Financial Data Analytics on Top of Data Science

Day 1: Fundamentals and Core Techniques

• 08:00 – 08:30 | Introduction and Overview
  • Welcome and objectives of the training program.
• 08:30 – 10:15 | Fundamentals of Financial Data Analysis
  • Introduction to financial datasets and key metrics.
  • Understanding the unique characteristics of financial data.
• 10:15 – 10:30 | Morning Break
• 10:30 – 12:00 | Exploratory Data Analysis (EDA) for Finance
  • Techniques for summarizing and visualizing financial data.
  • Identifying trends, patterns, and anomalies in financial datasets.
• 12:00 – 13:00 | Lunch Break
• 13:00 – 14:30 | Time Series Analysis and Forecasting
  • Introduction to time series data in finance.
  • Forecasting techniques including ARIMA, Exponential Smoothing, and others.
• 14:30 – 14:45 | Afternoon Break
• 14:45 – 16:00 | Hands-On Practice
  • Applying EDA and time series analysis to financial data.
  • Practical exercises and case studies.

Day 2: Advanced Financial Analytics and Compliance

• 08:00 – 08:30 | Recap of Day 1
  • Reviewing key concepts and addressing questions.
• 08:30 – 10:15 | Portfolio Optimization and Risk Management
  • Modern portfolio theory and optimization techniques.
  • Risk management strategies and tools for mitigating financial risks.
• 10:15 – 10:30 | Morning Break
• 10:30 – 12:00 | Machine Learning for Financial Applications
  • Implementing supervised and unsupervised learning models.
  • Applications of machine learning in fraud detection, credit scoring, and more.
• 12:00 – 13:00 | Lunch Break
• 13:00 – 14:30 | Risk Modeling
  • Developing and validating models to quantify and manage financial risk.
  • Practical exercises on building risk models.
• 14:30 – 14:45 | Afternoon Break
• 14:45 – 16:00 | Regulatory Compliance and Ethical Considerations
  • Understanding the regulatory landscape in financial data analysis.
  • Ethical considerations and best practices in financial data science.