AI, Technology & Digital · 5 Days · Public · Al-Khobraa certified · From USD 3,900
Overview
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.
Learning 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
To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field.
To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and data representation methods, which have direct relevance to a wide range of analytical problems.
To give delegates the ability to recognize which types of analysis are best suited to particular types of problems.
To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions.
To provide delegates with an overview of the main data analysis applications within engineering systems.
Who Should Attend
This course is designed for professionals working in technology and digital transformation, as well as those whose responsibilities require a stronger practical grasp of the subject and its application within their organisation.
Professionals in Data Analytics & BI
IT, data and digital professionals
Analysts and technical specialists
Course Outline
Day 1 — Linear Programming
Introduction to optimization; Multi‐variate optimization 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)
Day 2 — Newtonian and Genetic Optimization Methods
Linear and non‐linear optimization 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 parallelize. Implementation using Solver in Excel
How to solve a range of optimization problems, culminating in the classic ‘travelling salesman problem’ by optimizing the motion trajectory of a large manufacturing robot, both with and without forced constraints
Day 3 — 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
Day 4 — 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
Day 5 — 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; 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 product, predicting market growth and assessing risk in currency exchange rates
Accreditation
Al-Khobraa certified. On completion delegates receive an Al-Khobraa certificate of achievement.
What do Al-Khobraa AI, technology and digital courses cover?
The portfolio covers cyber security, data analytics and data science, artificial intelligence (AI) and machine learning, Power BI and Excel for analysis, digital transformation and IT governance. Courses are applied — delegates work with real datasets and tools rather than slideware.
Who should attend an AI, technology or digital course?
These courses suit IT and cyber security staff, data and business analysts, engineers and operations professionals who work with plant or business data, digital transformation teams, and managers who commission or govern technology projects. Foundation-level courses assume no programming background.
How long are AI, technology and digital courses, and where are they held?
Most Al-Khobraa AI, technology and digital courses run over 5 days. The exact duration, dates and city for this course are listed in the dates section on this page. Public sessions run across Saudi Arabia — Riyadh, Jeddah, Al-Khobar, Jubail and Yanbu — as well as regional and international venues, with live virtual classrooms available on request.
What certificate will I receive?
Delegates who complete the course receive an Al-Khobraa certificate of completion, issued by a Saudi-registered training provider. Al-Khobraa also delivers internationally accredited programmes; where a course carries an external awarding body such as PMI, CIPS, ILM or IOSH, that body is named on the course's own page.
Can AI, technology and digital training be delivered in-house for our team?
Yes. Any Al-Khobraa AI, technology and digital course can be delivered in-house at your own premises anywhere in Saudi Arabia or the GCC, with the content, dates and language of delivery tailored to your team. Al-Khobraa is an approved HRDF (Hadaf) provider, so eligible Saudi employers can seek training support. Contact us for a quotation.