Advanced Data Analytics

Course Code: DIGTR 1033

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Course Sector:

Digital Transformation and Innovation

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Introduction

Training course introducion / brief

With the advent of the emergence of the IoT, the consequential growth in Big Data, and the ever-increasing requirements to model and predict, many of the analytical opportunities and needs of a business now cannot be resolved only by using conventional statistical methods alone.

Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. This training course is designed to provide participants with advanced concepts of data analytics and techniques to create a variety of powerful modeling, simulation, and predictive analytical methods. This includes the Bayesian models, Newtonian and genetic optimization methods, Monte Carlo simulation, Markov models, advanced What If analysis, Time Series models, Linear Programming, and more.

Gain a comprehensive understanding of how to unravel a wide range of business problems that require modeling, simulation, and predictive analytical approaches….
Familiarize with and understand common modeling, simulation, and predictive analytical techniques, including Bayesian models, conventional and genetic optimization methods, Monte Carlo models, Markov models, What If analysis, Time Series models, Linear Programming, and more…
Select which modeling, simulation, and predictive analysis methods are best suited to which types of problems….
Apply modeling, simulation and predictive analytical methods using Microsoft Excel 2010 (or higher) and in particular the Solver tool

All Data analysis professionals
Business and technology leaders
Business Unit Managers
Business Development Consultants
General Managers / Regional Managers
Senior and mid-level leaders
Individual leaders of all levels in the organization
Marketing and sales Consultants

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;
Analyzing 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.

Boost certificate of completion

BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.

ENDORSED EDUCATION PROVIDER

Flexible deadlines

Customized dates accordance to your schedule

Shareable Certificate

Earn certificate upon completion

COURSE METHODOLOGY

Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).

At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.

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TRAINING COURSE: Advanced Data Analytics