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Machine Learning for Business Analytics


Admission requirements

Assumed prior knowledge
Elementary calculus and linear algebra; basics of probability theory and statistics; basics of python.


Many decisions in business and government are taken on the basis of data that come from all sorts of information sources. Business Analytics (BA) is about the use of the information stored in this data to generate new and useful knowledge that can support executive managers and governments in taking decisions.
The interest in BA is growing due to the increasing awareness of companies and governments that the vast amounts of data collected on humans and their behaviour contain valuable information. The volume of the collected data enables the use of Machine Learning methods to ‘learn’ the relevant knowledge related to the business question. Different types of knowledge can then be derived from the ‘learned’ models, like rules characterising potential customer classes, knowledge classifying groups with larger risks, and so on. Quite often useful causal relations are hidden in the data and the goal of the BA is to induce these from the data and to represent them in meaningful ways to improve businesses or governments. Typical business cases are: cross-selling, churn in mobile communications, and risk analysis in financial services.
The course is given by two lecturers Dr. Marc Hilbert and Dr. Andrii Kleshchonok, who combine more than 20 years of experience in applying Machine Learning in the R&D, automotive, chemical and energy industry.

Course objectives

The emphasis in this course will be on the methodological and practical aspects of BA. This includes the non-technical aspects such as: how to structure a BA project based on executive questions, current legislative development (EU AI Act) and the future of Machine Learning models in business. As well, the course focuses on the basics of Machine Learning: data reading, visualisation and statistical tests for decision making, introduction to artificial neural networks.
The course invites guest speakers from industry (e.g. Shell) to share their view of Machine Learning in BA and provide the students with first-hand industrial experience.
In addition, the skill objective for the course is to give the student some hands-on experience with Machine Learning methods. By working with Google Colab in the course and at home the student will develop basic knowledge to analyse a simple data set with Python.


You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

interactive lectures.

Assessment method

Final grade (F) = 0.7∙E + 0.15∙A1 + 0.15∙A2, where E is the Exam grade, and A1 and A2 are grades for assignments.

The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.

Reading list

Grossmann, W. and Rinderle-Ma, S., 2015. Fundamentals of Business intelligence. Springer.


Every student has to register for courses with the new enrollment tool MyStudyMap. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


Programme Co-ordinator: ms. Esme Caubo


There is only limited capacity for external students. Please contact the programme Co-ordinator
Important information about the course will be shared in Brightspace.