Elaboration of educational modules to be positioned within the repository is the core of WP5, Educational/training modules (AUC). The process of modules structuring includes: selecting industry and cross-domain (interdisciplinary) topics relevant and suitable to adopt through DS approach; developing educational modules according to these selected topics (scenario selection, module development and simulation case development). All modules will be driven by 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 development of educational modules to be implemented into the platform with the appropriate format.

The process of structuring of the modules includes: selecting industry and cross-domain (interdisciplinary) topics relevant and suitable to adopt through data science approach; developing 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 analyse will be performed continuously, the core of modules can be further updated in order to reflect the latest market needs. The methodologies will be tailored to examples and should include theories, methods and data divided in industry and/or cross-domain topics.

These modules will be structured in end-to-end business case but with flexibility and simplicity in mind, avoiding a pure data scientific approach (data collection methods, statistical modelling, experiment design, computing etc.) if certain industry will only use case as cook-book or focusing on technical background if industry requires such approach. Primary goal is to expand the potential impact of data science to the non-technical academic and business sectors while next level goal is to provide endto- end cases with value creation in focus and with clear sense of why such case is selected to be used in learning process. Educational modules will differ in methodology used (case study, situation game, simulation etc.) and sectors/topics they elaborate. Topics should cover wide range of different industries and crossdomain 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 be built around the notion of learning outcomes, units of learning and credits. This will facilitate the adoption of the educational material by HEIs and business/industry sector interested in topic. Namely, modules will be designed in a way they can be used “of the shelf” to create new or improve existing study programs in higher education or in adult training.

Educational material/scenario will contain:

  • – Business problem/task decomposition (whitepaper, up to 5 pages)
  • – Approach analysis for business problem/task to be tackled by data science methods (methodology, up to 3 pages)
  • – Data selection (memo)
  • – Data science technique selection based on business problem/task and available data (memo)
  • – Model development (methodology and/or simulation)
  • – Results decomposition (whitepaper, up to 3 pages)
  • – Operationalization of results in business environment (guidelines; transferable to different industries)

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

WP5 will produce the following deliverables:

Education material containing: business problem/task decomposition; approach analysis; data selection

  • DS technique selection
  • model development; sample data set development
  • result decomposition
  • operationalization of results
  • 2nd multiplier event