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Schedule Overview
| Week | Class Day | Topic | Before Class | In Class |
|---|---|---|---|---|
| 1 | Monday(Aug 24) | Course introduction; Data science lifecycle | ||
| Wednesday(Aug 26) | Asking questions | Homework 1 Due | Week 1 Quiz | |
| 2 | Monday(Aug 31) | Statistics review; Python foundations; Dataframes and Polars | Homework 2 Due | |
| Wednesday(Sept 2) | Obtaining data | Project checkpoint: question memo | ||
| 3 | Monday(Sept 7) | n/a | Homework 3 Due | |
| Wednesday(Sept 9) | Data formats; Wrangling | (Project checkpoint: groups assembled in class) | Week 3 Quiz | |
| 4 | Monday(Sept 14) | Exploratory analysis | Homework 4 Due | |
| Wednesday(Sept 16) | Descriptive statistics | Coding Exercise 0 Due | ||
| 5 | Monday(Sept 21) | Descriptive visualization | Homework 5 Due | |
| Wednesday(Sept 23) | Descriptive visualization | Project checkpoint: data acquisition | Week 5 Quiz | |
| 6 | Monday(Sept 28) | Visualization rhetoric | Homework 6 Due | |
| Wednesday(Sept 30) | Visualization rhetoric | Coding Exercise 1 Due | ||
| 7 | Monday(Oct 5) | Hypothesis testing | Homework 7 Due | |
| Wednesday(Oct 7) | Estimation; Sampling; Randomness | Project checkpoint: EDA | Week 7 Quiz | |
| 8 | Monday(Oct 12) | Linear regression | Homework 8 Due | |
| Wednesday(Oct 14) | Classification | Coding Exercise 2 Due | ||
| 9 | Monday(Oct 19) | Decision trees | Homework 9 Due | |
| Wednesday(Oct 21) | Support vector machines; Kernels | Project checkpoint: data documentation | Week 9 Quiz | |
| 10 | Monday(Oct 26) | Clustering; Principal component analysis | Homework 10 Due | |
| Wednesday(Oct 28) | Model evaluation | Coding Exercise 3 Due | ||
| 11 | Monday(Nov 2) | Recommendation systems | Homework 11 Due | |
| Wednesday(Nov 4) | A/B testing | Project checkpoint: rhetorical analysis | Week 11 Quiz | |
| 12 | Monday(Nov 9) | Network analysis | Homework 12 Due | |
| Wednesday(Nov 11) | Graphs; Network measures | Coding Exercise 4 Due | ||
| 13 | Monday(Nov 16) | Topic TBD (flex day) | Homework 13 Due | |
| Wednesday(Nov 18) | Topic TBD (flex day) | Project checkpoint: written report | Week 13 Quiz | |
| 14 | Monday(Nov 23) | Project presentations | ||
| Wednesday(Nov 25) | n/a | |||
| 15 | Monday(Nov 30) | Project presentations | ||
| Wednesday(Dec 2) | Project presentations |
Full Schedule
- Unit 1: Wrangling: weeks 1–3
- Unit 2: Exploring: weeks 4–7
- Unit 3: Modeling: weeks 8–10
- Unit 4: Adventuring: weeks 11–15
Unit 1: Wrangling
Week 1 🟧 Harold Washington Library 🟧
- Monday, August 24, 2026 worksheet
- Course introduction
- Data science lifecycle
Source materials
- The Art of Data Science, Chapter 1: Data Analysis as Art
- Ethics in Data Science (course mini-book), Chapter 1: Working Toward Wisdom
- Computational and Inferential Thinking, Chapter 1: What is Data Science?
