Course Overview
This course introduces students to data science approaches that have emerged from recent advances in programming and computing technology. They will learn to collect and use data from a variety of sources. Supervised and unsupervised statistical learning techniques made possible by recent advances in computing power will also be covered. This course is very computer-oriented, so it is very important to take the time outside of class to learn by doing, to explore the software being covered in class, and practice new skills on real data sets in the homework assignments and final project. The course will include computer exercises using Python and other relevant computing languages.
Instructor
Prerequisites
No prerequisites for this course. The course will also be taught using Anaconda and Python, so prior Python experience is helpful.
Textbooks
- An Introduction to Statistical Learning, by James, Witten, Hastie, Tibshirani (ISL).
- Python Machine Learning, by Raschka & Mirjalili (PML).
Assessment
| Component | Weight |
|---|---|
| Home assignments | 45% |
| Final project | 45% |
| Class participation | 10% |