|This is a series of subsequent courses on Data Entrepreneurship in Action. The purpose of these courses is to provide students with the ability to initiate and further develop data-driven new business development processes, by going through the whole datacycle, developing tangible results. Each is a team-based project course that integrates the courses of the semester, in the first semester specifically focusing on the knowledge and skills obtained in the data mining, data entrepreneurship, and strategyand business models courses.|
The aim is to confront students with all aspects of Data Entrepreneurship and challenge them to find their way, balancing issues from analytics, business, entrepreneuring, law, ethics, etc.. In each course all aspects are addressed, but with a variation on focus and depth. We foresee for instance that the aim for the entrepreneuring aspect can grow from a tentative plan on a few pages to a well-developed business plan, including an analysis of the market, competitors, phasing, resources needed, etc. Also, different application domains are addressed, to make students aware of the varying demands and issues of these.
SpecificsAfter completion of the course, the students are able to develop a concrete solution for a data-driven new business development opportunity, thereby actively integrating previously acquired knowledge and skills. This includes:
- follow a data-driven approach for creating technical solutions and designing businessmodels;
- communicate the core elements of the solutions to others and test them to improvethe solutions;
- work project-based in a multi-disciplinary team;
- provide critical, constructive, and clear feedback and compare own work withthat of fellow students;
- reflect on their role in the team, their learning¿s, and the different future rolesthey can play within the data science and entrepreneurship ecosystem.
|In this first course, students will start from a rough dataset and will be challenged to create value with the data using data mining techniques and other tools (algorithms, approaches, platforms, languages, ¿), picking in an underpinned way the right tools from their toolbox.|
Topics of the first data challenge ¿ that is foundational in nature- include: Entrepreneurship: definition, antecedents and effect, opportunity recognition (trends in data science) versus discovery/creation, brainstorming & creativity techniques, causal planningversus effectuation, business models and business model innovation, market research and customer validation, competitive strategy and value chain/network, ecosystems & collaboration.
These innovative data-centered ideas will then be translated into concrete project requirements for which a range of possible technical solutions are considered and one solution is worked out in a proof of concept. Next, an initial business model indicatinghow to create and appropriate value with the solution will be worked out. Finally, the effectiveness of the technical solution and business model is assessed. This data-driven approach will be contrasted in the second course with a more user-driven approach and in the third course with more attention to implementation aspects.
The subsequent Master¿s Thesis project will be an individual project combining all aspects of the three previous Data Entrepreneurship in Action courses. Students will learn to work in mixed teams combining diverse expertise, thereby reflectingon their own skills, role in the team, and learning points. Industry mentors will indicate problems and datasets in two or three domains from which the teams can choose. The teams will also be coached by mentors from industry. The data and casesselected will be rich enough to cover also interesting business, legal and ethical issues.
Type of instructions (guest) lectures; coaching sessions
Type of examsIntermediary results of the project teams will be assessed (technical solution and
business model), as well as the final report, final presentation, and individual reflection.
External partners will be involved in part of these assessments.
|Required materials-Recommended materials-|