Weekly Schedule

FIN510 12-week learning journey

Author

Professor Barry Quinn

1 FIN510 Weekly Schedule

1.1 Course Timeline Overview

Show code

gantt
    title FIN510: Financial Technology and Data Science
    dateFormat  X
    axisFormat %s
    
    section Foundation
    Week 1 - Fintech & Python :w1, 0, 1
    Week 2 - Data APIs :w2, 1, 1
    Week 3 - Time Series :w3, 2, 1
    Week 4 - Statistics :w4, 3, 1
    
    section Core Methods
    Week 5 - Trading + MCQ1 :crit, w5, 4, 1
    Week 6 - ML Fundamentals :w6, 5, 1
    Week 7 - Advanced ML + Case :crit, w7, 6, 1
    Week 8 - AI in Finance :w8, 7, 1
    
    section AI Applications
    Week 9 - NLP Finance :w9, 8, 1
    Week 10 - Generative AI :w10, 9, 1
    Week 11 - Production :w11, 10, 1
    Week 12 - Future + MCQ2 :crit, w12, 11, 1

1.2 Detailed Weekly Breakdown

1.2.1 📅 Week 1: Fintech Ecosystem & Python Foundations

Date: September 8-12, 2025

1.2.1.1 Learning Objectives

Understand the fintech landscape and career opportunities
Set up professional Python development environment
Master basic financial calculations in Python

1.2.1.2 Content Coverage

Lecture: Fintech revolution and market disruption
Lab: Python environment setup and basic financial calculations
Reading: Hilpisch “Python for Finance” Chapters 1-2

1.2.1.3 Key Activities

Install Anaconda and configure Jupyter Lab
Create GitHub account and basic Git setup
Implement compound interest and return calculations
Introduction to pandas for financial data

1.2.1.4 Deliverables

✅ Working Python environment
✅ GitHub repository setup
✅ Basic financial calculator functions

1.2.2 📅 Week 2: Financial Data Acquisition & APIs

Date: September 15-19, 2025

1.2.2.1 Learning Objectives

Connect to financial data sources programmatically
Build automated data collection pipelines
Handle data quality issues and preprocessing

1.2.2.2 Content Coverage

Lecture: Market data sources and API integration
Lab: Build comprehensive data acquisition system
Reading: Hilpisch “Python for Finance” Chapters 3-4

1.2.2.3 Key Activities

Yahoo Finance and Alpha Vantage API integration
Data quality assessment and cleaning techniques
Multi-asset data collection and synchronization
Error handling and rate limiting

1.2.2.4 Deliverables

✅ Financial data pipeline
✅ Data quality validation system
✅ Multi-source data integration

1.2.3 📅 Week 3: Time Series Analysis & Visualization

Date: September 22-26, 2025

1.2.3.1 Learning Objectives

Analyze financial time series characteristics
Create professional financial visualizations
Implement technical analysis indicators

1.2.3.2 Content Coverage

Lecture: Time series properties and modeling
Lab: Interactive financial dashboard creation
Reading: Hilpisch “Python for Finance” Chapters 5-6

1.2.3.3 Key Activities

Volatility clustering and autocorrelation analysis
Technical indicators (SMA, RSI, Bollinger Bands)
Interactive visualizations with Plotly
Stationarity testing and differencing

1.2.3.4 Deliverables

✅ Interactive financial dashboard
✅ Technical analysis toolkit
✅ Time series diagnostic functions

1.2.4 📅 Week 4: Statistical Analysis & Risk Management

Date: September 29 - October 3, 2025

1.2.4.1 Learning Objectives

Apply statistical methods to financial data
Calculate and interpret risk metrics
Implement portfolio optimization techniques

1.2.4.2 Content Coverage

Lecture: Financial statistics and risk measurement
Lab: Portfolio optimization and risk analysis
Reading: Hilpisch “Python for Finance” Chapters 7-8