- Learning Data Science, Chapter 1: The Data Science Lifecycle
- The Art of Data Science, Chapter 2: Epicycles of Analysis
- Wednesday, August 26, 2026 worksheet
- Asking questions
Source materials
- The Art of Data Science, Chapter 3: Stating and Refining the Question
- Ethics in Data Science (course mini-book), Chapter 2: Ethics in Asking Questions
- Leek & Peng (2015), “What is the question?”, Science 347(6228), 1314-1315
- Cross & Roelofsen (2014), “Questions”, Stanford Encyclopedia of Philosophy
Week 2 🟧 LaSalle/Van Buren 🟧
- Monday, August 31, 2026 worksheet
- Statistics review
- Python foundations
- Dataframes and Polars
Source materials
- Learning Data Science, Chapter 4: Modeling with Summary Statistics
- Calling Bullshit, 4.2: Means and Medians (video, 15 min)
- Learning Data Science, Section 10.1: Feature Types
- Learning Data Science, Chapter 6: Working with Dataframes Using pandas
- Data Science: A First Introduction, Chapter 1: Python and Pandas
- Polars user guide
- Wednesday, September 2, 2026 worksheet
- Obtaining data
Source materials
- Learning Data Science, Chapter 2: Questions and Data Scope
- Calling Bullshit, 5.3: Big Data Hubris (video, 6 min)
- The Art of Data Science, Section 2.4: Collecting Information
- Data Science: A First Introduction, Chapter 2: Reading in data locally and from the web
- Ethics in Data Science (course mini-book), Chapter 3: Ethics in Obtaining Data
Week 3 🟧 Quincy 🟧
- Monday, September 7, 2026
- No class (Labor Day)
- Wednesday, September 9, 2026 worksheet
- Data formats
- Wrangling
Source materials
- Learning Data Science, Chapter 8: Wrangling Files
- Learning Data Science, Chapter 9: Wrangling Dataframes
- Data Science: A First Introduction, Chapter 3: Cleaning and Wrangling Data
- Learning Data Science, Chapter 14: Data Exchange
Unit 2: Exploring
Week 4 🟧 Washington/Wells 🟧
- Monday, September 14, 2026 worksheet
- Exploratory analysis
Source materials
- John Tukey, We Need Both Exploratory and Confirmatory
- Learning Data Science, Chapter 10: Exploratory Data Analysis
- The Art of Data Science, Chapter 4: Exploratory Data Analysis
- Wednesday, September 16, 2026 worksheet
- Descriptive statistics
Source materials
- Learning Data Science, Chapter 4: Modeling with Summary Statistics
- The Art of Data Science, Chapter 5: Using Models to Explore Your Data
- Computational and Inferential Thinking, Chapter 14: Why the Mean Matters
Week 5 🟧 Clark/Lake 🟧
- Monday, September 21, 2026 worksheet
- Descriptive visualization
Source materials
- Learning Data Science, Chapter 11: Data Visualization
- Data Science: A First Introduction, Chapter 4: Effective Data Visualization
- Fundamentals of Data Visualization, Chapter 2: Visualizing Data — Mapping Data onto Aesthetics
- Wednesday, September 23, 2026 worksheet
- Descriptive visualization
Source materials
- Learning Data Science, Chapter 11: Data Visualization (continued)
- Fundamentals of Data Visualization, Chapter 5: Directory of Visualizations
- Fundamentals of Data Visualization, Chapter 16: Visualizing Uncertainty
- Computational and Inferential Thinking, Chapter 7: Visualization
- Calling Bullshit, 6.5: Glass Slippers (video, 9 min)
Week 6 🟧 Washington/Wabash 🟧
- Monday, September 28, 2026 worksheet
- Visualization rhetoric
Source materials
- Jessica Hullman and Nick Diakopoulos, Visualization Rhetoric: Framing Effects in Narrative Visualization
- The Art of Data Science, Chapter 10: Communication
- Fundamentals of Data Visualization, Chapter 17: The Principle of Proportional Ink
- Calling Bullshit, Misleading Axes on Graphs
- 6.2: Misleading Axes (video, 8 min)
- Calling Bullshit, The Principle of Proportional Ink
- 6.6: The Principle of Proportional Ink (video, 12 min)
- Wednesday, September 30, 2026 worksheet
- Visualization rhetoric
Source materials
- Fundamentals of Data Visualization, Chapter 23: Balance the Data and the Context
- Fundamentals of Data Visualization, Chapter 29: Telling a Story and Making a Point
- Calling Bullshit, Case Study: The Gender Gap in 100m Running Times
Week 7 🟧 Adams/Wabash 🟧
- Monday, October 5, 2026 worksheet
- Hypothesis testing
- Asynchronous (no in-person meeting)
Source materials
- The Art of Data Science, Chapter 6: Inference — A Primer
- Data Science: A First Introduction, Chapter 10: Statistical Inference
- Learning Data Science, Chapter 17: Theory for Inference and Prediction
- Computational and Inferential Thinking, Chapter 11: Testing Hypotheses
- Calling Bullshit, 4.3: P Values and the Prosecutor’s Fallacy (video, 12 min)
