The Internet of Things, the consequential growth of Big Data, and the ever- increasing requirements to model and predict, mean that many of the analytical opportunities and needs of a modern, high performing company cannot be met using conventional statistical methods alone. More and more companies are wrestling with complex modelling and simulation problems, addressing matters like trying to optimise production systems, to maximise performance efficiency, to minimize operating costs, to combat risk, to detect fraud and to predict future behaviour and outcomes.
This Oxford Management Centre training course explores how to perform complex numerical problem solving using Microsoft Excel 2016 (or 365). The course shows by example how to build on the methods learned in the Data Analysis Techniques training course to create variety of powerful modelling, simulation and predictive analytical methods. The methods introduced include Bayesian models, Newtonian and genetic optimisation methods, Monte Carlo simulation, Markov models, advanced What If analysis, Time Series models, Linear Programming, and more.
The course adopts a problem-based learning approach, in which delegates are presented with 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 product optimisation to financial risk assessment. Each problem presents and exemplifies the need for a different modelling or analytical approach. 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.