1.2.4.3 Key Activities

Modern Portfolio Theory implementation
VaR and CVaR calculations
Monte Carlo simulations for risk
Correlation analysis and diversification

1.2.4.4 Deliverables

✅ Portfolio optimization system
✅ Risk measurement toolkit
✅ Monte Carlo simulation framework

📚 Assessment Prep: Review for Week 5 MCQ Test

1.2.5 📅 Week 5: Algorithmic Trading Fundamentals ⚡

Date: October 6-10, 2025

1.2.5.1 Learning Objectives

Design and implement trading strategies
Build backtesting frameworks
Evaluate strategy performance with proper metrics

1.2.5.2 Content Coverage

Lecture: Trading strategy development and backtesting
Lab: Build and backtest momentum trading strategy
Reading: Hilpisch “Python for Finance” Chapter 15 + “AI in Finance” Chapters 2-3

1.2.5.3 Key Activities

Momentum and mean reversion strategy implementation
Transaction cost modeling and slippage
Performance metrics and risk-adjusted returns
Strategy optimization and parameter tuning

1.2.5.4 Deliverables

✅ Complete trading strategy implementation
✅ Backtesting framework with realistic constraints
✅ Performance analytics dashboard

📝 ASSESSMENT: MCQ Test 1 (17%) - Python foundations and financial analysis

1.2.6 📅 Week 6: Machine Learning for Finance I

Date: October 13-17, 2025

1.2.6.1 Learning Objectives

Apply supervised learning to financial prediction
Master feature engineering for financial data
Understand cross-validation for time series

1.2.6.2 Content Coverage

Lecture: ML fundamentals and financial applications
Lab: Stock price prediction with multiple algorithms
Reading: Hilpisch “Python for Finance” Chapter 13 + “AI in Finance” Chapters 4-5

1.2.6.3 Key Activities

Feature engineering for financial prediction
Linear models, Random Forest, and SVM implementation
Time series cross-validation techniques
Model selection and hyperparameter tuning

1.2.6.4 Deliverables

✅ ML prediction pipeline
✅ Feature engineering toolkit
✅ Model comparison framework

1.2.7 📅 Week 7: Advanced Machine Learning ⚡

Date: October 20-24, 2025

1.2.7.1 Learning Objectives

Compare advanced ML algorithms for finance
Implement proper model evaluation techniques
Address bias and fairness in financial ML

1.2.7.2 Content Coverage

Lecture: Advanced ML algorithms and evaluation
Lab: Comprehensive model comparison and bias analysis
Reading: Hilpisch “AI in Finance” Chapters 6-7

1.2.7.3 Key Activities

XGBoost, LightGBM, and Neural Network implementation
Model interpretability and SHAP values
Bias detection in credit scoring models
Production deployment considerations

1.2.7.4 Deliverables

✅ Advanced ML model comparison
✅ Bias detection framework
✅ Model interpretability dashboard

📝 ASSESSMENT: Case Study Analysis (16%) - ML model evaluation

1.2.8 📅 Week 8: AI in Financial Services

Date: October 27-31, 2025

1.2.8.1 Learning Objectives

Understand AI’s transformative role in finance
Implement neural networks for financial applications
Address ethical considerations and regulatory requirements

1.2.8.2 Content Coverage

Lecture: AI revolution in financial services
Lab: Neural network implementation for finance
Reading: Hilpisch “AI in Finance” Chapters 8-9

1.2.8.3 Key Activities

Deep neural networks for financial prediction
Ethical AI frameworks and bias mitigation
Regulatory considerations (EU AI Act, Model Risk Management)
AI governance and monitoring systems

1.2.8.4 Deliverables

✅ Neural network financial application
✅ AI ethics framework
✅ Model governance documentation

1.2.9 📅 Week 9: Natural Language Processing in Finance

Date: November 3-7, 2025

1.2.9.1 Learning Objectives

Apply NLP techniques to financial text data
Implement sentiment analysis for market prediction
Process financial documents automatically

