General Information
| Course Code | XM_0090 |
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| Credits | 6 EC |
| Period | P4 |
| Course Level | 400 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | prof. dr. W. Stam |
| Examiner | prof. dr. W. Stam |
| Teaching Staff |
dr. M. Mulhuijzen prof. dr. W. Stam drs. G.J. Beekman MA |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar, Written partial exam, Lecture |
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Target audiences
This course is also available as:
Course Objective
Academic and research skills: Students are able to generate a promising new business idea in the field of AI & Analytics and develop a prototype that validates the proposed customer needs and market potential.
Bridging theory and practice: Students understand the core theories and concepts in the field of entrepreneurship, as reflected by their ability to analyze, compare, and apply these theories and concepts to analyze and solve the key management challenges faced by real-world entrepreneurs.
Entrepreneurial soft skills: Students are able to effectively work in a team for generating and developing a new venture idea and can pitch the business concept to external stakeholders in a professional, engaging manner.
Course Content
Entrepreneurship in Analytics and AI is an engaging course for students who like to learn about entrepreneurship, its role and importance in our society, and the process by which entrepreneurs transform new ideas into successful AI-driven business ventures.
In this course, students will learn the ropes of this emerging field via three educational pillars:
1) Students will learn to understand and apply theories from economics, sociology, and psychology to study key topics in entrepreneurship, including innovation, new venture creation, entrepreneurial competences, entrepreneurial finance, scaling and growth of new ventures, corporate entrepreneurship, and entrepreneurial ecosystems. Theoretical understanding of these subjects is applied to real world cases focusing on key management challenges faced by entrepreneurs.
2) Students will familiarize with a set of entrepreneurial soft skills, which they will put into practice when they will be asked to approach different stakeholders to further their team projects to gain commitments or to obtain important information. For this purpose, the students will receive training in generating initial ideas for opportunities as well as networking and network utilization.
3) Students will be introduced to the actions and strategies that promote the process of new venture creation. For this purpose, the students, supported by the lecturers and coaching, will write (and pitch) a Student Design Sprint report around an innovative new venture opportunity in the field of Analytics and/or AI that they deem interesting for entrance into the market.
Additional Information Teaching Methods
During this course, students will attend both (guest) lectures and tutorials. The lectures explain relevant concepts, theories, and frameworks in the field of entrepreneurship and illustrate their usefulness using various practical examples. The tutorials are designed to facilitate students in making progress with their team projects by providing appropriate guidance and feedback.
Method of Assessment
The final grade for this course is based on a written individual exam, several individual pass/fail assignments, and a Student Design Sprint (SDS) project assignment.
The written exam and pass/fail assignments are individual assessments whereas the SDS assignment is a team assessment. To pass the course, the exam and SDS assignment should be graded as sufficient. Both forms of assessment have one option for re‐sit in case students received an insufficient score.
Literature
The literature for this course constitutes a selection of academic articles and business cases.