Engineering Data Analytics

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Engineering Data Analytics Training

ABOUT THE COURSE

This is a hands-on course to train engineers and managers about the methods and tools of data analytics. The course will cover a broad range of practical methods for analysis and modelling from data.

The great advances of information acquisition and storage technology has resulted in a new age of data deluge, which offers some unprecedented challenges and opportunities. However, this vast amount of data, though overwhelming to deal with, offers an excellent opportunity to acquire useful intelligence on the process, from which the data is recorded A professional who can take advantage of vast amounts of data would be able to deliver great value to his/her organisation through optimisation of processes, through enhanced situational awareness, and through discovery of new insights. In this course we introduce the scientific basis of analysing large datasets for fun and profit.

There would be a hands-on tutorial sessions on R, a statistical modelling software. Some exposure to programming would be helpful but it is not a pre-requisite. The course will cover a wide range of topics like information representation, statistical analysis, machine learning, and data mining. The course is intended for practising engineers, researchers, consultants, and managers with a numerate educational background.

WHO SHOULD ATTEND

Engineers, scientists, and managers can benefit from this course. Personnel from assets and transportation management companies, risk analysts, oil and gas companies, classification societies and engineering firms can all benefit from this broadly applicable course.

PROGRAMME

Day 1

08.30 – 09.00 Delegate Registration

09.00 – 10.30 Lecture 1: Data Representation and Manipulation. This lecture covers how data is represented in software and how to access and manipulate them at a desired level of detail – J. Majumder

10.30 – 10.45 Break

10.45 – 12.15 Lecture 2: Deterministic data modelling. This lecture covers architectures of models that can capture the relationships between data. Kernel methods, splines, neural networks, RBF networks etc. will be covered – J. Majumder

12.15 -13.30 Lunch

13.30 – 15.00 Lecture 3: Statistical analysis. This lecture covers the statistical methods for analysing data and models made from the data. This will include methods based on Central Limit Theorem, Null Hypothesis tests, T tests, correlation and covariance, etc.- N. Banerjee

15.00 – 15.30 Break

15.30 – 17.00 Lecture 4: Probabilistic models. This lecture covers probabilistic model architectures and describes the contexts of applicability. Practical Bayesian modelling will be discussed. – J. Majumder

Day 2

09.00 – 10.30 Lecture 5: Data Mining. This lecture covers methods of discovering patterns in data. Data methods of finding data regression, grouping methods like clustering and classification, Decision Tree predictive modelling (Using Microsoft Excel and Precision Tree Excel plug in) – N Bnerjee

10.30 – 10.45 Break

10.45 – 12.15 Lecture 6: Simulation, Calibration, and Emulation. This lecture covers methods of tuning simulation models using observed data, and capturing the behaviour of simulation in data-driven models – J. Majumder

12.15 -13.30 Lunch

13.30 – 15.00 Lecture 7: Optimisation and Decision Making. Introduction to optimization using Linear Programming models using Microsoft Excel and Mosel student version – N. Banerjee

15.00 – 15.30 Break

15.30 – 17.00 Lecture 8: Case studies. This lecture presents two case studies of data analytics. One study will be engineering oriented and the other would be business oriented – . Majumder & N. Banerjee

CVs of Lecturers

Jayanta Majumder is a research associate in the University of Strathclyde, Glasgow. He graduated with B.Tech (Hons) in Mechanical Engineering from the Indian Institute of Technology, Kharagpur, in 1999. He has worked for several years in the computer aided design (CAD) and simulation software industry (for Autodesk Ltd., Cambridge, and for Spiral Software, a division of Schneider Electric) as a developer of mathematical modelling software. In his multiple stints as university researcher he has provided engineering consultancy services to the following companies: National Semiconductors (now Texas Instruments) in the USA, Safety-at-Sea (now Brookes-Bells) in the UK, Navantia, and SENER, both in Spain. His research interests revolve around mathematical and computational modelling of real world objects and processes.

Nilabhra Banerjee has over 15 years of experience in business development. He has worked for signature companies in different business domains, namely finance, satellite data processing, core healthcare support, transportation and logistics. He also has an excellent academic career supported by dual masters, one in Information Technology and the other in Operational Research. Mr Banerjee is currently pursuing his PhD from the Management Science Department in the University of Strathclyde

Duration: 2 Days

Cost: £550

 

Engineering Data Analytics Training Aberdeen, Edinburgh, Dunfermline, Inverness  and other sites throughout the UK including onsite closed company courses are available.

Engineering Data Analytics Training Qatar, Abu Dhabi, Dubai, India, Ghana and Nigeria is also available.

 

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