Mastercourse 'Data Innovation beyond the Hype'
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The mastercourse ‘Data Innovation beyond the Hype’ provides pragmatic and industry-oriented modules on data-driven innovation. The mastercourse cycle consists of general modules, covering the different steps of the data science workflow. Additionally, the Lab has developed a set of topic specific modules focusing on industrial applications of data science and artificial intelligence. Finally, the lab has enhanced its training portfolio with a module introducing the concept of Artificial Intelligence to a non-technical audience.
MODULES COVERING THE DATA SCIENCE WORKFLOW
Module - The importance of data exploration and hypothesis building
A clear picture of the business opportunities and a library of intelligent algorithms are not enough to develop a data-driven solution. A thorough understanding of the quality of the available data and the interesting information it contains is equally important in order to derive viable working hypotheses about the underlying mechanisms of the problem under investigation. In this module, various approaches to such data exploration are discussed, with a particular focus on numerical and visual techniques.
Module - The power of data visualisation
Data visualisation can be applied in different phases of a data-driven project. It supports data exploration to extract first insights from the data and enables the presentation of these insights to users. However, choosing the most efficient method for data visualisation is not self-evident. This module aims to provide an overview of existing data visualisation methods and how they can be used most effectively to highlight important data properties, highlight trends, reveal hidden patterns, etc.
Module - The art of feature engineering
In order for intelligent algorithms to learn something from data, the data must be presented in the best possible way. The process of transforming data and preserving only the most relevant, distinctive features is called feature engineering. It is perhaps the most important step in the data science workflow. This module aims to provide an overview of the most commonly used methods, the most common pitfalls for different types of data (sensor data, location data...) and problem sets (prediction, profiling...).
Module: Choosing the right algorithm for the right task
The goal of this module 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.
Hands-on trajectory 'How to tackle your specific data challenge?'
The EluciDATA Lab has developed a practical and industry oriented hands-on trajectory to help companies acquire AI and data science competences illustrated via real-world industrial challenges. The objective of this trajectory is to help them in addressing their specific data challenge inspired by the approach applied in similar use cases.
MODULES FOCUSING ON INDUSTRIAL APPLICATIONS
Module - How to leverage data to reliably benchmark asset performance?
In this module, we will zoom in on how to leverage data to reliably benchmark asset performance. Several methods will be presented to compare assets in terms of operational performance, for which their main advantages and disadvantages will be explained in detail, as well as the most important data characteristics and parameters to take into account. The techniques will be illustrated by real-world industrial case studies, showing how the methods can be used to highlight performance trends, detect underperforming assets and identify anomalous operating behaviour.
Module - The industrial relevance of advanced AI/ML methods
The goal of this session is to give participants a better insight into the type of problems that can be tackled using machine-learning techniques (deep learning, federated learning,active learning, reinforcement learning and bio-inspired computing), illustrated by several real-world examples from a diverse set of domains.
NON-TECHNICAL MODULE TO UNDERSTAND WHAT AI IS ABOUT
In this module, we explain what AI is and what it is not. We explore with the participants the potential of AI and walk through the different steps of an AI project from a technical and non-technical perspective, focusing on what needs to be taken into account in order to ensure that the project is successful. Finally, we cover ethical, privacy and security considerations that should be considered when designing and conducting an AI project.
On demand we can also give the different sessions at your company’s premises tailored to your specific data challenges or provide an intensive training program with one of your colleagues working on your specific data challenges. Feel free to contact us in case your are interested at firstname.lastname@example.org
The mastercourse is open to any company that is interested in data innovation as an opportunity for its company and activities. To follow these modules, some basic analytic skills (e.g. high-level understanding of algebra and interpretation of statistical figures) is a prerequisite. All sessions can be followed independently from each other.
Pricing (all prices are exclusive of VAT)
Session number and title
|Module: modules covering the data sciences workflow & the session on industrial relevance of advanced AI/ML methods*||575 EUR|
|Module: Hands-on trajectory 'How to tackle your specific data challenge?'||To be announced|
|Module: How to leverage data to reliably benchmark asset performance ?||275 EUR|
|Module: Understanding what artificial intelligence (AI) is about||275 EUR|
* Thanks to the support of Vlaio (Industriepartnerschap), companies located in Flanders pay only 575 euro for 2 modules, if they actually book and follow at least 2 modules (1 module = 2 half days). This cannot be cumulated with the KMO-portefeuille.
In case you are a Flemish SME, it is possible to make use of the KMO-portefeuille. The KMO-portefeuille should be requested at latest 14 days after the course has taken place. (Sirris approval number: DV.O105154). Please note that if you do not apply in time your request will be declined. For more details on the KMO-portefeuille, please visit kmo-portefeuille.be or contact us through email (email@example.com).
All sessions are given in English.
Our general terms and conditions for the training
- Invoices will be sent after the event. You can consult our general conditions on our website.
- If you are unable to attend, you can be replaced by a colleague. Please notify us by email to firstname.lastname@example.org of the name of your colleague.
- Cancellations must be made by email to email@example.com. You can cancel your participation free of charge up to 3 working days before the event. In case of cancellation after this date or non-participance without cancellation, you will be invoiced for the full participation fee.