2 Course Schedule
| Date | Topics & Slides | Workshops | Assignments | Supplemental Reading |
|---|---|---|---|---|
| 26 & 28 Aug. | What Is Data? & Responsible Data Collection | Jupyter | Bruce Ch. 1 (Sakai) | |
| 2 & 4 Sept. | Python & Data Wrangling | 0: Practice Python | McKinney, Ch. 3, 5, and 8; Downey Ch. 7 | |
| 9 & 11 Sept. | Exploratory Data Analysis: Summary Statistics & Data Types | 1: Movies 1 | Documentation Assignment Due 11 Sept. | Downey Ch. 2; McKinney Ch. 11 |
| 16 & 18 Sept. | Exploratory Data Analysis: Visualization | 1: Movies 2 | UW Ch. 1, 2, 3, & 6 | |
| 23 & 25 Sept. | Hypothesis Testing: Comparison of Means | 2: Sports 1 | (Begin Test 1) | Downey Ch. 11 and 13 |
| 30 Sept. & 2 Oct. | NO CLASS TUESDAY; Hypothesis Testing: Correlation | 2: Sports 2 | Test 1 2 Oct. | Downey Ch. 9 |
| 7 & 9 Oct. | Supervised Learning: Regression | 3: Business 1 | Downey Ch. 10; McKinney Ch. 12.4 | |
| 14 & 16 Oct. | Supervised Learning: Regression | 3: Business 2 | ||
| 23 Oct. | NO CLASS TUESDAY (FALL BREAK); Ethical Data Science | 3.5: Ethics | ||
| 28 & 30 Oct. | Supervised Learning: Classification | 4: Health 1 | Project Planning Assignment Due 30 Oct. | |
| 4 & 6 Nov. | Supervised Learning: Classification | 4: Health 2 | (Begin Test 2) | |
| 11 & 13 Nov. | Unsupervised Learning: Clustering | 5: Literature 1 | Test 2 13 Nov. | |
| 18 & 20 Nov. | Unsupervised Learning: Dimension Reduction | 5: Literature 2 | ||
| 25 Nov. | Work on Final Projects; NO CLASS THURSDAY (THANKSGIVING) | Video Presentations Due 25 Nov. | ||
| 2 & 4 Dec. | Discuss Projects & Videos, Sample Final Project | |||
| Saturday 13 Dec. 2-5pm | Panel Discussions | Final Project Due 13 Dec. at 2pm |
2.1 Textbooks:
There are no required textbooks for this course. All textbooks are free online or made available to you on Sakai.
- McKinney, Python for Data Analysis
- UW, Visualization Curriculum for Altair
- Downey, Elements of Data Science
- Bruce, Bruce, and Gedeck, Practical Statistics for Data Scientists 2nd ed.
2.1.1 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.
2.2 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.