Schedule¶
See Course Canvas site for detailed description of graded deliverables and submission details
| # | Topic | Completion Date |
|---|---|---|
| 1 | Course Introduction | May 14 2026 |
| 2 | Python Basics | May 21 2026 |
| 3 | Data Analysis using Python | Jun 04 2026 |
| 4 | Experimentation + Project proposal due | Jun 18 2026 |
| 5 | Data Analysis using R | July 2 2026 |
| 6 | Text Data | July 16 2026 |
| 7 | AI & ML: Fairness, Bias, and Inclusiveness | July 23 2026 |
| 8 | Optional: Final Project | July 23 2026 |
Course Introduction | May 14 2026
Course Introduction¶
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Go through the course syllabus, understand expectations, get familiar with the logistics of the course, and join the learning community (on Canvas).
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Required exercises:
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Create an AI-generated audio overview of the course syllabus using NotebookLM.
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Please see the LinkedIn Learning Courses if you need help getting started:
- NotebookLM for Research
- NotebookLM: First Look
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Python Basics | May 21 2026
Python Basics¶
Required exercises:
- Learn basic Python using LinkedIn Learning courses posted on Canvas.
Challenge exercise:
- Develop original art using Google Colab and ColabTurtlePlus.
Programmatic data analysis, pattern detection, and storytelling using Python | Jun 04 2026
Data Analysis using Python¶
Required exercises:
- Generate plots using Python
Challenge exercises:
- 3-minute story telling video
- Self-directed study of additional visualization tools (e.g., Plotly, Tableau, PowerBI) or modeling tools (e.g., Scikit)
Experimentation | Jun 18 2026
Experimentation¶
Required exercises:
- Case analysis
Challenge exercises:
- Design an experiment Propose a causal question and a natural experiment
Project proposal due for students aiming to complete a final course project | Jun 18 2026
Programmatic data analysis, pattern detection, and storytelling using R | Jul 02 2026
Data Analysis using R¶
Required exercises:
- Learn R using LinkedIn Learning courses posted on Canvas
Challenge exercises:
- Generate plots using R Self-directed study of regression analysis using R
Text Data | July 16 2026
Text Data¶
Required exercises:
- Conduct X (formerly Twitter) data analysis using Python or R
Challenge exercises:
- Conduct text mining analysis in R
AI and machine learning fairness, bias, and inclusiveness | July 23 2026
AI & ML: Fairness, Bias, and Inclusiveness¶
Required exercises:
- Complete a LinkedIn Learning course on responsible AI. Apply the insights and analyze your NotebookLM experience through the lens of responsible AI. Submit a 2-page evaluation.
Challenge exercises:
- Read posted research papers and answer discussion questions
Homestretch: Complete final Project - July 23 2026
` Notes:
- The schedule shows a default learning pace of about 2 weeks per module, so that students get ample time for completing all assigned exercises. Students are welcome to proceed at a faster pace if desired.
- Please note that the modules closer to the finals week goes through some time compression to free up students during the finals week. Please take this into account when you plan your study schedule.
- Students aiming to complete a final project must submit a brief proposal and receive approval by the completion of Module-3.