This short course provides delegates with a working appreciation of advanced data analytics, combined with conventional analysis methods to make the best use of available analytical tools when seeking insight from engineering data.

Many engineering organisations have created various initiatives to embrace the latest digital capabilities and equip their workforce with related tools and techniques. A key growth area of interest is the application of modern data-analytic techniques to aid understanding of how products behave in service and where potential vulnerabilities may exist in product usage with the aim of improving design and functionality. Delegates will therefore gain a working practical appreciation of techniques related to data-science and AI that will help unlock valued insight from business and engineering data.


At a glance

  • Dates
    • Please enquire for course dates
  • DurationThree days
  • LocationCranfield campus
  • Cost

    £1,200.


    Concessions available

Course structure

This course is delivered over three days as a series of lectures and practical discussion sessions. The course will also cover ways in which delegates can access modern analytical techniques within their organisation via open-source software and/or commercially available packages.

What you will learn

On completion of the course, you will be able to:

  • Understand fundamental concepts and methodologies of machine learning.
  • Visualise multi-parameter complex engineering data in an understandable form.
  • Understand and develop analysis work-flows to gain valued insight from data.
  • Make better decisions from your engineering data using analytical methods.
 

Core content

  • Introduction to data analytics
  • Understanding data
  • Descriptive analytics
  • Data analytics workflow
  • Data visualisation
  • Overview of data analytics software
  • Manipulation and querying data using Python
  • Appreciation of signal processing
  • Linear algebra 101
  • Probability and statistics 101
  • Unsupervised learning
  • Supervised learning
 

Timetable

 

Who should attend

The course will benefit engineers from a range of industries and disciplines (e.g. manufacturing, design, operation support, etc.) who want to make better data-driven decisions. 

Concessions

20% discount for Cranfield alumni.

10% discount when registering three or more delegates from the same organisation at the same time.



Accommodation options and prices

This is a non-residential course. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information regarding our accommodation on campus can be found here.

Alternatively you may wish to make your own arrangements at a nearby hotel.

 

 

Location and travel

Cranfield University is situated in Bedfordshire, close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes away respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

Cranfield University
College Road
Cranfield
Bedford 
MK43 0AL

How to apply

To apply for this course please use the online application form.

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