Advanced Data Analysis Techniques

This course is designed for professionals who work with data in analytical, strategic, and decision-making roles, including:

• Business analysts, financial analysts, and data scientists.
• Risk managers and professionals involved in decision-making under uncertainty.
• Operations and supply chain analysts optimizing business processes.
• Engineers, researchers, and consultants handling complex data models.
• Professionals looking to enhance their statistical and predictive analytics skills.

Note: Participants should have prior experience working with Microsoft Excel (version 2007 or higher) and a solid understanding of basic statistical methods.

Day 1: Forecasting and Predictive Analytics

• Techniques for forecasting future trends and behaviors.
• Time series analysis and trend identification.
• Building predictive models to anticipate business outcomes.

Day 2: Business Process Modeling and Simulation

• Advanced techniques for business process simulation.
• Understanding process variability and performance optimization.
• Case studies in modeling real-world business processes.

Day 3: Risk Assessment and Decision-Making Under Uncertainty

• Identifying, measuring, and mitigating risks using data.
• Risk-informed decision-making frameworks.
• Scenario analysis and stress testing.

Day 4: Optimization and AI-Driven Analytical Techniques

• Conventional optimization techniques, including linear programming.
• Artificial intelligence applications in optimization.
• Implementing decision-making models for business efficiency.

Day 5: Big Data Analytics and Advanced Statistical Techniques

• Introduction to big data analytics in decision-making.
• K-Means clustering for data segmentation.
• Principal Component Analysis (PCA) for dimensionality reduction.
• Monte Carlo simulation for scenario analysis and risk modeling.