AI Assisted Python Programming for Finance

Courseware for the 4-day instructor-led course AI Assisted Python Programming for Finance, delivered by Tertiary Infotech Academy Pte Ltd (UEN 201200696W).

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Accredited under the Skills Framework for Financial Services, IBF Standard FSE-DIT-3018-1.1, and eligible for funding under the IBF Standards Training Scheme (IBF-STS) — 50–70% course fee subsidy, capped at S$3,000 per candidate per course, subject to all eligibility criteria being met. Find out more at ibf.org.sg.

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What this course covers

Learners translate real business requirements from financial services into working, tested Python — using AI assistants (Google Colab Gemini, GitHub Copilot, Cursor) to draft code, then reviewing and correcting every line before it is trusted. The course runs from first principles through to a deployed Streamlit analytics application.

#	Topic	Labs
1	Introduction to Python Programming	1–4
2	Data Types and Operators	5–12
3	Problem Solving with Control Structures	13–20
4	Scripting with Function and Lambda	21–28
5	Error Handling Using Exception	29–34
6	Import and Process Finance Data	35–42
7	Aggregate and Visualize Finance Data	43–50
8	Object Oriented Programming	51–56
9	Analyze Finance Data	57–62

62 hands-on labs, every one grounded in a financial task — CPF contributions, loan amortisation, retirement projection, SGX market data, ROE/ROA/PE screening, credit-risk classification, moving-average crossover backtesting, portfolio and instrument modelling, and a progressive Streamlit dashboard capstone.

Learning outcomes

• LO1 Translate business requirements in financial services into Python programming objectives
• LO2 Apply data types, operators and control structures to financial calculation and classification
• LO3 Construct reusable functions and lambda expressions to compute financial ratios
• LO4 Implement error handling that survives missing, zero or malformed market data
• LO5 Import, clean, filter and process financial data with pandas
• LO6 Aggregate and visualise financial data, and publish it as an interactive Streamlit application
• LO7 Apply object-oriented programming to model financial instruments and analysers
• LO8 Analyse financial data with apply/pipe pipelines, assess code for gaps, and test the results

Repository layout

courseware/    slide deck (PPTX + PDF), Lesson Plan and Learner Guide (DOCX + PDF)
labs/          62 lab folders (README.md + starter .py) + the combined Colab notebook
LG-*.md        the Learner Guide as Markdown, generated from the same source as the DOCX

The assessment set (question papers and answer keys) is deliberately excluded from this repository — it is trainer-only material and is distributed through the LMS.

The reference/ folder (the original source deck and notebook this courseware was derived from) is likewise excluded — it is third-party source material, not ours to republish.

Running the labs

Every lab is a self-contained uv project. uv replaces pip, virtualenv and pyenv, and records exact dependency versions — reproducibility is an audit requirement in financial services.

# install uv once
curl -LsSf https://astral.sh/uv/install.sh | sh          # macOS / Linux

# then, per lab
cd labs/lab-03-cpf-contribution-calculator
uv init .
uv add pandas yfinance matplotlib
uv run python cpf_contribution_calculator.py

Prefer the browser? Open labs/AI Assisted Python Programming for Finance-All-Labs.ipynb in Google Colab — every lab is included as an instruction cell plus a code cell.

The Streamlit capstone (labs 49, 50, 61, 62) builds one application progressively:

uv add streamlit
uv run streamlit run app.py

Single-source build

All artifacts are generated from one content module, so the slides, Lesson Plan, Learner Guide and labs cannot drift apart:

course_data.py + data_domain1..9.py
        │
        ├─→ build_slides.py        → courseware/*.pptx
        ├─→ build_lesson_plan.py   → courseware/LP-*.docx
        ├─→ build_learner_guide.py → courseware/LG-*.docx + LG-*.md
        └─→ build_labs.py          → labs/**  + the combined .ipynb

bash .claude/skills/courseware-build/build/build_courseware.sh
python3 .claude/skills/courseware-build/build/build_labs.py

Assessment

Instrument	Format	Duration
Written Assessment (WA)	8 open-ended short-answer questions (K1–K8)	60 min
Practical Test (PP)	7 open-ended practical questions (A1.1–A1.9)	120 min

Open book — slides, Learner Guide and lab files. Passing score 80%. A minimum of 75% attendance is required to be eligible for assessment and IBF-STS funding.

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