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Sessions — Teaching Material

One markdown file per session (32 total), with everything a professor needs to prepare and teach a 90-minute class. Files are named session-NN-topic.md and map 1:1 to the sessions in ../weekly-schedule.md.

How a 90-minute session is structured

Block Time Material used
Lecture ~25 min Learning objectives, Key concepts, Detailed lecture notes, Important terminology
Hands-on ~50 min Python examples, Beginner example, Practical Data Science example, In-class activity
Wrap-up ~15 min Lab exercise kickoff, Short assessment questions discussion, Suggested homework

Template — every session file contains these 13 sections

  1. Learning objectives — measurable outcomes for the session.
  2. Key concepts — the 4–8 ideas the lecture revolves around.
  3. Detailed lecture notes — the teachable narrative: why before how.
  4. Important terminology — the vocabulary students must use correctly.
  5. Python examples — code with brief explanations (the core API of the session).
  6. Beginner example — the smallest possible working illustration.
  7. Practical Data Science example — a realistic dataset task tying the topic to real work.
  8. In-class activity — the 50-minute hands-on block, with concrete steps.
  9. Lab exercise — the weekly lab (from ../labs/), with tasks and submission notes.
  10. Common mistakes — frequent errors and how to avoid them.
  11. Short assessment questions — quick checks; usable as quiz/exit-ticket items.
  12. CLO mapping — which course learning outcome(s) this session serves and how.
  13. Suggested homework — reading and practice before the next session.

Conventions

  • Audience: shared between two courses — BS Data Science 3rd-semester students (Introduction to Data Science) and MS Data Science 1st-semester students (Tools and Techniques in Data Science). Concepts are introduced with intuition and plain language first; formal/mathematical detail is added only when it aids understanding.
  • Datasets: prefer built-in data (sns.load_dataset, sklearn.datasets) so examples run without downloads; live APIs are used in session 11.
  • Code style: every example must run as shown; random seeds are set wherever randomness appears.
  • Assessments: Short assessment questions across sessions feed the three module quizzes in ../quizzes/; sample exam tasks come from the Practical Data Science example sections of sessions 7–15, 17–22.
  • Exam/presentation sessions (16, 31, 32) use the same 13-section template adapted to review, exam, and presentation logistics.

Session index

Session File CLO
1 — What is Data Science? session-01-what-is-data-science.md 1
2 — Python Refresher session-02-python-refresher.md 1
3 — Jupyter in Depth session-03-jupyter-in-depth.md 1
4 — Git & GitHub session-04-git-and-github.md 1
5 — NumPy I session-05-numpy-part-1.md 1
6 — NumPy II session-06-numpy-part-2.md 1
7 — Pandas I session-07-pandas-part-1.md 1
8 — Pandas II session-08-pandas-part-2.md 1
9 — Data Cleaning I session-09-data-cleaning-1.md 1
10 — Data Cleaning II session-10-data-cleaning-2.md 1
11 — Data Acquisition session-11-data-acquisition.md 1
12 — Combining Data session-12-combining-data.md 1
13 — Matplotlib session-13-matplotlib.md 1
14 — Seaborn session-14-seaborn.md 1
15 — EDA Workflow session-15-eda-workflow.md 1
16 — Midterm Exam session-16-midterm-exam.md 1, 2
17 — Intro to ML session-17-intro-to-machine-learning.md 2
18 — Linear Regression session-18-linear-regression.md 2
19 — Classification I session-19-classification-1.md 2
20 — Classification II session-20-classification-2.md 2
21 — Clustering session-21-clustering.md 2
22 — Pipelines & CV session-22-pipelines-and-cross-validation.md 2
23 — Project Kickoff session-23-project-kickoff.md 1, 2, 3
24 — Reproducible Workflows session-24-reproducible-workflows.md 3
25 — LLMs for Data Science session-25-llms-for-data-science.md 3
26 — PandasAI session-26-pandasai.md 3
27 — Ollama Local Models session-27-ollama-local-models.md 3
28 — Simple AI Agents session-28-ai-agents.md 3
29 — n8n Automation session-29-n8n-automation.md 3
30 — Ethics & Responsible AI session-30-ethics-and-responsible-ai.md 3
31 — Project Presentations I session-31-project-presentations-1.md 1, 2, 3
32 — Project Presentations II & Final session-32-project-presentations-2-and-final-exam.md 1, 2, 3