The Advanced Data Analysis Techniques 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 modeling and simulation problems, addressing matters like trying to optimize production systems, to maximize performance efficiency, to minimize operating costs, to combat risk, to detect fraud and to predict future behavior and outcomes.
Manipulate complex data sets to gain deeper insights and make better decisions!
This advanced analytics seminar will introduce you to predictive analytics techniques, so you can frame strategic and operational questions involving marketing, finance, and operations or other real-world business applications.
In this hands-on course, you will cover a variety of analytics tools, such as histograms, ANOVA analysis, A/B testing, Pareto analysis, clustering, box plots, scatter diagrams, partitioning, unstructured text analysis, and multivariate regression analysis. Best of all, no background in statistics or programming is required. As long as you have a basic understanding of spreadsheets, you will learn how to manipulate complex data sets so you can gain insights that are not possible with common business intelligence techniques.
This training course provides you with the Advanced Data Analysis skills to enable you to improve the planning, monitoring, and performance of your organization. This will strengthen your ability within the workplace, allowing you to understand, produce and evaluate data to support you in your role for the benefit of yourself and your organization.
Delegates will work through practical case studies.
This training seminar is intended for delegates who have already attended the Data Analysis Techniques training seminar (this is a necessary prerequisite for this training) and hence who already have a solid understanding of conventional data analysis methods.
The training seminar shows by example how to build on the method learned in the Data Analysis Techniques training seminar and to create a variety of powerful modeling, simulation, and predictive analytical methods.
The methods introduced include Bayesian models, Newtonian and genetic optimization methods, Monte Carlo simulation, Markov models, advanced What If analysis, Time Series models, Linear Programming, and more.
The training seminar uses advanced features of Microsoft Excel throughout, and it is important that all delegates are fully competent in both Excel and the statistical analysis of data.
Upon completing the course, participants will be awarded Microsoft Certificate in addition to the certificate they receive from Strategic Axis.
Data and Methods of Collecting Data
Presentation of Data using Excel
Statistical Analysis of Data – Mean; Median and Mode; Variance and Standard Deviation
Probability – Binomial; Normal and Poisson Distribution
The Limitations of Data and Data Analysis
Confidence Intervals and Hypothesis Testing
Pivot Tables – developing and using Pivot Tables to aid Decision Making
Cross Tabulation and Chi-squared
Data Analysis Tools in Excel – a detailed understanding
ANOVA; t-tests and Z scores
Moving Average and Exponential Smoothing
Advanced and Applied Solver and Goal Seek
Co-variance and Correlation
Multiple Regression using Excel
Statistically Testing Results
Making Decisions – Project Planning, Estimating Costs; Reducing Risk
Forecasting Costs and Production levels
Estimating Project Duration using Statistical estimates and P.E.R.T
Network and Critical Path Analysis
Economic Quantity Order Analysis – to optimize inventory levels
Linear Programming and Optimisation Techniques to maximize resource allocation, e.g., inventory and capital
Earned Value Analysis – to identify Project Cost and Schedule Variances
Analysing and Forecasting Volatility in the Market, e.g., Oil Prices
Evaluating Suppliers, Competitors and Acquisition Targets using Data Analysis
Developing Financial Models
Cost, Volume, Profit Analysis
Risk Management using Data Analysis
Assessing the impact of decisions on Return on Investment and Return on Equity
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)
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 ‘travelling salesman problem’ by optimizing the motion trajectory of a large manufacturing robot, both with and without forced constraints
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
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
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 issues; How many iterations are enough?; Defining complex issues; Modelling the variables; Analysing the data; Freezing the model; Manual recalculation; “Paste Values” function; Basic statistical purposes; 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