The Big Data Analysis potential in companies is very high. Companies that implement data driven innovation have a 5 to 10% higher productivity growth compared to those companies that do not apply data driven methods.
In the EU, the application of Big Data Analytics will lead to an increase of 1.9% in GDP between 2014 and 2020. The IDC estimates that, due to high demand, in 2020 more than 770,000 data scientist jobs in the EU will be unfilled. Yet, many companies are unable to turn this potential into a benefit due to lack of skills, both on the specific level of advanced data analysis, but also on a broader scope of digital transformation in general.
The main goal of the Innovation Training Course "Data Science and Deep Learning" (iDSDL) is to enable the implementation of data driven innovation by imparting knowledge to companies on the newest developments in the Data Science area.
The Innovation Training Course (Innovationslehrgang) includes five specialized modules:
- Data Science Fundamentals;
- Scalable Data Technologies;
- Deep Learning;
- Text Analysis and Word Embedding;
- From Data to Markets.
Half of each module is an interlocking between theoretical basic principles, on one side, and knowledge and examples of specific tools, on the other side. The other half consists of practical exercises that reinforce the learned concepts by means of concrete tools and development environments. The information and utilized tools are based on the technological state of the art and on the technologies employed by the lecturers in their numerous research and industry projects.
The changes above-mentioned, affect not only the research and practice oriented data areas but concern also the behavioral sciences and have socio-political aspects. In order to have a complete problem statement, ethic and diversity issues must also move into focus. To this end we planned for the compulsory module "Integrated Strategy: Ethics and Equal Opportunities" The project has in total 26 partners: 2 university partners - TU Wien and Donau Uni Krems - and 24 companies, the coordinating partner being the Information & Software Engineering Group at the Institute of Information Systems Engineering (ISE), TU Wien, Austria. The company areas of activity include banking and financial, medical care, logistics and transportation, image and video processing, gaming, and comprises of both large institutions as well as SME and start-ups.
To guarantee the knowledge transfer from research into practice, and to respond to the actual needs of the participating companies, an iDSDL mandatory component are Knowledge Transfer Projects. The goal of a Transfer Project is to enable its participants to recognize and describe potential application areas for Data Science in an enterprise and to design adequate solution and intervention strategies that they can subsequently implement in form of a project. The Austrian Federal Ministry for Digital and Economic Affairs (BMDW), through the Austrian Research Promotion Agency (FFG), fund the project. The project is part of a wider Austrian digital initiative that systematically supports the build-up of higher qualified resources in innovation and research, for small and medium size companies, as well as large companies. iDSDL runs from February 2018 to May 2020, with a total budget of EUR 1.3 Mil.
The Innovation Training Course: Data Science and Deep Learning follows two strategical goals:
- To guarantee the current knowledge transfer into companies;
- To ensure the companies' competitiveness.
Both goals can be reached through employees' increased qualifications. Furthermore, the knowledge transfer through both teaching modules and Transfer Projects will allow companies to not only address their current data related issues, but also to identify ways to diversify their product portfolios. There are more than 70 participants in the training course, from 24 companies with offices in Austria.
The way the Transfer Projects are executed, by face-to-face meetings with company employees and common workshops, we expect that the persons that are part of our project transmit their gained expertise to their colleagues and teams.