This module teaches students about binary search and sorting algorithms. Key areas include:
Students will learn how to optimize data searching and organization.
This module introduces students to the objectives of the course and the fundamental concepts of computation. Participants will learn about:
Students will gain an understanding of how computation can be applied to solve real-world problems.
In this module, students will explore the essential components of programming, focusing on:
Through practical examples, students will learn how to control the flow of their programs effectively.
This module covers common coding patterns, particularly focusing on iterative programming. Students will learn:
By understanding these patterns, students will enhance their problem-solving capabilities.
This module introduces the concepts of decomposition and abstraction through functions. Students will discover:
These concepts are fundamental for writing modular and reusable code.
This module focuses on floating point numbers and their significance in programming. Key topics include:
Students will learn how to handle real-number computations in their programs.
This module introduces bisection methods and the Newton/Raphson technique for finding roots. Key areas of focus include:
Students will learn how to solve equations numerically using these methods.
This module addresses lists and mutability, alongside dictionaries and pseudocode. Students will learn:
Understanding these data structures is essential for efficient coding.
This module dives into computational complexity, defining various algorithmic complexities. Students will explore:
Students will learn to evaluate algorithms beyond mere functionality.
This module teaches students about binary search and sorting algorithms. Key areas include:
Students will learn how to optimize data searching and organization.
This module covers divide and conquer methods, focusing on their application in algorithms. Students will explore:
These techniques are fundamental for writing efficient algorithms.
This module focuses on testing and debugging, essential skills for any programmer. Students will learn:
Students will develop the skills necessary to identify and fix issues in their code.
This module covers the knapsack problem and introduces dynamic programming. Students will explore:
Students will learn how to approach complex problems using dynamic programming.
This module delves into dynamic programming concepts, focusing on:
Students will learn how to break down and solve complex problems efficiently.
This module introduces object-oriented programming (OOP) principles. Key topics include:
Students will gain insights into structuring code for better organization and reusability.
This module focuses on abstract data types, classes, and methods. Students will learn about:
These concepts are vital for effective software development.
This module covers encapsulation, inheritance, and shadowing in programming. Key points include:
Students will learn how to design flexible and efficient code using these principles.
This module introduces computational models, focusing on simulations. Key topics include:
Students will learn the significance of simulations in understanding complex systems.
This module focuses on presenting simulation results effectively. Students will learn:
Effective presentation of results is crucial for conveying insights gained from simulations.
This module introduces digital communication within the scope of computer science. Students will discover:
Students will gain a broader understanding of the significance of computer science beyond programming.
This module examines biased random walks and distributions. Key concepts include:
Students will learn to analyze and interpret random processes.
This module covers Monte Carlo simulations, focusing on their applications and techniques. Students will learn about:
Students will gain insights into the power of simulations for solving complex problems.
This module focuses on validating simulation results. Key topics include:
Students will learn to ensure the reliability of their simulation outputs.
This module examines normal, uniform, and exponential distributions, along with common statistical misuses. Students will understand:
Students will gain a solid foundation in statistical concepts critical for data-driven decision-making.
This module introduces stock market simulation, providing students with insights into financial modeling. Key topics include:
Students will learn how simulations can inform investment strategies and financial decisions.