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 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.