WP5, Educational/Training Modules Home|About the project|WP5, Educational/training modules The elaboration of educational modules positioned within the repository is the core of WP5, Educational/training modules (AUC). The process of module structuring includes: selecting industry and cross-domain (interdisciplinary) topics relevant and suitable to adopt through a DS approach and developing educational modules according to these selected topics (scenario selection, module development and simulation case development). All modules will be driven by specific industry examples. and will be based on the demand analysis carried out in WP3. Modules will be prepared in a way that they could be used by any HEI in order to structure/append its study programs. The main objective of this IO3 is the development of educational modules to be implemented onto the platform within the appropriate format.

The process of structuring of the modules includes: the selecting of industry specific and cross-domain (interdisciplinary) topics relevant and suitable to adopt through a data science approach, and the development of educational modules according to these selected topics. All modules will be driven by industry examples and will be based on the demand analysis carried out in WP3. Since this analysis will be performed continuously, the core of the modules can be further updated in order to reflect latest market needs. The methodologies will be tailored to the examples and should include theories, methods and data divided in industry and/or cross-domain topics. These modules will be structured in an end-to-end business case but with flexibility and simplicity in mind, avoiding a pure data scientific approach (such as data collection methods, statistical modelling, experiment design, computing etc.).

Certain industries will only use the individual case as a cookbook, or focus on the technical background, if the industry deems that it requires such an approach. The primary goal is to expand the potential impact of data science to the non-technical academic and business sectors while the next level goal is to provide end-to- end cases with a value preposition in focus and with a clear sense of why such a case is selected to be used in a learning process. Educational modules will differ in the methodology used (case study, situation game, simulation etc.) and the sectors/topics they elaborate. Topics should cover a wide range of different industries and cross-domain topics that can illustrate the main benefits of data science and, at the same time, attract different populations (HEI, policy makers, entrepreneurs, industry etc.). At least one module will cover the cultural heritage area (defined as the horizontal priority). Once it is fully developed, the educational material will use an output-based approach and will be built around the notion of learning outcomes, and units of learning and credits. This will facilitate the adoption of the educational material by HEIs and business/industry sectors interested in such a topic. Namely, modules will be designed in a way they can be used “off the shelf” and help create new or improve existing study programs in higher education institutions or in adult training.

Educational material/scenarios will contain:
• – A business problem/task decomposition (whitepaper, up to 5 pages)
• – An approach analysis for a business problem/task to be tackled by data science methods (methodology, up to 3 pages)
• –A data selection (memo)
• – A data science technique selection based on a business problem/task and available data (memo)
• – A model development (methodology and/or simulation)
• – A results decomposition whitepaper, up to 3 pages
• – An operationalization of results in a business environment (guidelines; transferable to different industries)

The material will be frequently revisited to ensure that it covers the actual needs/themes and to verify that it can be used in various learning and professional pathways. This kind of approach encourages all groups of stakeholders included in the process, while it is especially suitable for students (end users). Not only is this kind of platform conducive to different users, allowing them to concentrate on their engagement focus (eg. data science, business impact), as long as it is a part of use-case, but also it encourages team work and explains how the result can be reached even with a focus only on one specific expertise. Users can, furthermore, substitute data from learning cases with their own data and go through the whole process, testing, on the one hand, how results can change and, on the other, manipulate the environment and analyze how a slightly different approach may affect their target values. Finally, with the development of technologies, hardware and tools, this approach helps customers to invoke technological advancement into the same business cases and to study its impact through a platform as proactive as the Learning Motivation Environment (LME).

WP5 will produce the following deliverables: Educational material containing: a business problem/task decomposition; an approach analysis; a data selection
• a DS technique selection
• a model development; sample data set development
• a result decomposition
• an operationalization of results
• a 2nd multiplier event