1.2.9.2 Content Coverage

Lecture: NLP applications in financial analysis
Lab: Build sentiment analysis trading system
Reading: Hilpisch “AI in Finance” Chapters 10-11

1.2.9.3 Key Activities

Multi-model sentiment analysis implementation
News sentiment correlation with stock prices
Earnings call transcript analysis
Social media sentiment monitoring

1.2.9.4 Deliverables

✅ Sentiment analysis platform
✅ News-based trading signals
✅ Financial document processor

1.2.10 📅 Week 10: Generative AI & Large Language Models

Date: November 10-14, 2025

1.2.10.1 Learning Objectives

Understand generative AI applications in finance
Implement LLMs for financial analysis and reporting
Address model limitations and risks

1.2.10.2 Content Coverage

Lecture: Generative AI and LLMs in financial services
Lab: Build LLM-powered financial analysis system
Reading: Hilpisch “AI in Finance” Chapters 12-13

1.2.10.3 Key Activities

OpenAI API integration for financial tasks
Automated investment research report generation
ESG analysis with large language models
Prompt engineering optimization for finance

1.2.10.4 Deliverables

✅ LLM-powered financial analyst
✅ Automated research report system
✅ ESG analysis framework

1.2.11 📅 Week 11: Production Systems & Deployment

Date: November 17-21, 2025

1.2.11.1 Learning Objectives

Deploy ML models in production environments
Build APIs for financial applications
Implement monitoring and model governance

1.2.11.2 Content Coverage

Lecture: Production deployment and MLOps for finance
Lab: Deploy trading strategy as web API
Reading: Hilpisch “Python for Finance” Chapter 16 + “AI in Finance” Chapters 14-15

1.2.11.3 Key Activities

Flask/FastAPI development for financial services
Docker containerization and cloud deployment
Model monitoring and drift detection
CI/CD pipelines for financial applications

1.2.11.4 Deliverables

✅ Production-deployed financial API
✅ Model monitoring system
✅ CI/CD pipeline implementation

📚 Final Project Work: Intensive development session

1.2.12 📅 Week 12: Future of Fintech & Presentations ⚡

Date: November 24-28, 2025

1.2.12.1 Learning Objectives

Explore emerging fintech trends and technologies
Present final projects professionally
Plan career development in financial technology

1.2.12.2 Content Coverage

Lecture: Emerging technologies and career opportunities
Lab: Final project presentations and peer review
Reading: Hilpisch “AI in Finance” Chapter 16

1.2.12.3 Key Activities

Final project presentations (10 minutes per student/group)
Peer evaluation and constructive feedback
Industry guest speaker session
Career planning and networking session

1.2.12.4 Deliverables

✅ Professional project presentation
✅ Peer evaluation feedback
✅ Career development plan

📝 ASSESSMENT: MCQ Test 2 (17%) - AI applications and advanced topics

1.3 Assessment Calendar

Week	Assessment	Weight	Format	Duration
5	MCQ Test 1	17%	Multiple choice	40 minutes
7	Case Study	16%	Analysis + MCQ	40 minutes
12	MCQ Test 2	17%	Multiple choice	40 minutes
15	Final Project	50%	Report + Code	2000 words

1.4 Reading Schedule

1.4.1 Hilpisch “Python for Finance” (2019)

Weeks 1-2: Chapters 1-4 (Foundations)
Weeks 3-4: Chapters 5-8 (Analysis)
Weeks 5-6: Chapters 13-15 (ML & Trading)
Week 11: Chapter 16 (Production)

1.4.2 Hilpisch “Artificial Intelligence in Finance” (2020)

Week 5: Chapters 1-3 (AI Foundations)
Weeks 6-7: Chapters 4-7 (Neural Networks)
Weeks 8-9: Chapters 8-11 (Advanced AI)
Weeks 10-12: Chapters 12-16 (Applications & Future)

1.5 Important Dates

September 8: Course begins
October 10: MCQ Test 1 (17%)
October 24: Case Study Assessment (16%)
November 28: MCQ Test 2 (17%)
January 14, 2026: Final Project due (50%)
January 21: Feedback and grades released

Schedule subject to minor adjustments. All changes will be announced via Blackboard and course website.

