ADVANCED DATA ANALYSIS TECHNIQUES

Objective:

• Understand advanced analytical methods for transforming complex data into meaningful business insights
• Apply modelling, simulation, and predictive analytical techniques to support decision-making
• Develop practical skills in optimization, scenario analysis, and risk modelling
• Use analytical tools to evaluate business processes and improve operational performance
• Apply mathematical and statistical techniques to solve complex business and operational challenges
• Build confidence in using Excel-based analytical models for forecasting and problem-solving

Content:

Linear Programming and Optimization

• Introduction to optimization and multivariable optimization problems
• Defining objective functions and constraints
• Understanding sign restrictions and feasibility regions
• Graphical representation of optimization problems
• Implementing linear programming models using Excel Solver
• Applying linear programming to production, supply chain, and logistics optimization
• Optimizing manufacturing, distribution, and resource allocation decisions

Newtonian and Genetic Optimization Methods

• Understanding linear and non-linear optimization problems
• Exploring stochastic search strategies
• Introduction to genetic algorithms and biological foundations
• Understanding limitations of Newton-type optimization methods
• Applying genetic algorithms through encoding, selection, recombination, and mutation
• Implementing optimization techniques using Excel Solver
• Solving complex optimization challenges including the travelling salesman problem

Scenario Analysis and Business Forecasting

• Understanding scenario analysis and business uncertainty
• Applying What-If analysis techniques in Excel
• Using one-variable and two-variable data tables
• Applying Excel Scenario Manager for decision-making
• Using scenario analysis to forecast expenses, revenues, and future business outcomes

Markov Models and Decision Analysis

• Understanding risk modelling and Markov processes
• Applying the five steps for developing Markov models
• Manipulating arrays and matrices within Excel
• Constructing, analysing, and interpreting Markov models
• Applying rollback analysis and sensitivity analysis
• Understanding Monte Carlo techniques within Markov models
• Using decision trees and Markov models for complex decision-making
• Modelling real-world applications including insurance and healthcare systems

Monte Carlo Simulation and Predictive Modelling

• Understanding the principles of Monte Carlo simulation
• Building simulation models using Excel
• Using the RAND() function to generate simulation variables
• Designing and analysing worksheet-based simulations
• Determining appropriate simulation iterations
• Modelling uncertainty and analysing complex business problems
• Applying statistical functions and percentile analysis
• Using Monte Carlo simulation for traffic modelling, sales forecasting, market growth prediction, and currency risk assessment

For Whom:

• Data Analysts and Business Analysts
• Operations and Process Improvement Professionals
• Supply Chain and Logistics Professionals
• Finance and Risk Management Professionals
• Managers involved in data-driven decision-making
• Professionals responsible for data manipulation, modelling, forecasting, and analytical problem-solving