Course Schedule
| Date | Topics & Data | Readings | Assignments | |
|---|---|---|---|---|
| 1: 25 & 27 Aug. | What Is Data? & Data Ethics | ADSA Case Study | ||
| 2: 1 & 3 Sept. | Jupyter, Python, and Data Wrangling | Downey Ch. 1 & 7 | ||
| 3: 8 & 10 Sept. | Exploratory Data Analysis w/ Movies Data: Summary Statistics & Data Types | Bruce Ch. 1 (pdf); Downey Ch. 2 | Documentation Assignment Due 10 Sept. | |
| 4: 15 & 17 Sept. | Exploratory Data Analysis w/Movies Data: Visualization | Wilke Ch. 5 | ||
| 5: 22 & 24 Sept. | Distributions & Hypothesis Testing w/ Baseball Data | Bruce Ch. 3 (pdf); (Optional: Downey Ch. 8) | ||
| 6: 29 Sept. & 1 Oct. | Correlation w/ Baseball Data | Downey Ch. 9 | ||
| 7: 6 & 8 Oct. | Review & Exam | Test 1 8 Oct. | ||
| 8: 13 & 15 Oct. | Regression w/ Business Data | Bruce Ch. 4 (pdf) | ||
| 9: 22 Oct. | Open Lab: Explanatory vs. Predictive Modeling | Descriptive Analysis Project Due 22 Oct. | ||
| 10: 27 & 29 Oct. | More Regression w/ Business Data | Bruce Ch. 6 (pdf) | ||
| 11: 3 & 5 Nov. | Classification w/ Health Data | Bruce Ch. 5 (pdf) | ||
| 12: 10 & 12 Nov. | More Classification w/ Health Data | |||
| 13: 17 & 19 Nov. | Review & Exam | Test 2 19 Nov. | ||
| 14: 24 Nov. | Clustering w/ Literature Data | Bruce Ch. 7 (pdf) | ||
| 15: 1 & 3 Dec. | Dimension Reduction w/ Literature Data | Video Presentations Due 3 Dec. | ||
| 8 Dec. 2pm–3:50pm | CIS Open House on Scholars Day! | |||
| Final Exam Time | Discuss Videos/Projects | Final Project Due Mon. 14 Dec. at 9am |
Textbooks:
There are no required textbooks for this course. All textbooks are free online or made available to you on Sakai.
- Bruce, Bruce, and Gedeck, Practical Statistics for Data Scientists 2nd ed.
- Downey, Elements of Data Science
- Wilke, Fundamentals of Data Visualization
Note on the schedule
Keep in mind that some of this schedule could change throughout the semester. However, if anything changes I’ll update this page, and I’ll be sure to give you plenty of advance notice.
Software
All projects in this course will be scripted and analyzed using Python, an open source programming language and environment. Specifically, we will be using JupyterHub as our programming environment. No previous experience with Python, statistical software packages, or computer programming is required.