Course Overview

Course Coverage

This hands-on, project-based course introduces data science, blending computer science and statistics to extract insights from diverse datasets. Students master Python’s data science tools, including Jupyter, NumPy, Pandas, and Scikit-learn, and apply advanced visualization techniques. The curriculum emphasizes statistical understanding and analytical models like regression and clustering. A key focus is on data storytelling, enabling students to effectively communicate complex insights, bridging technical analysis with strategic decision-making.

Student Learning Outcomes

• (Concept) Understand the background and rationale behind this new data science discipline.
• (Programming) Proficiently use the Python data science toolset including Jupyter notebook, NumPy, Pandas, Scikit-learn etc., in the lab assignments and projects.
• (Visualization) Use data visualization python packages Matplotlib and Seaborn to present analytical results.
• (Statistics) Reinforce the understanding of statistics and its application in the scope of data science.
• (Analytics) Apply data analytical models including regression, clustering and decision trees to solve data science problems.