- Wednesday, October 7, 2026 worksheet
- Estimation
- Sampling
- Randomness
- Asynchronous (no in-person meeting)
Source materials
- Learning Data Science, Chapter 3: Simulation and Data Design
- Data Science: A First Introduction, Chapter 10: Statistical Inference (continued)
- Computational and Inferential Thinking, Chapter 9: Randomness
- Computational and Inferential Thinking, Chapter 10: Sampling and Empirical Distributions
- Computational and Inferential Thinking, Chapter 13: Estimation
Unit 3: Modeling
Week 8 🟧 Roosevelt 🟧
- Monday, October 12, 2026 worksheet
- Linear regression
Source materials
- Learning Data Science, Chapter 15: Linear Models
- Data Science: A First Introduction, Chapter 8: Regression II — Linear Regression
- The Art of Data Science, Chapter 7: Formal Modeling
- Wednesday, October 14, 2026 worksheet
- Classification
Source materials
- Learning Data Science, Chapter 19: Classification
- Data Science: A First Introduction, Chapter 5: Classification I — Training and Predicting
- Computational and Inferential Thinking, Chapter 17: Classification
Week 9 🟧 Halsted 🟧
- Monday, October 19, 2026 worksheet
- Decision trees
Source materials
- Data Science: A First Introduction, Chapter 5: Classification I — Training and Predicting (continued)
- A Course in Machine Learning, Chapter 1: Decision Trees
- Introduction to Statistical Learning, Chapter 8: Tree-Based Methods
- Python Data Science Handbook, Decision Trees and Random Forests
- Wednesday, October 21, 2026 worksheet
- Support vector machines
- Kernels
Source materials
- A Course in Machine Learning, Chapter 11: Kernel Methods
- Introduction to Statistical Learning, Chapter 9: Support Vector Machines
- Python Data Science Handbook, Support Vector Machines
Week 10 🟧 Ashland 🟧
- Monday, October 26, 2026 worksheet
- Clustering
- Principal component analysis
Source materials
- Data Science: A First Introduction, Chapter 9: Clustering
- Mining of Massive Datasets, Chapter 7: Clustering
- Mining of Massive Datasets, Chapter 11: Dimensionality Reduction
- Python Data Science Handbook, Principal Component Analysis
- Wednesday, October 28, 2026 worksheet
- Model evaluation
Source materials
- Learning Data Science, Chapter 16: Model Selection
- Data Science: A First Introduction, Chapter 6: Classification II — Evaluation and Tuning
- The Art of Data Science, Chapter 8: Inference vs. Prediction — Implications for Modeling Strategy
- Ethics in Data Science (course mini-book), Chapter 4: Ethics in Understanding
Unit 4: Adventuring
Week 11 🟧 35th/Archer 🟧
- Monday, November 2, 2026 worksheet
- Recommendation systems
Source materials
- Mining of Massive Datasets, Chapter 9: Recommendation Systems
- Ethics in Data Science (course mini-book), Chapter 5: Ethics in Reporting, Decision-Making, and Problem-Solving
- Wednesday, November 4, 2026 worksheet
- A/B testing
Source materials
- Learning Data Science, Chapter 3: Simulation and Data Design (continued)
- Computational and Inferential Thinking, Chapter 2: Causality and Experiments
- Computational and Inferential Thinking, Chapter 12: Comparing Two Samples
- Calling Bullshit, 3.5: Common Causes (video, 10 min)
Week 12 🟧 Western 🟧
- Monday, November 9, 2026 worksheet
- Network analysis
Source materials
- Mining of Massive Datasets, Chapter 10: Mining Social-Network Graphs
- Learning Data Science, Chapter 7: Working with Relations Using SQL
- Wednesday, November 11, 2026 worksheet
- Graphs
- Network measures
Source materials
- Mining of Massive Datasets, Chapter 10: Mining Social-Network Graphs (continued)
- Mining of Massive Datasets, Chapter 5: Link Analysis
Week 13 🟧 Kedzie 🟧
- Monday, November 16, 2026 worksheet
- TBD (margin)
- Wednesday, November 18, 2026 worksheet
- TBD (margin)
Week 14 🟧 Pulaski 🟧
- Monday, November 23, 2026 worksheet
- Project presentations
Source materials
- The Art of Data Science, Chapter 10: Communication
- Ethics in Data Science (course mini-book), Chapter 5: Ethics in Reporting, Decision-Making, and Problem-Solving
- Wednesday, November 25, 2026
- No class (Student Wellness Day)
Week 15 🟧 Midway 🟧
- Monday, November 30, 2026 worksheet
- Project presentations
Source materials
- The Art of Data Science, Chapter 9: Interpreting Your Results
- Data Science: A First Introduction, Chapter 11: Combining Code and Text with Jupyter
- Wednesday, December 2, 2026 worksheet
- Project presentations
Source materials
- Learning Data Science, Chapter 1: The Data Science Lifecycle (revisited)
- Ethics in Data Science (course mini-book), Chapter 6: Conclusion