| Date | Subject |
|---|---|
| 1: 9/6 | Course intro: syllabus and writing |
| 2: 9/11 | What is Big Data and why do we collect it? |
| 3: 9/13 | Ethical issues in big data |
| 4: 9/18 | Ethical issues in big data |
| 5: 9/20 | Government data collection: the census and bias (data accuracy) |
| 6: 9/25 | Using government data: gerrymandering (algorithms) |
| 7: 9/27 | Government data collection: crime |
| 8: 10/2 | Using government data: criminal sentencing (models) |
| 9: 10/4 | Search data: The parable of Google Flu (correlations) |
| 10: 10/9 | Employment and unemployment (sampling) |
| 11: 10/11 | Hiring and employees (models) |
| 12: 10/16 | Business optimization |
| 13: 10/18 | Targeted advertising (models) |
| 14: 10/23 | Advertising (correlations) |
| 15: 10/25 | Social media |
| 16: 10/30 | Words and Music (machine learning) |
| 17: 11/1 | Political polls (sampling and error) |
| 18: 11/6 | Medical Data |
| 19: 11/8 | Medical image (etc) data (Machine learning) |
| 20: 11/15 | Biodiversity |
| 21: 11/20 | Biodiversity |
| 22: 11/27 | Genome Sequencing* |
| 23: 11/29 | Using genomic data |
| 24: 12/4 | Cities eg transportation |
| 25: 12/6 | Sports (probabilities) |
| 26: 12/11 | Data in art |
*Guest lecture by Dr. Robert Literman