Computational Thinking and Data Science

This module focuses on advanced Python programming concepts and techniques. Students will enhance their Python skills through practical applications, including implementing complex algorithms and creating programs to simulate real-world phenomena. The module emphasizes hands-on programming experience with Python 3.

This module explores optimization problems and their solutions, with a focus on the Knapsack problem and graph-based optimizations. Students learn about algorithmic approaches to solving complex optimization problems and implementing these solutions in Python.

This module covers dynamic programming concepts and applications. Students learn how to break down complex problems into simpler subproblems and develop efficient solutions using dynamic programming techniques.

This module focuses on data visualization techniques using the pylab package. Students learn how to create effective visualizations of data and computational results, including graphs, charts, and other visual representations of scientific data.

This module explores probability concepts and random processes, with a focus on random walks and probability distributions. Students learn how to model and analyze random phenomena using computational methods.

This module covers Monte Carlo simulation techniques and their applications. Students learn how to use computational methods to simulate complex systems and analyze their behavior through random sampling and statistical analysis.

This module focuses on data analysis techniques, including curve fitting and understanding statistical fallacies. Students learn how to analyze data accurately while avoiding common statistical pitfalls and misinterpretations.