Python for AI-Driven Automation and Business Data Science

From your first line of Python to shipping a real AI-driven automation — Python fluency, business data science, machine learning, AI engineering, and production wiring in one hands-on, self-paced curriculum.

10 modules · 38 notebooks (+ 11 optional appendices) · end-to-end

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Two ways to take it

There are two learning paths — pick one:

🎓 Complete course — the full depth

All 10 modules / 38 notebooks (plus 11 optional appendices), worked in spiral order with every exercise, stretch problem, and capstone. ~105 hours. → Start: 00_onboarding/00_master_onboarding.ipynb

🏎️ Fast track — the essentials, condensed

The same teaching trimmed to 13 notebooks, ~14 hours — a credible end-to-end pass you can finish in a few weeks of evenings (Stretch A/B and the bonus projects removed). → Start: fast_track/00_fast_track_onboarding.ipynb

New here? The 5-minute offline demo 00c_see_it_work.ipynb shows what you'll build before the Python begins, and 00b_course_overview.ipynb has the full module map and an interactive time estimator.

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The modules (complete course)

Module	NB	Focus
0 · Onboarding	—	Setup, orientation, 5-min demo
1 · Foundations	1–6	Variables, control flow, lists, dicts, functions, classes
2 · Data Science	7–11	pandas, NumPy, matplotlib, statistics, time series
3 · Real-world I/O	12–13	HTTP/APIs, SQL, data validation
4 · Machine Learning	14–16	scikit-learn, model evaluation, feature engineering
5 · AI Engineering	17–21	prompts, RAG, agents, document processing, AI eval & observability
6 · Production	22–23	packaging notebooks into projects, scheduling
7 · Capstones	24–25	two end-to-end projects (analytics + AI assistant)
8 · Business AI	26–29	digital transformation, architecture, AI-assisted dev, governance
9 · Building AI POCs	30–34	LLM theory, Copilot setup, three POCs, RAG deep dive, vector DBs + agents
10 · Industry Applications	35–38	churn/CLV, fraud, segmentation + recommenders, demand & maintenance

11 optional appendices (classical → deep-learning → foundation-model forecasting, PyTorch, vector stores, RAG/agent frameworks) live beside their modules — all runnable offline.

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What's in the repo

Folder	What
00_onboarding/ … 10_industry_applications/	The complete course — 38 notebooks + 11 optional appendices.
fast_track/	The fast track — 13 trimmed notebooks (~14 h).
quizzes/	9 short multiple-choice quizzes (Modules 1–6 & 8–10).
data/	The three sample CSVs the notebooks read (support_ops, api_log, customer_feedback).
slides/	Course-overview deck + lecture decks (PDF + LaTeX source).
scripts/	Local helpers to run every notebook end-to-end or check NB-number references.
llm_providers.py	Unified interface to OpenAI / Anthropic / Google / Ollama (+ offline MockLLM).
previous_versions/	The legacy flat 19-notebook layout, archived.

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How each notebook is structured

A consistent six-part template: 🎯 objectives + ✅ prerequisites → numbered concept sections (prose + runnable code) → 🧪 practice exercises (incl. a 🐞 debug-me) → 🧠 stretch exercises A–D → 🎁 bonus mini-project → ✅ self-assessment + 🚀 next step. Every exercise — 300+ across the course — ships with a worked solution and the reasoning behind it.

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Setup

Google Colab (easiest): click the “Open in Colab” badge at the top of any notebook — it opens straight from GitHub, no download, and the required libraries are pre-installed. Or use the Open-in-Colab index below to jump straight to any notebook.

Local Jupyter:

python -m venv .venv
source .venv/bin/activate     # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter lab

Tested with Python 3.10+. Module 0 includes an environment-check cell.

The 11 optional appendices demo heavier libraries (PyTorch, Prophet, FAISS, LangChain, …), listed commented-out at the bottom of requirements.txt. Each still runs offline via a built-in stand-in, so install them only to see the real library at work.

