A basis and understanding of methods pertaining to Informatics is needed to effectively obtain information from data.

The objective of this course is to supply the student with a toolbox of techniques for data mining and modelling (informatics) and develop in the student the strategic ability to effectively apply this toolbox.

At a glance

  • Dates
    • Please enquire for course dates
  • DurationFive days
  • LocationCranfield campus
  • Cost£1400 Concessions available

What you will learn

On successful completion of this module the student will be able to:

  • Assess the potential and potential pitfalls of ‘big data’,
  • Assemble and organize data for prescribed analysis and modelling approaches,
  • Appraise and apply data mining techniques, identify underlying data structures,
  • Construct models that reproduce observed relationships; the application of inference engines,
  • Create integrative designs of process models with data; applying model data fusion,
  • Recognise uncertainty and error in data and model parameter estimations,
  • Develop diagnostics measures of model performance.

Core content

  • Introduction to computational methods in informatics; how do we turn data into information,
  • Strategies and approaches to manage large data for computational analysis,
  • Data exploration and data mining. Strategies to elucidate underlying structures in the data. Are these causal or coincidental? How does one interpret and communicate results from a data mining exercise effectively,
  • Inference modelling. Generating quantitative models which can be deployed on existing or new data to generate the required information. Identifying the appropriate model type, form and configuration. Developing the technical skill to configure and deploy inference engines,
  • Process, empirical or semi-empirical modelling. To use existing process based or (semi) empirical models to generate information from data. Identifying and understanding the constraints of each type of model. Developing the technical skill to configure and deploy these models. Introduction to model data fusion methods and their applications,
  • Understanding how models operate in space and over time and how spatial and/or temporal effects can affect model behaviour,
  • Error and Diagnostics. How to assess model performance. Validation procedures and measures. Sources of uncertainty (data/model/deployment),
  • Using model performance measures as diagnostic tools for optimal model configuration and on-going quality control,
  • Effective communication of the computational Informatics processes and outcomes. Statement of quality and remit of the modelling development process. Identifying and communicating what a particular model can and cannot do.

Upgrade to a professional qualification

Cranfield credits are available for this short course which you can put towards selected Cranfield degrees. Find out more about short course credit points.


20% discount for Cranfield alumni, 10% discount for colleagues of alumni
£1340 - Professional/trade association discount
£1280 - Multiple bookings*
* Minimum of 5 delegates.

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 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 
MK43 0AL

Read our Professional development (CPD) booking conditions.