Prerequisites

• Basic mathematics and statistics knowledge
• Understanding of financial principles
• Basic computer literacy
• Spreadsheet software experience
• Optional: Basic programming skills
• Computer with minimum 8GB RAM

12-Week Financial Analytics Course Syllabus

Week 1: Foundations of Financial Analytics

• Introduction to financial analytics
• Corporate finance fundamentals
• Data types in financial analysis
• Basic financial modeling concepts
• Analytics ecosystem overview
• Financial decision-making frameworks
• Technology integration in finance

Week 2: Programming for Financial Analytics

• Python programming basics
• Jupyter Notebook and IDE setup
• Data manipulation techniques
• NumPy and Pandas libraries
• Financial data structures
• Basic statistical functions
• Data importing and cleaning

Week 3: Statistical Analysis in Finance

• Descriptive and inferential statistics
• Financial probability theory
• Regression analysis techniques
• Hypothesis testing
• Cash flow analysis
• Financial break-even modeling
• Statistical modeling principles

Week 4: Financial Market Analysis

• Risk and return estimation
• Stock and bond investment analysis
• Time series analysis
• ARMA and GARCH models
• Value at Risk (VaR) calculation
• Market data cleansing
• Investment performance metrics

Week 5: Portfolio Management

• Capital Asset Pricing Model (CAPM)
• Portfolio optimization techniques
• Sharpe ratio analysis
• Markowitz mean-variance model
• Portfolio clustering
• Option pricing models
• Investment strategy development

Week 6: Technical Analysis

• Financial chart analysis
• Technical indicators (RSI, MACD)
• Moving average techniques
• Candlestick chart interpretation
• Machine learning in price prediction
• Trading strategy simulation
• Algorithmic trading fundamentals

Week 7: Credit Risk Analytics

• Credit risk modeling
• Decision tree techniques
• Logistic regression
• Credit scoring methods
• Bankruptcy prediction models
• Risk evaluation strategies
• Financial default analysis

Week 8: Advanced Machine Learning

• Predictive financial modeling
• Machine learning algorithms
• Neural network applications
• Support Vector Machines
• Financial forecasting techniques
• Model performance evaluation
• Practical implementation workshops

Week 9: Financial Modeling

• Corporate financial modeling
• Valuation techniques
• Financial statement analysis
• Sensitivity analysis
• Capital budgeting methods
• NPV and IRR calculations
• Investment decision frameworks

Week 10: Quantitative Finance

• Advanced financial mathematics
• Derivative pricing
• Financial econometrics
• Stochastic modeling
• Risk management techniques
• Quantitative trading strategies
• Financial optimization methods

Week 11: Financial Technology

• Fintech innovations
• Blockchain in finance
• Algorithmic trading
• Automated investment platforms
• Regulatory technology
• Ethical considerations
• Future of financial analytics

Week 12: Capstone Project

• End-to-end financial analytics project
• Portfolio development
• Industry best practices
• Implementation strategies
• Professional networking
• Career guidance
• Final project presentations

Learning Outcomes

• Advanced financial analytics expertise
• Predictive modeling capabilities
• Professional tool proficiency
• Strategic financial decision-making skills
• Ethical data management understanding
• Practical implementation knowledge
• Industry-ready financial analytics skills

Certification

Students receive a comprehensive Financial Analytics certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging industry trends and technological advancements.