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LLM providers

Notebooks 17–21 and 25 run entirely offline with the built-in MockLLM. For real intelligence, swap one line — the unified interface in llm_providers.py supports four providers:

Provider	Class	When
🟢 OpenAI	OpenAILLM(model="gpt-4o-mini")	Reliable default
🟠 Anthropic	AnthropicLLM(model="claude-haiku-4-5-20251001")	Long context, careful tone
🔵 Google	GoogleLLM(model="gemini-2.0-flash")	Cheap at scale
🟣 Ollama	OllamaLLM(model="llama3.2:3b")	Local — no internet, key, or cost

Set the matching *_API_KEY env var for hosted providers (never inline). See 05_ai_engineering/A1_llm_providers_guide.ipynb for setup and cost notes. Never commit API keys.

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🚀 Open any notebook in Colab

Every notebook runs in Google Colab with one click — no install, no download. Expand a module and click a badge.

0 · Onboarding

Notebook	Colab
00_master_onboarding.ipynb
00b_course_overview.ipynb
00c_see_it_work.ipynb

1 · Foundations

Notebook	Colab
01_python_basics.ipynb
02_control_structures.ipynb
03_lists_data_structures.ipynb
04_dictionaries_advanced.ipynb
05_functions_modules.ipynb
06_classes_and_oop.ipynb

2 · Data Science

Notebook	Colab
07_pandas_fundamentals.ipynb
08_numpy_fundamentals.ipynb
09_matplotlib_basics.ipynb
10_statistics_basics.ipynb
11_time_series_forecasting.ipynb
A1_forecasting_classical.ipynb
A2_forecasting_prophet_libraries.ipynb
A3_forecasting_deep_learning.ipynb
A4_forecasting_foundation_models.ipynb

3 · Real-world I/O

Notebook	Colab
12_apis_and_http.ipynb
13_sql_fundamentals.ipynb

4 · Machine Learning

Notebook	Colab
14_sklearn_basics.ipynb
15_model_evaluation.ipynb
16_feature_engineering.ipynb
A1_pytorch_foundations.ipynb
A2_pytorch_vision_and_sequences.ipynb
A3_pytorch_fine_tuning.ipynb
A4_tabpfn_priorlab.ipynb

5 · AI Engineering

Notebook	Colab
17_ai_workflows.ipynb
18_embeddings_retrieval.ipynb
19_tools_and_agents.ipynb
20_document_processing.ipynb
21_ai_evaluation_observability.ipynb
A1_llm_providers_guide.ipynb
A2_vector_stores_survey.ipynb
A3_rag_and_agent_frameworks.ipynb

6 · Production

Notebook	Colab
22_from_notebook_to_project.ipynb
23_scheduling_orchestration.ipynb

7 · Capstones

Notebook	Colab
24_capstone_analytics.ipynb
25_capstone_ai_assistant.ipynb

8 · Business AI

Notebook	Colab
26_digital_transformation.ipynb
27_architecture_patterns.ipynb
28_ai_assisted_software_development.ipynb
29_bpm_governance_poc_mvp.ipynb

9 · Building AI POCs

Notebook	Colab
30_llm_fundamentals.ipynb
31_from_setup_to_first_poc.ipynb
32_three_pocs_growing_complexity.ipynb
33_rag_pipeline_deep_dive.ipynb
34_vector_db_and_agentic_ai.ipynb

10 · Industry Applications

Notebook	Colab
35_churn_clv_retention.ipynb
36_fraud_anomaly_detection.ipynb
37_segmentation_recommenders.ipynb
38_demand_maintenance.ipynb

🏎️ Fast track

Notebook	Colab
00_fast_track_onboarding.ipynb
01_python_basics.ipynb
02_control_structures.ipynb
03_lists_and_dicts.ipynb
04_functions.ipynb
05_classes_basics.ipynb
06_pandas_fundamentals.ipynb
07_visualization_and_stats.ipynb
08_sklearn_basics.ipynb
09_apis_and_sql.ipynb
10_ai_workflows.ipynb
11_embeddings_and_rag.ipynb
12_tools_and_agents.ipynb
13_notebook_to_project.ipynb

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Contributing & licence

Spotted a bug or an unclear explanation? Open an issue or PR. Licensed MIT (see LICENSE) — use freely for learning, teaching, or anything else.

Happy coding.