An Intensive 5-day Training Course

Advanced Data Analysis Techniques

Modelling, Simulation, Optimisation and Predictive Analytics using Microsoft Excel

INTRODUCTION TO THE COURSE

Data-driven insights are at the core of informed business decision-making. Statistical analysis of numerical data continues to be a fundamental tool in areas such as finance, manufacturing, service delivery, and quality control. Yet, the rise of the Internet of Things (IoT), the explosion of Big Data, and the need for sophisticated modeling and forecasting have outgrown the capabilities of traditional analytical methods.

In today’s high-performing business environments, companies increasingly face complex simulation and modeling challenges—from optimizing production and improving efficiency to managing operational costs, assessing risk, preventing fraud, and predicting outcomes. These challenges demand advanced analytical solutions that go beyond standard practices.

The Advanced Data Analysis Techniques training course is a fully computer-based learning experience that demonstrates how to solve complex, real-world business problems using Microsoft Excel. Participants will work on a diverse set of scenarios drawn from fields such as robotics, supply chain management, process optimization, financial risk management, and healthcare efficiency. Each case study is designed to highlight specific learning outcomes and practical applications.

Through this hands-on training course, delegates will learn how to build, code, and simulate realistic business problems using Excel, gaining the ability to optimize processes, evaluate risk, and make accurate predictions. This training course is intended for professionals already familiar with basic data analysis who are ready to specialize in advanced modeling and simulation techniques.

Key topics covered in this KC Academy Advanced Data Analysis Techniques training course include:

  • Developing simulation models to understand and predict system performance
  • Solving advanced problems in logistics, operations, finance, and engineering
  • Applying predictive tools to manage risk and support strategic decisions
  • Mastering practical Excel-based solutions for realistic business challenges

COURSE DETAILS

Objectives Icon

OBJECTIVES

This training course is designed to empower professionals managing complex business operations with the practical skills and conceptual understanding needed to transform data into actionable insights using cutting-edge analytical methods.

By the end of this Advanced Data Analysis Techniques training course, participants will:

  • Learn to solve complex business challenges using simulation, modeling, and predictive analytics
  • Gain hands-on experience with Microsoft Excel (2016/365) to apply a wide range of analytical methods
  • Understand and apply techniques such as Bayesian modeling, Monte Carlo simulation, Markov models, time series analysis, and linear programming
  • Explore eight real-world business problems and develop full modeling solutions using Excel
  • Shift from intuition-led to evidence-based decision-making for greater accuracy in forecasting and risk assessment
  • Recognize the strategic value of modeling and simulation in delivering high-quality, cost-effective products and services
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TRAINING METHODOLOGY

This training course on Advanced Data Analysis Techniques adopts a problem‐based learning approach, in which delegates are presented with a series of real problems drawn from the widest possible range of applications – they range from insurance to supply chain logistics, from chemistry to engineering, and from production optimization to financial risk assessment. Each problem presents and exemplifies the need for a different modelling or analytical approach.

This training course is entirely applications‐oriented, minimizing the time spent on the theory and mathematics of analysis and maximizing the time spent on the use of practical methods from within Excel, along with the understanding of how and why such methods work.

Delegates will spend almost all of their time exploring the use of modelling and simulation methods using Microsoft Excel, to develop solutions to the totally realistic problems that are presented.

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ORGANISATIONAL IMPACT

Organizations that enable their teams to apply advanced data analysis techniques will gain a significant competitive advantage. Benefits of attending this training course include:

  • A shift toward data-driven decision-making across departments
  • Accurate modeling and simulation of complex operational challenges
  • Stronger forecasting capabilities and future behavior prediction
  • Enhanced risk analysis and mitigation strategies
  • More effective use of Big Data for strategic planning and process improvement
  • Practical problem-solving using accessible, Excel-based tools
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PERSONAL IMPACT

Participants will leave this training course with a strong foundation in the application of advanced data analytics and modeling, with immediate relevance to their professional roles. They will gain:

  • Practical experience with optimization, simulation, and predictive analytics in Excel
  • Proficiency in linear programming, Newtonian and genetic optimization methods
  • The ability to apply scenario analysis, Markov modeling, and Monte Carlo simulations
  • Skills to match the right analytical method to the problem at hand
  • The confidence to make data-informed decisions and avoid misinterpretation of results
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WHO SHOULD ATTEND?

This KC Academy Advanced Data Analysis Techniques training course has been designed for professionals whose jobs involve the manipulation, representation, interpretation and/or analysis of data. This training course involves extensive modelling and analysis using Excel 2010 (or higher) and therefore delegates must not only be numerate, but must enjoy detailed working with numerical data to solve complex problems.

Full familiarity with Microsoft Excel (version 2007 or higher), and the ability to analyze data using common statistical methods, are fundamental prerequisites for attendance on this  Advanced Data Analysis Techniques training course.

 

DAILY AGENDA

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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Certificate

On successful completion of this training course, KC ACademy Certificate will be awarded to the delegates.

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