Schedule
- Readings in normal font should be completed and annotated ahead of lecture.
- Readings in italic provide optional additional depth on the material.
- Assignments are listed on the day when we suggest you begin working on them.
- Assessments are listed on the day when they will occur.
Reading sources:
- CN: Class notes written for this course, hosted here.
- PDSH: The Python Data Science Handbook by Vanderplas (2016).
- PDA: Python for Data Analysis, 3rd edition by McKinney (2022).
- BHN: Fairness and Machine Learning: Limitations and Opportunities by Barocas, Hardt, and Narayanan (2023).
Week 1
| Mon Jan. 05 |
Welcome! | ||||
| Course introduction. We get started with Python notebooks, the Python data science ecosystem and the Python NumPy library. | |||||
| Objectives Getting Oriented Navigation |
Reading Course syllabus |
Notes Welcome slides Class notes Live notes In-class slides |
Assignments Working with NumPy |
||
| Tue Jan. 06 |
Python data science ecosystem: Data frames | ||||
| We introduce data wrangling with data frames and the Python Pandas library. | |||||
| Objectives Navigation |
Reading PDSH: Chapters 1-3 |
Notes Class notes Live notes In-class slides |
Assignments Working with Pandas |
||
| Wed Jan. 07 |
Python data science ecosystem: Visualization | ||||
| Data visualization with Python Matplotlib and Seaborn libraries. | |||||
| Objectives Navigation |
Reading PDSH: Chapter 4 |
Notes Class notes Live notes |
Assignments Visualization with Python |
Assessments Quiz 1: NumPy, Pandas | |
| Thu Jan. 08 |
Python data science ecosystem: Data wrangling | ||||
| Data wrangling with Python data science tools. | |||||
| Objectives Navigation |
Reading PDA: Chapters 6-8 |
Notes Class notes Live notes |
Assignments Data Wrangling with Python |
||
Week 2
| Mon Jan. 12 |
Introducing Prediction | ||||
| Predictive modeling and elementary linear regression | |||||
| Objectives Theory Experimentation |
Reading TBD |
Notes Class notes Live notes |
Assignments Score Functions Grid Search vs. Optimization |
Assessments Quiz 2: Visualization, Data wrangling | |
| Tue Jan. 13 |
Feature Maps and Overfitting | ||||
| How to model nonlinear trends, and the perils of overfitting. | |||||
| Objectives Theory Experimentation |
Reading TBD |
Notes Class notes Live notes |
Assignments Bias in a medical risk predictor |
||
| Wed Jan. 14 |
Classification and Logistic Regression | ||||
| Training algorithms that can predict categorical outcomes or guide yes/no decisions. | |||||
| Objectives Theory Experimentation |
Reading TBD |
Notes Class notes Live notes |
Assignments Decision Theory in Classification |
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| Thu Jan. 15 |
Bias and Fairness in Automated Classification | ||||
| A famous, complex case study in algorithmic bias. | |||||
| Objectives Social Responsibility Theory Experimentation |
Reading Machine Bias |
Notes Class notes Live notes |
Assignments Tradeoffs Between Conceptions of Fairness |
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© Michael Linderman and Phil Chodrow, 2026
References
Barocas, Solon, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. Cambridge, Massachusetts: The MIT Press.
McKinney, Wes. 2022. Python for Data Analysis. 3rd ed. O’Reilly Media. https://wesmckinney.com/book/.
Vanderplas, Jacob T. 2016. Python Data Science Handbook: Essential Tools for Working with Data. First edition. Sebastopol, CA: O’Reilly Media, Inc. https://jakevdp.github.io/PythonDataScienceHandbook.