Schedule¶
See Course Canvas site for detailed description of graded deliverables and submission details
| # | Topic | Date |
|---|---|---|
| 1 | Course Introduction | Jan 23 2026 |
| 2 | Python Basics | Feb 06 2026 |
| 3 | Data Analysis using Python | Feb 20 2026 |
| 4 | Experimentation + Project proposal due | Mar 06 2026 |
| 5 | Data Analysis using R | Mar 20 2026 |
| 6 | Text Data | Apr 03 2026 |
| 7 | AI & ML: Fairness, Bias, and Inclusiveness | Apr 17 2026 |
| 8 | Optional: Final Project | Apr 24 2026 |
Course Introduction | Jan 23 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 | Feb 06 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 | Feb 20 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 | Mar 06 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 | Mar 06 2026
Programmatic data analysis, pattern detection, and storytelling using R | Mar 20 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 | Apr 03 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 | Apr 17 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 - Apr 24 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.
- Students aiming to complete a final project must submit a brief proposal and receive approval by the completion of Module-3.