CSC 380: Principles of Data Science - Spring 2022
The course introduces students to principles of data science that are necessary for computer scientists to make effective decisions in their professional careers. A number of computer science sub-disciplines now rely on data collection and analysis. For example, computer systems are now complicated enough that comparing the execution performance of two different programs becomes a statistical estimation problem rather than a deterministic computation. This course teaches students the basic principles of how to properly collect and process data sources in order to derive appropriate conclusions from them. The course has three main components: data analysis, machine learning, and a project where students apply the concepts discussed in class to a substantial open-ended problem.
Syllabus
Here is the syallabus updated on 01/06/2022. Further adjustments will be available in D2L.
Logistics info
- Tuesdays and Thursdays, 2pm-3:15pm
- Please find other information from D2L or the syllabus.
Instructor
- Kwang-Sung Jun
- k[lastname]@cs.arizona.edu
- Gould-Simpson Rm 746
Textbook
The required textbook is
- WJ: Watkins, J., “An Introduction to the Science of Statistics: From Theory to Implementation”
- MK: Murphy, K., “Machine Learning: A Probabilistic Perspective.” MIT press, 2012
Backup textbook
- WL: Wasserman, L. “All of Statistics: A Concise Course in Statistical Inference.” Springer, 2004
Other useful resources
- Here are some excellent notes for probability review and linear algebra review.
- You should have no difficulty in Python programming (matlab/julia allowed).
- You may also find The matrix cookbook, The Probability and Statistics Cookbook useful, and Calculus cheatsheet.
Schedule
The following is a rough schedule. Please see D2L for a more detailed and calibrated schedule.
| # | Topics | Readings | Homework | ||
|---|---|---|---|---|---|
| 1: 01/13 | intro | ||||
| 2: 01/18 | probability | WJ 5 | |||
| 3: 01/20 | . | HW1 | |||
| 4: 01/25 | . | ||||
| 5: 01/27 | statistics | HW2 | |||
| 6: 02/01 | . | ||||
| 7: 02/03 | data collection and exploratory analysis | ||||
| 8: 02/08 | . | HW3 | |||
| 9: 02/10 | data processing and visualization | ||||
| 10: 02/15 | . | ||||
| 11: 02/17 | hypothesis testing | HW4 | |||
| 12: 02/22 | . | ||||
| 13: 02/24 | intro to ML | ||||
| 14: 03/01 | . | ||||
| 15: 03/03 | midterm | midterm | |||
| 16: 03/15 | midterm review | ||||
| 17: 03/17 | predictive models | HW5 | |||
| 18: 03/22 | . | ||||
| 19: 03/24 | supervised learning - linear models | ||||
| 20: 03/29 | . | HW6 | |||
| 21: 03/31 | supervised learning - nonlinear models | ||||
| 22: 04/05 | . | ||||
| 23: 04/07 | unsupervised learning - clustering | HW7 | |||
| 24: 04/12 | . | ||||
| 25: 04/14 | unsupervised learning - PCA | ||||
| 26: 04/19 | . | HW8 | |||
| 27: 04/21 | model assessment | ||||
| 28: 04/26 | . | ||||
| 29: 04/28 | data science ethics | ||||
| 30: 05/03 | . | ||||
| : 05/05 | project due | ||||
| : 05/09 | final exam | ||||