This module focuses on stock market simulation, where students can apply their programming skills in a financial context. Key topics include:
Students will gain practical experience in financial modeling and analysis.
This module sets the stage for the course, outlining its goals and objectives. Students will learn about:
By understanding these foundational concepts, students will be prepared to engage with more complex programming topics.
This module delves into operators and operands, essential components in programming. Students will gain insights into:
By the end of this module, students will be able to create more dynamic and interactive programs.
This module introduces common coding patterns, focusing on iterative programming. Key topics include:
Students will enhance their programming logic and problem-solving abilities through hands-on exercises.
This module focuses on decomposition and abstraction through functions, essential programming concepts. Students will learn to:
These skills will empower students to write cleaner and more efficient code.
This module covers floating point numbers and their significance in programming. Key learning points include:
By mastering these topics, students will be prepared for more advanced numerical programming tasks.
This module introduces bisection methods and Newton/Raphson techniques, along with an introduction to lists. Students will learn about:
These concepts will broaden the students' programming toolkit, enabling more complex problem-solving.
This module focuses on lists and mutability, along with dictionaries and pseudocode. Key topics include:
Students will gain a deeper comprehension of data structures and their use cases in programming.
This module introduces the concept of complexity in algorithms. Students will learn about:
These concepts are essential for writing efficient and effective code.
This module covers the binary search algorithm along with sorting techniques, specifically bubble and selection sorts. Key areas include:
Students will gain practical skills in implementing and analyzing search and sorting algorithms.
This module introduces divide and conquer methods, focusing on merge sort and exception handling. Students will learn:
These techniques will prepare students for more advanced programming challenges.
This module focuses on testing and debugging, essential skills for any programmer. Students will learn about:
By mastering these skills, students will enhance their programming proficiency and confidence.
This module further explores debugging and introduces the knapsack problem, along with dynamic programming concepts. Key areas include:
Students will develop critical thinking skills and learn to apply dynamic programming in various contexts.
This module focuses on dynamic programming, emphasizing overlapping subproblems and optimal substructure. Students will learn about:
These concepts are crucial for efficiently solving a range of computational problems.
This module introduces object-oriented programming (OOP) principles through the analysis of the knapsack problem. Key learning points include:
By mastering OOP, students will enhance their programming capabilities and design robust software solutions.
This module focuses on abstract data types (ADTs), including classes and methods in Python. Key areas of study include:
Students will develop a strong foundation in OOP and its practical applications in coding.
This module introduces key concepts related to encapsulation in programming, including inheritance and shadowing. Students will learn:
These concepts are vital for writing maintainable and scalable code.
This module covers computational models, specifically focusing on random walk simulations. Key learning points include:
Students will gain practical experience in modeling and simulation techniques.
This module focuses on presenting simulation results, utilizing Pylab and plotting techniques. Key areas of study include:
Students will learn to communicate their findings effectively through visual means.
This module introduces digital communication, providing an overview of what computer scientists do. Students will learn about:
This foundational knowledge will help contextualize their learning in the broader field of computer science.
This module explores biased random walks and their relationship with distributions. Key topics include:
Students will gain a deeper understanding of randomness in computational models.
This module introduces Monte Carlo simulations, focusing on estimating pi. Students will learn:
These techniques will enhance students' skills in probabilistic modeling and simulation.
This module focuses on validating simulation results, covering curve fitting and linear regression techniques. Key learning points include:
Students will learn how to ensure their simulations are reliable and meaningful.
This module explores normal, uniform, and exponential distributions, along with the misuse of statistics. Students will learn about:
These insights will help students become more informed consumers of statistical information.
This module focuses on stock market simulation, where students can apply their programming skills in a financial context. Key topics include:
Students will gain practical experience in financial modeling and analysis.