Introduction

Modern organisations generate and manage increasingly large volumes of data across operational, financial, and strategic functions. As business environments become more complex and data-driven, conventional analytical approaches are often insufficient for addressing sophisticated forecasting, optimisation, and decision-making challenges.
The Advanced Data Analysis Techniques training course provides professionals with practical expertise in advanced modelling, simulation, and predictive analytics methods using Microsoft Excel. Participants explore powerful analytical techniques that support operational efficiency, risk assessment, production optimisation, forecasting, and strategic planning across a wide range of industries.
This highly practical training course focuses on solving realistic business problems through the application of optimisation models, scenario analysis, Monte Carlo simulation, Markov modelling, and predictive techniques. Participants gain hands-on experience in developing data-driven solutions that improve organisational performance and support more informed decision-making.

Key focus areas of this Advanced Data Analysis Techniques training course include:

Key Learning Outcomes

At the end of this Advanced Data Analysis Techniques training course, participants will be able to:

 

Training Methodology

This training course adopts a highly practical, problem-based learning methodology centred on real-world business applications. Participants engage extensively with hands-on modelling, simulation, and optimisation exercises using Microsoft Excel. The training course minimises theoretical complexity while emphasising practical implementation, business interpretation, and analytical problem-solving through realistic case studies and interactive workshops.

Advanced Data Analysis Techniques

Who Should Attend?

This Advanced Data Analysis Techniques training course is designed for professionals involved in data analysis, modelling, forecasting, and decision-making activities, including:

  • Data Analysts
  • Financial Analysts
  • Risk Management Professionals
  • Operations and Supply Chain Specialists
  • Engineers and Technical Professionals
  • Business Intelligence Professionals
  • Project and Planning Managers
  • Professionals working extensively with Microsoft Excel and statistical analysis

Course Outline

Day 1

Linear Programming

  • Introduction to Optimisation, Multi‐variate Optimisation Problems, Determining the Objective Function, Constraints to Problems, Sign Restrictions, The ‘feasibility region’, Graphical Representation, Implementation using Solver in Excel
  • Using Linear Programming to Solve Production and Supply Chain / Logistics Problems, such as optimising the products from a refinery, and minimising the manufacturing and delivery costs for a complex supply chain (with and without batch manufacturing, and with and without warehousing)
Day 2

Newtonian and Genetic Optimisation Methods

  • Linear and Non‐linear Optimisation Problems, Stochastic Search Strategies, Introduction to Genetic Algorithms, Biological Origins, Shortcomings of Newton‐type optimisers, How to Apply Genetic Algorithms, Encoding, Selection, Recombination, Mutation, How to Parallelise, Implementation using Solver in Excel
  • How to Solve a range of Optimisation Problems, Culminating in the classic ‘travelling salesman problem’ by optimising the motion trajectory of a large manufacturing robot, both with and without forced constraints
Day 3

Scenario Analysis

  • Introduction to Scenario Analysis, A What‐If example in Excel, Types of What‐If analysis, Performing manual what‐if analysis in Excel, One Variable Data Tables, Two‐variable data tables
  • Using Scenario Manager in Excel, Using scenario analysis to predict business expenses and revenues for an uncertain future
Day 4

Markov Models

  • Understanding Risk, Introduction to Markov Models, 5 Steps for Developing Markov Models, Manipulating Arrays and Matrices inside Excel, Constructing the Markov Model, Analysing the Model, Roll Back and Sensitivity Analysis, First‐order Monte Carlo, Second‐order Monte Carlo
  • Decision Trees and Markov Models, Simplifying Tree Structures, Explicitly Accounting for Timing of Events
  • Using Markov Chains to simulate an insurance no claims discount scheme, and Modelling the Outcomes of a Healthcare System
Day 5

Monte Carlo Simulation

  • Introduction to Monte Carlo Simulation, Monte Carlo building blocks in Excel, Using the RAND() function, Learning to model the problem, Building worksheet‐based simulations, Simple problems, How many iterations are enough?, Defining complex problems, Modelling the variables, Analysing the data, Freezing the model, Manual recalculation, "Paste Values" function, Basic statistical functions, PERCENTILE() function
  • Monte Carlo Simulation solutions to problems of traffic flow in a city, dealing with uncertainty in the sale of product, predicting market growth and assessing risk in currency exchange rates

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