Mastercourse ‘Data Innovation beyond the Hype’

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

The mastercourse ‘Data Innovation beyond the Hype’ 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) or by following the separate session(s) you are interested in.

Session 1: The art of formulating a data science task

• 31 January 2019, 13:00-17:30 - Zwijnaarde

The goal of this session is to offer an overview of data science and its opportunities for innovation from an industrial point of view. By means of actual cases from current data innovation projects in several domains, the iterative and creative path from business understanding to data exploitation will be described by means of the different steps in the data science workflow. Particular attention will be given to the kind of challenges that can be tackled, and the data and skills that you need to have available in order to realise those challenges. Furthermore, some common beliefs about data analytics as a commodity are debunked.

Session 2: Bring your own data innovation challenge

• 28 February 2019, 13:00-17:30 - Zwijnaarde

In this hands-on session, companies are invited to bring a well-described business challenge that they want to tackle via data innovation and – if applicable – a description of the data that they are currently gathering. The goal of the session is to support the participants in identifying the potential of solving this challenge in a data-driven way and specifying the data innovation opportunities for their particular business context. During the first part, participants will be requested to pitch their business challenge, followed by an interactive discussion on the data innovation potential. The second part is hands-on, during which each company will be coached to translate their business problem into a data science task and to outline possible solution strategies.

Session 3: The importance of data exploration and hypothesis building

• 21 March 2019, 13:00-17:30 - Zwijnaarde

Having a clear idea of the business opportunity and a library of intelligent algorithms at your disposal is not sufficient for realizing a data-driven solution. A detailed understanding of the available data in order to derive viable working hypotheses about the underlying mechanisms of the problem under study is just as important. Is the data suitable to solve the business challenge? Is it of the right quality and nature? What data is missing? Does it exhibit significant patterns and trends that can be exploited to model and understand better the problem under study? In this session, several data exploration approaches will be discussed, with particular attention for numerical and visual data exploration techniques.

Session 4: The power of data visualisation

• 25 April 2019, 13:00-17:00 - Zwijnaarde

Data visualisation is a powerful mechanism, useful in several phases of the data science workflow: it supports data exploration and understanding, and enables to present insights extracted from this data to users. However, choosing the most effective data visualisation method is not straightforward. If not selected carefully, inappropriate visualisations might lead to incorrect interpretations. Choosing the most effective visualisation not only requires knowing which methods exist and are most suited, but also requires knowledge about the domain, knowledge about the problem, and knowledge about human perception. The goal of this session is to give an overview of the existing data visualisation methods and of how they can be most effectively used to highlight important data properties, emphasize trends, reveal hidden patterns, etc.

Session 5: The art of feature engineering

• 21 May 2019, 13:00-17:30 - Zwijnaarde

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.).

Session 6: Choosing the right algorithm for the right task

• 4 June 2019, 13:00-17:30 – Zwijnaarde

The goal of this session is to introduce the participants to the most important data science tasks (classification, clustering, regression, etc.) and provide an overview of the most commonly used algorithms and techniques to solve each of these tasks. For each of the methods, its characteristics, advantages and disadvantages will be explained in order to guide the participants in making a conscious choice in terms of the available data (dimensionality, attribute types, etc.) and the expected model requirements (interpretability, accuracy, scalability, etc.). Finally, the guiding principles to train and evaluate the resulting models, including an overview of common pitfalls and frequently-used evaluation measures, will be presented.

Target audience

The mastercourse 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) are a prerequisite.

Practical information:

Pricing

 *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 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.