| Date | Subject | Broad topic | Readings |
|---|---|---|---|
| 1: 9/7 | Course intro: syllabus and writing rubrics | ||
| 2: 9/12 | What is Big Data and why do we collect it? | Overview | M&C 1 |
| 3: 9/14 | Ethical issues in big data | Ethics | O’Neill 10 |
| 4: 9/19 | Government data collection: the census | ||
| 5: 9/21 | Using government data: gerrymandering* | ||
| 6: 9/26 | Government data use: crime and policing | O’Neill 5 | |
| 7: 9/28 | Political polls** | Sampling | |
| 8: 10/3 | Advertising | Models | O’Neill 4, M&C 4 |
| 9: 10/5 | Advertising | Models | |
| 10: 10/10 | The parable of Google Flu | ||
| 11: 10/12 | Healthcare | ||
| 12: 10/17 | Medical Data | O’Neill 8 | |
| 13: 10/19 | Medical image data | Machine learning | |
| 14: 10/24 | Medical Data | O’Neill 9 | |
| 15: 10/26 | Genome Sequencing | ||
| 16: 10/31 | Using genomic data | ||
| 17: 11/2 | Biodiversity | ||
| 18: 11/7 | Biodiversity | ||
| 19: 11/9 | Cities eg transportation | ||
| 20: 11/14 | Cities eg transportation | ||
| 21: 11/16 | Business*** | Optimization | O’Neill 7, 11 |
| 22: 11/21 | Business | Optimization | O’Neill 7, 11 |
| 23: 11/28 | Financing and hiring | Bias | O’Neill Intro |
| 24: 11/30 | Financing and hiring | Bias | O’Neill Intro |
| 25: 12/5 | Sports | Probabilities | M&C 7-8 |
| 26: 12/7 | Sports | Probabilities | M&C 7-8 |
Guest lecturer from the Library
**Guest lecturer from Political Science
**Guest lecturer from Business