course | Advanced Data Analytics

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 optimizing the products from a refinery, and minimizing 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 optimizers;
• 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 ‘traveling salesman problem’ by optimizing 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; 
• Modeling the variables; 
• Analyzing 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 products, predicting market growth, and assessing risk in currency exchange rates.