Data Science & Artificial Intelligence
Programme
Data Science (DS) and Artificial Intelligence (AI) are omni-present and have the potential to be incorporated in various fields. However, in order to live up to this expectation, a good understanding of the techniques, their implications and limitations is required. In this minor you develop insights and skills on the intersection of the fields of data science, artificial intelligence. The overall aim is to get an understanding and basic skills in integrate AI technologies with problems that could arise during your main (major) program. The curriculum is aimed at putting the teaching material into practice.
At the end of the minor you have attained the following learning outcomes:
Basic Python programming skills, including control structures, data structures, and object-oriented programming; (primairily: Basic Programming for AI)
Basic understanding of data management, data wrangling and data visualisation techniques (i.e., how to massage data into aan artificial intelligence solution and visualise this); (primairily: Data Management & Visualization)
Evaluating and presenting the performance and other implications (such as ethics) of a machine learning solution; (primairily: Essentials of Machine Learning)
Analyse the role of artificial intelligence on your main (major) program; (primairily: Data Science and AI Applications)
Apply machine learning and artificial intelligence skills to a certain field (e.g., the one of your main program) (primairily: Essentials of Artificial Intelligence)
These outcomes encompass technical programming skills, theoretical knowledge of AI, practical application of Data Science and AI, and an understanding of the impact of AI on society from various perspectives (including an ethical perspective).
Studyload
The study load of the minor program in Data Science & Artificial Intelligence is 30 ECTS, in the autumn semester.
Language
The minor is taught in English
Accessibility
The minor in Data Science & Artificial Intelligence is accessible to bachelor's students with an interest in both the methods, processes, and systems used to extract knowledge from data, as well as in computer science.
Number of participants
The minor has a minimum number of participants of 15 and a maximum capacity of 90 participants.