Admission requirements
Assumed prior knowledge
Elementary calculus and linear algebra, basics of probability theory and statistics, basics of python.
Description
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.
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. In detail the Intended Learning Outcome is:
List the steps involved in a Business Analytics (BA) project using Machine Learning (ML).
Explain good practices for analyzing; visualizing; and communicating your BA results.
Explain ML methods used in BA.
Implement the best practices of Business Analytics on sample use cases using Google Colab and Python.
Discuss how various BA problems can be approached using the knowledge learned in the course.
Debate the guest lectueres and their specific approaches to BA.
Create your own BA report for a sample use case.
Schedule
The timetables are available through My Timetable (see the button in the upper right corner).
Teaching method
Interactive lectures.
Assesment method
Final grade (F) = 0.4∙E + 0.3∙A1 + 0.3∙A2; where E; A1; and A3 are grades for the exam and each assignments.
The course grading is based on two individual home assignments and one exam aligned with the course objectives. Each assignment will be announced on Brightspace during the lecture. Late submissions of the assignment or absence during the exam will be graded 0. If an assignment is not submitted on time, there will be one resubmission date per assignment during the semester. The exam has one re-sit date during the semester. Grades will be published after the final submission and resubmission dates for all assignments and exam. Assignment and exam grades cannot be transferred to subsequent semesters.
Students are encouraged to use Generative AI techniques for completing their assignments. For each assignment, students must include a disclaimer detailing how the Generative AI results were obtained.
Resit, review & feedback
The grade for the written exam should be 5.5 or higher in order to complete the course. The exam has a regular written re-sit opportunity. The weighted average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the assignments is not submitted the grade for that assignment is 0. Each assignment has a re-sit opportunity (a later submission); the maximum grade for a re-sit assignment is 6.
Reading list
The books are recommended for further reading and additional explanations. It is not mandatory to buy the books.
Burkov; A.; 2020; Machine Learning Engineering
Bishop; C.M.; 2006. Pattern Recognition and Machine Learning
Google Colab (you need a google account)
Grossmann; W. and Rinderle-Ma; S.; 2015. Fundamentals of Business intelligence.
Registration
Enrolment through MyStudyMap (button in upper right corner) is mandatory. General information about course and exam enrolment is available on the website https://www.student.universiteitleiden.nl/en/your-study-programme/courses-and-exams/enrolment/leiden-university
Contact
mastercs@liacs.leidenuniv.nl
Remarks
There is only limited capacity for external students. Please contact the programme Co-ordinator
Important information about the course will be shared in Brightspace.
Software
Starting from the 2024/2025 academic year; the Faculty of Science will use the software distribution platform Academic Software. Through this platform; you can access the software needed for specific courses in your studies. For some software; your laptop must meet certain system requirements; which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.