Kies de Nederlandse taal
Course module: JM0120-M-6
Data Entrepreneurship in Action I
Course info
Course moduleJM0120-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Management; TiSEM: Management;
Is part of
M Data Science and Entrepreneurship (joint degree)
Y.K. Leung
Other course modules lecturer
L. Shen, MSc
Other course modules lecturer
B.J.P.C. Willemse, MSc
Other course modules lecturer
Academic year2020
Starting block
SM 1
Course mode
RemarksCaution: this information is subject to change
Registration openfrom 25/08/2020 up to and including 20/08/2021
Data Entrepreneurship in Action (DEiA) includes a series of courses that lie at the intersection of Data Sciences and Entrepreneurship. Each of the courses focuses on a team-based project that integrates the knowledge and skill-based courses of the semester. The purpose of the DEiA courses is to equip students with the ability to initiate and develop a data-driven business by going through the whole data cycle and developing tangible results.
In each course various aspects of the entrepreneurial process are addressed, but with a variation on focus and depth. We foresee for instance that the aim for the entrepreneurial 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.
For DEiA I, students will be challenged to come up with a valuable business solution using real-world data provided by external organizations. To accomplish that, students will be guided to explore and evaluate the market opportunity, build a business model with the lean startup method, and develop a proof of concept using a data-driven approach. Furthermore, students will be challenged to find a way to balance issues from different domains (e.g., analytics, business, law, and ethics) while developing the business model.

After 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. These include:
  • Design a business model with the lean startup method and market opportunity navigator.
  • Follow a data-driven approach for creating technical solutions.
  • Learn, build, and test the solution in an iterative fashion.
  • Communicate the core elements of the solutions to others.
  • Work project-based in a multi-disciplinary team.
  • Provide critical, constructive, and clear feedback and compare their work with that of fellow students.
  • Reflect on their role in the team, their learnings, and the different future roles they can play within the entrepreneurship and data science ecosystem.
In this first course, students will start from a rough dataset and will be challenged to come up with valuable business ideas using data mining techniques and other tools (e.g., algorithms, approaches, platforms) from their toolbox.
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 will be developed into a proof of concept. Next, an initial business model indicating how to create and assess the value of 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 with a more user-driven approach in the second course and with more attention to implementation aspects in the third course.

Students will learn to work in mixed teams combining diverse expertise, thereby reflecting on their own skills, role in the team, and learning points. Industry mentors will indicate challenges in two or three domains and provide a dataset from which the teams can work on. Student teams will also be coached by mentors from industry. The data and cases selected will be rich enough to cover also interesting business, legal and ethical issues.

Topics that are fundamental to the early stage of the entrepreneurial process will also be addressed. These include the definition, antecedents and effect of entrepreneurship; trends in data science within the entrepreneurial ecosystem; opportunity recognition; opportunity discovery/creation, business models development; customer validation.

Type of instructions
Lectures; coaching sessions; meetings.
Type of exams
Intermediary results of the project will be assessed (technical solution and business model), as well as the final report, final presentation, individual assignment and individual reflection. Industrial partners will be involved in part of these assessments.
Contact person
Y.K. Leung
Timetable information
Data Entrepreneurship in Action I
Required materials
Recommended materials
Final grade

Kies de Nederlandse taal