Day 1:
Foundations of Financial Data Analysis
Overview of financial datasets and key metrics for analysis.
Introduction to mathematical and statistical principles in finance.
Exploring data visualization techniques for financial reporting.
Hands-on session: Cleaning and preparing financial data for analysis.
Day 2:
Statistical Analysis in Finance
Understanding descriptive and inferential statistics.
Hypothesis testing and its applications in financial decision-making.
Variance analysis and its role in financial performance evaluation.
Case study: Analyzing company profitability using statistical tools.
Day 3:
Regression and Correlation Techniques
Fundamentals of regression analysis and correlation in finance.
Application of simple and multiple regression models.
Identifying relationships between financial variables using correlation.
Practical exercise: Predicting revenue growth through regression modeling.
Day 4:
Time Series Analysis and Forecasting
Introduction to time series data and key forecasting models.
Decomposition of time series into trend, seasonal, and irregular components.
Applying ARIMA and exponential smoothing techniques.
Workshop: Forecasting stock market trends using time series models.
Day 5:
Advanced Financial Data Tools and Applications
Overview of financial analysis tools: Python, R, and Excel.
Automating data analysis using scripts and templates.
Real-world application: Integrating statistical models with financial software.
Final project: End-to-end financial data analysis and presentation of findings.
