Advanced Data Analysis Techniques
Modelling, Simulation, Optimisation and Predictive Analytics using Microsoft Excel
Course Overview
However, the rapid expansion of the Internet of Things (IoT), the exponential growth of Big Data, and the increasing need for modelling and prediction mean that many analytical requirements of modern, high-performing organizations can no longer be satisfied using conventional data analysis methods alone. Contemporary organizations are increasingly confronted with complex modelling and simulation challenges, such as optimizing production systems, maximizing operational efficiency, minimizing costs, managing risk, detecting fraud, and predicting future behavior and outcomes.
This Advanced Data Analysis Techniques training course, delivered by HighPoint Training Center, is a fully computer-based program that demonstrates—through realistic, example-driven applications—how Microsoft Excel can be used to solve complex business problems. The case studies span a wide range of sectors, including robotics, refining, supply chain logistics, production optimization, financial risk management, and healthcare systems. Each problem is uniquely designed to achieve specific and well-defined learning objectives.
Participants will learn how to code, simulate, and analyze realistic business problems, and how to use these simulations to understand system behavior, optimize performance, and forecast future outcomes. The course is designed for professionals who already possess experience in conventional data analysis and who seek to develop specialist expertise in modelling, simulation, and predictive analytics for complex business environments.
Course Objectives
By the end of this training , participants will be able to:
- Solve a wide range of complex business problems using modelling, simulation, and predictive analytics.
- Implement advanced analytical techniques
- Apply and understand advanced methods such as Bayesian models, linear and genetic optimization, Monte Carlo simulation, Markov models, What-If analysis, time-series modelling, and linear programming.
- Develop complete, end-to-end solutions to multiple realistic business problems using Excel-based modelling tools.
- Transition from intuition-based to evidence-based decision-making in complex and uncertain environments.
- Enhance forecasting accuracy, risk assessment capabilities, and risk-informed decision-making.
- Understand why leading global organizations rely on modelling, simulation, and predictive analytics to deliver high-quality products and optimized services at minimal cost.
Course Audience
This training course is designed for professionals whose roles involve data manipulation, analysis, interpretation, and decision support. As the course involves extensive modelling and numerical analysis using Excel, participants should be comfortable working with quantitative data and advanced spreadsheet functionality.
Prerequisites:
- Full proficiency in Microsoft Excel (version 2007 or later).
- Prior experience with common statistical data analysis methods.
Course Methodology
This course adopts a problem-based learning approach, where participants work through a series of realistic case studies drawn from diverse industries, including insurance, logistics, engineering, chemistry, production, and finance. Each case highlights the need for a specific analytical or modelling technique.
The program is highly application-oriented, minimizing theoretical exposition and mathematical derivations while maximizing hands-on use of Excel-based tools. Participants spend the majority of the course time developing, testing, and refining models and simulations to solve real-world business problems.
Course Outline
Day 1 – Linear Programming
- Principles of optimization and multivariate optimization problems
- Objective functions, constraints, feasibility regions, and sign restrictions
- Graphical representation and implementation using Excel Solver
- Applications in production planning and supply chain cost optimization
Day 2 – Newtonian and Genetic Optimization Methods
- Linear vs. non-linear optimization
- Stochastic search strategies and genetic algorithms
- Encoding, selection, recombination, mutation, and parallelization
- Excel-based implementation and applications, including the Travelling Salesman Problem
Day 3 – Scenario and What-If Analysis
- Principles of scenario analysis and What-If modelling
- One-variable and two-variable data tables
- Scenario Manager applications for revenue and cost forecasting under uncertainty
Day 4 – Markov Models
- Risk concepts and Markov modelling fundamentals
- Model development steps and matrix manipulation in Excel
- Monte Carlo extensions and sensitivity analysis
- Applications in insurance systems and healthcare modelling
Day 5 – Monte Carlo Simulation
- Monte Carlo concepts and Excel implementation
- Random number generation and simulation design
- Iteration analysis and statistical interpretation
- Applications in traffic flow, sales uncertainty, market growth, and currency risk assessment
Certificates