Python_Course

Python for Data Science Workshop

A separate, self-contained beginner Python course — not part of the Advanced Quantitative Research Methods (ISLP) course this repository otherwise holds, and with no prerequisites in common. It is vendored here for convenient local access; it keeps its own structure, audience and licence, and its own Colab links (rebased to this repo's copy below).

Two tracks: a 90-minute live introduction, then a self-paced deep dive from Python fundamentals to a first PyTorch model.

🐣 Track 1 — Introduction Session (90 minutes, live)

Five short notebooks, ~75 minutes of content plus ~15 minutes of questions and discussion, with a sixth optional bonus. Every notebook is Colab-ready — no installation needed.

#	Notebook	Time	Covers
1	Welcome to Python	~15 min	What Python is and why it matters for Data Science & AI; notebooks; basic syntax; variables; data types; arithmetic; f-strings
2	Data Structures	~18 min	Lists, tuples/sequences, dictionaries — what each is for and basic operations
3	Control Flow	~12 min	if / elif / else, for loops, while loops — short and conceptual
4	Functions	~12 min	What functions are, defining and calling, parameters, return values, structuring code
5	First Steps with Pandas	~18 min	DataFrames, loading tabular data, inspection, selecting/filtering, simple manipulation, a first chart
✨	A First Look at Seaborn (bonus, optional)	~10 min	The same chart in one line; hue= for colour by category; what a seaborn bar chart averages for you

Session plan with timings and instructor notes: 01_introduction/README.md.

🚀 Track 2 — Advanced & Self-Learning (self-paced, ~12–14 h)

Fifteen notebooks that build directly on Track 1 and go substantially deeper.

Python fundamentals — in depth

#	Notebook	Time
1	Python Basics — types, conversion pitfalls, rounding, float precision	30–35 min
2	Control Structures — loops in depth, convergence, break/continue, try/except	35–40 min
3	Lists and Sequences — slicing, comprehensions, zip, tuples, aliasing	30–40 min
4	Dictionaries and Nested Data — nested structures, counting, JSON	35–45 min
5	Functions and Modules — return vs print, *args/**kwargs, scope, imports	35–40 min

Data science toolkit

#	Notebook	Time
6	NumPy Fundamentals — arrays, vectorisation, broadcasting, axes	45–55 min
7	Pandas Essentials — loc/iloc, boolean masks, groupby	45–55 min
8	Data Cleaning and Preprocessing — missing values, dtypes, duplicates, outliers, scaling, encoding	45–55 min
9	Visualisation with Matplotlib — Figure/Axes, choosing the right chart, subplots	50–60 min
10	Visualisation with Seaborn — long data, faceting, distributions, heatmaps, palettes	50–60 min
11	Exploratory Data Analysis — the EDA workflow on a real dataset	50–60 min

Machine learning & deep learning

#	Notebook	Time
12	Machine Learning Basics — supervised vs unsupervised, features/target, train/test, evaluation, overfitting	45–55 min
13	The Scikit-Learn Workflow — classification, regression, pipelines, GridSearchCV	70–85 min
14	PyTorch Basics — tensors, autograd, a small neural network, the training loop	50–60 min

Capstone

#	Notebook	Time
15	Capstone Project — a full end-to-end analysis	75–105 min

Track overview and learning path: 02_advanced_self_learning/README.md.

☁️ Working in Google Colab

Every notebook is fully Colab-ready: no installation, no downloads, no local files. Click any Open in Colab badge above, or the badge in the first cell of each notebook. Colab opens it read-only from GitHub — use File → Save a copy in Drive once to keep your edits, then Runtime → Run all.

Source

Imported from BridgingAISocietySummerSchools/Python-for-Data-Science-Workshop at commit 94930a6, part of the Bridging AI & Society Summer Schools. Licensed under the MIT License (see LICENSE in this folder) — copyright the Python Data Science Course authors, not Prof. Dr. Christoph Weisser.

For updates, issues or the latest version, use the upstream repository linked above rather than this copy.