Introduction to Data Science¶
Tools and Techniques in Data Science — BS Data Science, 3rd semester · 16 weeks · 2 sessions/week · 90-minute sessions · Python
From messy CSV files to a working data app: this site collects every lecture, lab, assignment, and guide for the course in one place. Follow the learning path below in order, or jump to any section from the menu.
Course at a glance¶
| Program | BS Data Science — 3rd Semester |
| Format | 16 weeks × 2 sessions/week × 90 min (32 sessions) |
| Language / stack | Python · Jupyter · Git/GitHub · NumPy · Pandas · Matplotlib · Seaborn · scikit-learn · Streamlit · PandasAI · Ollama · n8n |
| Modules | A — Data handling & EDA (Weeks 1–8, CLO-1) · B — Machine learning basics (Weeks 9–12, CLO-2) · C — AI-assisted workflows (Weeks 13–16, CLO-3) |
Start with the Course Overview, the CLOs, and the Course Schedule to see how the semester is organized.
The learning path¶
The course is deliberately progressive — each stage builds on the last. Work through the path top to bottom; links open the session, its notebook, or the guide you need at that point.
- Python — types, control flow, functions, comprehensions → Session 2 · Notebook 01 · Python Setup
- NumPy — arrays, indexing, universal functions, masks, random → Sessions 5–6 · Notebook 02
- Pandas — Series, DataFrames, filtering, reading/writing files → Sessions 7–8 · Notebook 03
- Data Cleaning — missing values, duplicates, dtypes, string methods, reshaping → Sessions 9–10 · Notebook 04 · Assignment 1
- EDA — summary stats, distributions, correlations, the EDA workflow → Session 15 · Notebook 07
- Visualization — Matplotlib & Seaborn → Sessions 13–14 · Notebook 06
- Git — version control & GitHub workflow → Session 4 · Notebook 08 · Git & GitHub Guide
- APIs — data acquisition with
requests, JSON, scraping ethics → Session 11 · Notebook 09 - Machine Learning — scikit-learn, regression, classification, clustering, evaluation → Sessions 17–22 · Notebooks 10–13 · Assignment 2
- Streamlit — turn Python scripts into interactive data apps → Streamlit Guide (full module)
- PandasAI — ask questions about your data in plain language → PandasAI Guide · Session 26
- LLMs — how large language models work, prompting, and their limits → LLM & Ollama Guide · Session 25
- Ollama — run models locally on your own machine → LLM & Ollama Guide · Session 27
- AI Agents — tool calling and simple agent loops → AI Agents Guide · Session 28
- n8n — visual automation workflows → n8n Guide · Session 29
- Final Project — everything end to end, presented in Week 16 → Project overview · Kickoff session
Why this order?
You first learn to read data (Pandas), then to fix it (cleaning), look at it (EDA + visualization), model it (machine learning), and finally to share it (Streamlit apps). Only after all that do we add AI assistance (PandasAI → LLMs → Ollama → agents → n8n) — because AI tools are only trustworthy once you can verify their output yourself.
Where to go next¶
| I want to… | Start here |
|---|---|
| See the whole semester | Course Schedule |
| Prepare one lecture or catch up | Weekly Lectures |
| Practice coding | Labs |
| Check graded work | Assignments · Quizzes · Exams |
| Build the capstone | Final Project |
| Set up my computer | Python Setup |
| Fix a common problem | FAQ |
| Add AI to a data app | Streamlit Guide · Module C |
Who this site is for¶
- Students — follow the learning path, do every lab week by week, and submit work through Git/GitHub as each deliverable describes. Answer keys and instructor solutions are intentionally not published here; they are distributed separately by your instructor.
- Instructors — the full teaching pack (sessions, notebooks, module C, assessment banks, project rubric) lives in the same repository; see Publish this Website to host or customize this site.
Course materials are offered under CC BY 4.0 unless stated otherwise per file — see the repository README for the full license note.