Course Overview

Course Coverage

This project-based hands-on course introduces students to data science in general. Data science is an interdisciplinary field that applies computer science and statistics to extract useful knowledge from data collected in an almost unlimited range of other disciplines, including business, biology, physics, medicine, meteorology, and many others. In this course, students will apply the popular Python data science toolset to implement analytical models with k-nearest neighbors, regression, decision trees, and clustering, etc., and infer knowledge from real-world data.

Student Learning Outcomes

• (Introduction) Understand the background and rationale behind this new data science discipline.
• (Hacking) 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.