Due to the Corona virus education methods or examination can deviate. For the latest news please check the course page in Brightspace.


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Managing Innovation (minor Science Based Business)

  • Subject to changes *

Admission requirements

Third-year Bachelor students


AI is changing the ways organizations are performing and ways in which decisions are made. In this course, the goal is to understand some of this change's implications for strategic decision-making and innovation problems. This course revisits some of the most popular strategy and innovation frameworks to understand how AI can help us manage innovation and other strategic decisions. Further, this course introduces many different AI tools, focusing on the intuitions behind these methods. This course adopts a hands-on approach in implementing these AI tools using Python, a powerful programming language used widely to tackle and solve machine learning problems. To sum, the course enables students to understand, analyze, and tackle strategic and innovation problems using AI tools.

Course objectives

The course provides students with practical knowledge of using AI techniques and different strategy and innovation frameworks to tackle innovation and strategy problems. At the end of the course, students will be able to:

  • to draw insight about internal and external factors that influence innovation and strategy process, using Python.

  • to predict the outcome of different scenarios or propositions, using Python.

  • to learn from different strategic and innovation decisions

  • to describe the differences between different analytical approach for describing, predicting, and explaining.


Semester 1:

  • Course: November 9th - December 9th 2020

  • Exam: December 16th 2020

  • Retake: January 20th 2021

Please check the latest version of the schedule on the SBB website.

Mode of instruction

The course emphasizes interactive teaching. Classes start with a brief reflection on the past sessions. Then, it reviews the session’s reading(s) and discusses relevant AI methods. In the second half, the lecture focuses on a real-life case and uses Python to explore the session’s topic in practice. The session ends with a summary.

Assessment method

The course assessment includes a mix of individual and group assignments and an open book final take-home exam. The details of each component will be announced on Brightspace.

Reading list

The reading list will be announced on Brightspace.


Students have to register for the course in uSis. Click here for instructions.

This course can only be followed as part of the SBB minor (15 or 30 ECTS).




  • Students are responsible for enrolling/unenrolling themselves for (partial) exams/retakes.

  • Students are responsible for enrolling themselves for (partial) exams/retakes.

  • The deadline for enrolling for an exam/retake is 10 calendar days before the exam/retake takes place (exam date - 10 = deadline enrolling date).

  • Students who do not enroll themselves for an exam/retake by the deadline are not allowed to take the exam/retake.

  • Students fail the course if any of the components that make up the final mark of the course is assessed below 5.0.

  • Partial grades will not be rounded. If partial grades will be communicated, it is possible partial grades are rounded, but unrounded partial grades will be used in the calculation of the final grade.

  • The final grade is expressed as a whole or half number between 1.0 and 10.0, including both limits. The result is not to be expressed as a number between 5.0 and 6.0.

  • If one of the components of the final mark constitutes a component that assesses attendance or class participation, students cannot take a retake for this component. Therefore, students fail the course if their mark for this component is less than 5.0.

  • It is not possible to do retakes for group assignments. Therefore, if students fail the group assignment component, they fail the course.

  • Students pass the course if the final mark is 6.0 or higher (5.49 will rounded down to a 5 and a 5.5 will be rounded up to a 6.0).

  • For courses, for which class participation is an assessment component, students may not be penalised for an absence if the student has a legitimate justification for this absence. The student must notify the program coordinator via email (info@sbb.leidenuniv.nl) of such an absence BEFORE the lecture, describing the reason for missing the lecture. If the student does not notify the program coordinator before the lecture, the student will be penalised. Students may be required to provide further documentation to substantiate their case, and class attendance requirements are only waived under exceptional circumstances such as illness.

  • Students who are entitled to more exam/retake time must report to info@sbb.leidenuniv.nl 10 days before the exam/retake takes place.