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About the course
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 seminar explores how to perform complex numerical problem solving using Microsoft Excel 2016 (or 365). The seminar shows by example how to build on the methods learned in the Data Analysis Techniques training seminar 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 seminar 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.
- To teach delegates how to solve a wide range of business problems which require modelling, simulation and predictive analytical approaches
- To show delegates how to implement a wide range of the more common modelling, simulation and predictive analytical methods using Microsoft Excel and the Solver tool
- To provide delegates with both a conceptual understanding and practical experience of a range of the more common modelling, simulation and predictive analytical techniques
- To give delegates the ability to recognize which modelling, simulation and predictive analysis methods are best suited to which types of problems
- To give delegates sufficient background and situation experience to be able to judge when an applied technique will likely lead to incorrect conclusions
- To provide a clear understanding of why the best companies in the world see modelling, simulation and predictive analysis as being essential to delivering the right quality products and optimised services at the lowest possible costs
- Forecasting and Future Behavior Prediction
- Advanced Modelling and Simulation of Business Processes
- Risk Assessment and Risk - Informed Decision Making
- Conventional and Artificial Intelligence Optimisation Techniques
- Linear Programming
- Advanced Scenario Analysis
- Monte Carlo Simulation
- Big Data
- K- Means Clustering
- Principal Component Analysis
Who should attend
This seminar is designed for professionals whose jobs involve the manipulation, representation, interpretation and / or analysis of data. It involves extensive modelling and analysis, and must enjoy detailed working with numerical data to solve complex problems.