--- title: "Weekly Schedule" subtitle: "FIN510 12-week learning journey" author: "Professor Barry Quinn" --- # FIN510 Weekly Schedule ## Course Timeline Overview ```{mermaid} gantt title FIN510: Financial Technology and Data Science dateFormat X axisFormat %s section Foundation Week 1 - Fintech & Python :w1, 0, 1 Week 2 - Data APIs :w2, 1, 1 Week 3 - Time Series :w3, 2, 1 Week 4 - Statistics :w4, 3, 1 section Core Methods Week 5 - Trading + MCQ1 :crit, w5, 4, 1 Week 6 - ML Fundamentals :w6, 5, 1 Week 7 - Advanced ML + Case :crit, w7, 6, 1 Week 8 - AI in Finance :w8, 7, 1 section AI Applications Week 9 - NLP Finance :w9, 8, 1 Week 10 - Generative AI :w10, 9, 1 Week 11 - Production :w11, 10, 1 Week 12 - Future + MCQ2 :crit, w12, 11, 1 ``` --- ## Detailed Weekly Breakdown ### 📅 **Week 1**: Fintech Ecosystem & Python Foundations **Date**: September 8-12, 2025 #### Learning Objectives - Understand the fintech landscape and career opportunities - Set up professional Python development environment - Master basic financial calculations in Python #### Content Coverage - **Lecture**: Fintech revolution and market disruption - **Lab**: Python environment setup and basic financial calculations - **Reading**: Hilpisch "Python for Finance" Chapters 1-2 #### Key Activities - Install Anaconda and configure Jupyter Lab - Create GitHub account and basic Git setup - Implement compound interest and return calculations - Introduction to pandas for financial data #### Deliverables - ✅ Working Python environment - ✅ GitHub repository setup - ✅ Basic financial calculator functions --- ### 📅 **Week 2**: Financial Data Acquisition & APIs **Date**: September 15-19, 2025 #### Learning Objectives - Connect to financial data sources programmatically - Build automated data collection pipelines - Handle data quality issues and preprocessing #### Content Coverage - **Lecture**: Market data sources and API integration - **Lab**: Build comprehensive data acquisition system - **Reading**: Hilpisch "Python for Finance" Chapters 3-4 #### Key Activities - Yahoo Finance and Alpha Vantage API integration - Data quality assessment and cleaning techniques - Multi-asset data collection and synchronization - Error handling and rate limiting #### Deliverables - ✅ Financial data pipeline - ✅ Data quality validation system - ✅ Multi-source data integration --- ### 📅 **Week 3**: Time Series Analysis & Visualization **Date**: September 22-26, 2025 #### Learning Objectives - Analyze financial time series characteristics - Create professional financial visualizations - Implement technical analysis indicators #### Content Coverage - **Lecture**: Time series properties and modeling - **Lab**: Interactive financial dashboard creation - **Reading**: Hilpisch "Python for Finance" Chapters 5-6 #### Key Activities - Volatility clustering and autocorrelation analysis - Technical indicators (SMA, RSI, Bollinger Bands) - Interactive visualizations with Plotly - Stationarity testing and differencing #### Deliverables - ✅ Interactive financial dashboard - ✅ Technical analysis toolkit - ✅ Time series diagnostic functions --- ### 📅 **Week 4**: Statistical Analysis & Risk Management **Date**: September 29 - October 3, 2025 #### Learning Objectives - Apply statistical methods to financial data - Calculate and interpret risk metrics - Implement portfolio optimization techniques #### Content Coverage - **Lecture**: Financial statistics and risk measurement - **Lab**: Portfolio optimization and risk analysis - **Reading**: Hilpisch "Python for Finance" Chapters 7-8 #### Key Activities - Modern Portfolio Theory implementation - VaR and CVaR calculations - Monte Carlo simulations for risk - Correlation analysis and diversification #### Deliverables - ✅ Portfolio optimization system - ✅ Risk measurement toolkit - ✅ Monte Carlo simulation framework **📚 Assessment Prep**: