C188 — AI Vibe Coding for Python Financial Analysis

Complete courseware for the commercial two-day instructor-led course AI Vibe Coding for Python Financial Analysis, delivered by Tertiary Infotech Academy Pte Ltd (UEN 201200696W).

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Preview

Deliverables

• Trainer slide deck in PowerPoint and learner slide PDF.
• Lesson Plan in Word and PDF with a two-day, 15-hour timetable.
• Learner Guide in Word and PDF with detailed prompts, commands, expected results, troubleshooting and recovery.
• 62 executable finance-Python labs: a 20-lab facilitated pathway plus 42 optional extensions.
• One Jupyter notebook containing the 20 core labs.

Course structure

Topic	Focus	Core labs
1	Getting Started with AI Vibe Coding for Financial Analysis	1, 4, 35, 36, 37
2	Analysing Financial Data with AI	38, 40, 41, 48, 60
3	Portfolio and Investment Analysis with Vibe Coding	12, 42, 52, 56, 59
4	Building Financial Dashboards and Reports	43, 49, 50, 61, 62

Every lab follows the same disciplined workflow:

Frame → Plan → Generate → Inspect → Verify → Correct → Commit

Learners use Cursor, GitHub Copilot or Claude as an AI pair programmer while retaining responsibility for financial assumptions, numerical correctness, data treatment and interpretation.

Learning outcomes

By the end of the course, learners can:

• Frame a financial analysis question as a safe, testable AI coding request.
• Import, inspect, clean and transform market or portfolio data with pandas.
• Compute and validate returns, volatility, risk measures and time-series indicators.
• Analyse portfolio performance, diversification, drawdowns and simple strategy results.
• Build, review and share an interactive Streamlit financial dashboard.
• Inspect plans and diffs, verify numerical logic, correct failures and retain evidence before committing AI-generated code.

Repository layout

courseware/   PPTX/PDF slides, DOCX/PDF Lesson Plan and Learner Guide
labs/         62 lab folders and the 20-lab core notebook
LG-*.md       searchable Markdown companion to the Learner Guide

Running a lab

Install uv once:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv --version

Then follow the selected lab's README. For example:

cd labs/lab-42-activity-algorithmic-trading-moving-average-cros
uv init .
uv add yfinance pandas numpy matplotlib
uv run python activity_algorithmic_trading_moving_aver.py

Use synthetic or public data only. Never place secrets, credentials or identifiable client data in an AI prompt.

Core notebook

Open labs/AI Vibe Coding for Python Financial Analysis-Core-Labs.ipynb in Jupyter or Google Colab. The individual lab READMEs remain the authoritative step-by-step instructions.

Courseware quality gates

• Course identity: C188, version 1.0, 22 July 2026.
• Duration and sequence: two days, 15 hours, four published topics.
• Slide deck: more than 100 slides in 16:9 format.
• Labs: at least 20 connected activities with executable steps, verification, troubleshooting, boundary challenges, evidence and reflection.
• Documents and slides: rendered and visually inspected before publication.
• Source scan: no programme-compliance or formal-test material in learner deliverables.

© 2026 Tertiary Infotech Academy Pte Ltd (UEN 201200696W). All rights reserved.