This course serves as an introduction to computer science and programming, specifically tailored for students with minimal or no programming experience. It aims to:
Throughout the course, students will explore various topics from basic concepts to more advanced programming principles, ensuring a comprehensive foundation in computer science.
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.