Trainign Advanced Data Analysis Techniques
INTRODUCTION
The Statistical Analysis of Numerical Information is proven to be a powerful tool, providing everyday insight into matters like corporate finance, production processes and quality control. However, the advent of the Internet of Things, the consequential growth in 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 minimise operating costs, to combat risk, to detect fraud and to predict future behaviour and outcomes.
PROGRAM OBJECTIVES
- 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 2010 (or higher) and in particular 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, including Bayesian models, conventional and genetic optimisation methods, Monte Carlo models, Markov models, What If analysis, Time Series models, Linear Programming, and more
- 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 analytics as being essential to delivering the right quality products and optimised services at the lowest possible costs
WHO SHOULD ATTEND?
- Professionals whose jobs involve the manipulation, representation, interpretation and/or analysis of data. This training seminar 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.
PROGRAM OUTLINE
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)
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
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
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
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
Permintaan Brosur penawaran Training
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