Review for Week 5 MCQ Test --- ### 📅 **Week 5**: Algorithmic Trading Fundamentals ⚡ **Date**: October 6-10, 2025 #### Learning Objectives - Design and implement trading strategies - Build backtesting frameworks - Evaluate strategy performance with proper metrics #### Content Coverage - **Lecture**: Trading strategy development and backtesting - **Lab**: Build and backtest momentum trading strategy - **Reading**: Hilpisch "Python for Finance" Chapter 15 + "AI in Finance" Chapters 2-3 #### Key Activities - Momentum and mean reversion strategy implementation - Transaction cost modeling and slippage - Performance metrics and risk-adjusted returns - Strategy optimization and parameter tuning #### Deliverables - ✅ Complete trading strategy implementation - ✅ Backtesting framework with realistic constraints - ✅ Performance analytics dashboard **📝 ASSESSMENT**: MCQ Test 1 (17%) - Python foundations and financial analysis --- ### 📅 **Week 6**: Machine Learning for Finance I **Date**: October 13-17, 2025 #### Learning Objectives - Apply supervised learning to financial prediction - Master feature engineering for financial data - Understand cross-validation for time series #### Content Coverage - **Lecture**: ML fundamentals and financial applications - **Lab**: Stock price prediction with multiple algorithms - **Reading**: Hilpisch "Python for Finance" Chapter 13 + "AI in Finance" Chapters 4-5 #### Key Activities - Feature engineering for financial prediction - Linear models, Random Forest, and SVM implementation - Time series cross-validation techniques - Model selection and hyperparameter tuning #### Deliverables - ✅ ML prediction pipeline - ✅ Feature engineering toolkit - ✅ Model comparison framework --- ### 📅 **Week 7**: Advanced Machine Learning ⚡ **Date**: October 20-24, 2025 #### Learning Objectives - Compare advanced ML algorithms for finance - Implement proper model evaluation techniques - Address bias and fairness in financial ML #### Content Coverage - **Lecture**: Advanced ML algorithms and evaluation - **Lab**: Comprehensive model comparison and bias analysis - **Reading**: Hilpisch "AI in Finance" Chapters 6-7 #### Key Activities - XGBoost, LightGBM, and Neural Network implementation - Model interpretability and SHAP values - Bias detection in credit scoring models - Production deployment considerations #### Deliverables - ✅ Advanced ML model comparison - ✅ Bias detection framework - ✅ Model interpretability dashboard **📝 ASSESSMENT**: Case Study Analysis (16%) - ML model evaluation --- ### 📅 **Week 8**: AI in Financial Services **Date**: October 27-31, 2025 #### Learning Objectives - Understand AI's transformative role in finance - Implement neural networks for financial applications - Address ethical considerations and regulatory requirements #### Content Coverage - **Lecture**: AI revolution in financial services - **Lab**: Neural network implementation for finance - **Reading**: Hilpisch "AI in Finance" Chapters 8-9 #### Key Activities - Deep neural networks for financial prediction - Ethical AI frameworks and bias mitigation - Regulatory considerations (EU AI Act, Model Risk Management) - AI governance and monitoring systems #### Deliverables - ✅ Neural network financial application - ✅ AI ethics framework - ✅ Model governance documentation --- ### 📅 **Week 9**: Natural Language Processing in Finance **Date**: November 3-7, 2025 #### Learning Objectives - Apply NLP techniques to financial text data - Implement sentiment analysis for market prediction - Process financial documents automatically #### Content Coverage - **Lecture**: NLP applications in financial analysis - **Lab**: Build sentiment analysis trading system - **Reading**: Hilpisch "AI in Finance" Chapters 10-11 #### Key Activities - Multi-model sentiment analysis implementation - News sentiment correlation with stock prices - Earnings