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Advanced Data Analysis Techniques

Advanced Data Analysis Techniques

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.

YOU WILL LEARN HOW TO

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.

IMPORTANT COURSE INFORMATION

Upon completing the course, participants will be awarded Microsoft Certificate in addition to the certificate they receive from Strategic Axis.

COURSE OUTLINE

Modules

Module 1: Data Collection, Presentation and Analysis

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

Module 2: Pivot Tables and Data Analysis Tools in Excel

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

Module 3: Advanced Correlation and Regression

Co-variance and Correlation

Linear Regression

Non-linear regression

Multiple Regression using Excel

Statistically Testing Results

Making Decisions – Project Planning, Estimating Costs; Reducing Risk

Module 4: Project Management and Applied Data Analysis

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

Module 5: Financial Management and Applied Data Analysis

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

Module 6: 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)

Module 7: Newtonian and Genetic Optimization 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 ‘travelling salesman problem’ by optimizing the motion trajectory of a large manufacturing robot, both with and without forced constraints

Module 8: 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

Module 9: 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

Module 10: 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 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

 

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