Mastercourse session 5: The art of feature engineering

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Mastercourse ‘Data Innovation beyond the Hype’ - Session 5 the art of feature engineering

The‘Data Innovation beyond the Hype’ mastercourse given by the Data and AI Competence Lab of Sirris (EluciDATA Lab) consists of 6 sessions providing a pragmatic and industry-oriented approach to data-driven innovation. As of January 2019, we will start a new cycle.

The ‘Data Innovation beyond the Hype’ mastercourse provides pragmatic and industry-oriented sessions on data-driven innovation. Between January 2019 and June 2019, we will organise a new cycle, which you can follow in its entirety (by registering for all sessions - possible until 17/01/2019) or by independently following the session(s) you are interested in.

Session 5: The art of feature engineering

Any intelligent algorithm that is used to learn something from data requires that this data is presented in the most optimal way. The process of transforming the data and extracting the most relevant distinguishing characteristics out of it is called feature engineering. It is arguably the most important step in the data science workflow as even the most intelligent algorithm will not produce satisfactory results if the used data does not capture the most essential properties of the phenomenon under study. There is no clearly-defined formal process for engineering features and consequently this requires a lot of creativity, iterations, domain knowledge, etc. The goal of this session is to give an overview of the most commonly used approaches, as well as lessons learnt and common pitfalls for different types of data (sensor data, location data, etc.) and problem settings (prediction, profiling, etc.).

Target audience

This mastercourse session is open to any company that is interested in data innovation as an opportunity for its company and activities. For sessions 3 to 6, some basic analytic skills (e.g. high-level understanding of algebra and interpretation of statistical figures) is a prerequisite.

Practical information

Location Sirris, Technologiepark 935, Zone A2, 9052 Zwijnaarde


Session number and title 

Normal price

Early bird* and/or Sirris member**

Session 1: The art of formulating a data science task 

625 EUR 

575 EUR 

Session 2: Bring your own data innovation challenge

475 EUR 

425 EUR 

Session 3: The importance of data visualisation and hypothesis building

525 EUR 

475 EUR 

Session 4: The power of data visualisation

575 EUR 

525 EUR 

Session 5: The art of feature engineering 

625 EUR 

575 EUR 

Session 6: Choosing the right algorithm for the right task 

625 EUR 

575 EUR 

*The early bird price is only applicable if you choose to follow one session. Registration must be done at least more than 3 weeks in advance. 

**This price is applicable for any Sirris member who subscribes at any time to one single session. 

If you register for the whole cycle (possible until 17/01/2019) session 2: Bring your own data innovation challenge is for free. Total price is 2975 EUR instead of 3450 EUR.

If you register for any 3 sessions – the total price is 1575 EUR. 

All prices are exclusive of VAT.

A hardcopy of the course notes is included in the registration price. All sessions are given in English.

If you are a Flemish SME you can also make use of the kmo-portefeuille (the kmo-portefeuille should be requested at latest 14 days after the course has taken place). (Erkenningsnr. Sirris: DV.O105154).Please note that if you do not apply in time your request will be declined. 

For more information, please visit contact us (

Our general terms and conditions for the training

Any cancellation has to be made by e-mail ( Cancellations made before the 3 business days preceding a session are free of charge. After this deadline, 50% of the participation fee will be charged (incl. VAT). In case of cancellation the day itself, the full amount of the registration will be due. In case of "no-show" the full amount of the registration fee will be due too. Replacement by a colleague is always possible if notified in advance by e-mail to

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