call transcript analysis - Social media sentiment monitoring #### Deliverables - ✅ Sentiment analysis platform - ✅ News-based trading signals - ✅ Financial document processor --- ### 📅 **Week 10**: Generative AI & Large Language Models **Date**: November 10-14, 2025 #### Learning Objectives - Understand generative AI applications in finance - Implement LLMs for financial analysis and reporting - Address model limitations and risks #### Content Coverage - **Lecture**: Generative AI and LLMs in financial services - **Lab**: Build LLM-powered financial analysis system - **Reading**: Hilpisch "AI in Finance" Chapters 12-13 #### Key Activities - OpenAI API integration for financial tasks - Automated investment research report generation - ESG analysis with large language models - Prompt engineering optimization for finance #### Deliverables - ✅ LLM-powered financial analyst - ✅ Automated research report system - ✅ ESG analysis framework --- ### 📅 **Week 11**: Production Systems & Deployment **Date**: November 17-21, 2025 #### Learning Objectives - Deploy ML models in production environments - Build APIs for financial applications - Implement monitoring and model governance #### Content Coverage - **Lecture**: Production deployment and MLOps for finance - **Lab**: Deploy trading strategy as web API - **Reading**: Hilpisch "Python for Finance" Chapter 16 + "AI in Finance" Chapters 14-15 #### Key Activities - Flask/FastAPI development for financial services - Docker containerization and cloud deployment - Model monitoring and drift detection - CI/CD pipelines for financial applications #### Deliverables - ✅ Production-deployed financial API - ✅ Model monitoring system - ✅ CI/CD pipeline implementation **📚 Final Project Work**: Intensive development session --- ### 📅 **Week 12**: Future of Fintech & Presentations ⚡ **Date**: November 24-28, 2025 #### Learning Objectives - Explore emerging fintech trends and technologies - Present final projects professionally - Plan career development in financial technology #### Content Coverage - **Lecture**: Emerging technologies and career opportunities - **Lab**: Final project presentations and peer review - **Reading**: Hilpisch "AI in Finance" Chapter 16 #### Key Activities - Final project presentations (10 minutes per student/group) - Peer evaluation and constructive feedback - Industry guest speaker session - Career planning and networking session #### Deliverables - ✅ Professional project presentation - ✅ Peer evaluation feedback - ✅ Career development plan **📝 ASSESSMENT**: MCQ Test 2 (17%) - AI applications and advanced topics --- ## Assessment Calendar | Week | Assessment | Weight | Format | Duration | |------|------------|--------|---------|----------| | **5** | MCQ Test 1 | 17% | Multiple choice | 40 minutes | | **7** | Case Study | 16% | Analysis + MCQ | 40 minutes | | **12** | MCQ Test 2 | 17% | Multiple choice | 40 minutes | | **15** | Final Project | 50% | Report + Code | 2000 words | ## Reading Schedule ### Hilpisch "Python for Finance" (2019) - **Weeks 1-2**: Chapters 1-4 (Foundations) - **Weeks 3-4**: Chapters 5-8 (Analysis) - **Weeks 5-6**: Chapters 13-15 (ML & Trading) - **Week 11**: Chapter 16 (Production) ### Hilpisch "Artificial Intelligence in Finance" (2020) - **Week 5**: Chapters 1-3 (AI Foundations) - **Weeks 6-7**: Chapters 4-7 (Neural Networks) - **Weeks 8-9**: Chapters 8-11 (Advanced AI) - **Weeks 10-12**: Chapters 12-16 (Applications & Future) --- ## Important Dates ::: {.important-dates} - **September 8**: Course begins - **October 10**: MCQ Test 1 (17%) - **October 24**: Case Study Assessment (16%) - **November 28**: MCQ Test 2 (17%) - **January 14, 2026**: Final Project due (50%) - **January 21**: Feedback and grades released ::: --- *Schedule subject to minor adjustments. All changes will be announced via Blackboard and course website.*