key topics include:
Students will learn to model and analyze biased random walks effectively.
This module introduces Monte Carlo simulations, focusing on estimating values such as pi. Key topics include:
Students will gain hands-on experience in using Monte Carlo techniques for estimation and simulation.
This module focuses on validating simulation results through techniques such as curve fitting and linear regression. Key topics include:
Students will learn how to assess and improve their simulation results effectively.
This module covers normal, uniform, and exponential distributions, focusing on:
Students will gain a comprehensive understanding of statistical principles relevant to programming.
This module presents a simulation of the stock market, focusing on:
Students will learn to apply programming skills to simulate complex financial systems.