The Data Science Master's programme has been developed in collaboration with Amsterdam Data Science (ADS), and is designed for you to become an all-round data scientist who knows how to go through all the steps in a data-driven project; from how to approach a business or societal problem from a data-analytical perspective (asking the right questions) to the final implementation of data science solutions, while also understanding the organisational and social implications these solutions may have.
The curriculum and courses
In the first semester you will take three core courses and at least one elective. In the second semester you will take one more core course and one more elective, before focusing on your Master's thesis.
For detailed information regarding the curriculum and courses, please see the UvA Course Catalogue via the link below.
Fundamentals of Data Science
The first course Fundamentals of Data Science provides an overview of the essential concepts, theories and challenges in Data Science. You and your team members work on case studies from multiple domains and disciplines in the field of Data Science. You learn to approach business or social problems data-analytically.
Statistics, Simulation, and Optimisation
Statistics, Simulation, and Optimisation focuses on the understanding of the statistical methods and their application when dealing with data problems.
Applied Machine Learning
Applied Machine Learning concentrates on understanding, applying, and evaluating algorithms that learn from data.
Data Systems Project
Throughout the first semester you will learn and experiment with the creative process of developing an interaction environment as part of research into complex systems in the Data Systems Project. You will focus on stakeholder-research, user-research, context mapping, agile development to a technologic prototype, including its validation. In this course you collaborate with students of the Information Systems track.
In the final core course Big Data, you will be introduced to state-of-the- art expertise in big data management. In addition, we will invite guest lecturers who work on large scale data projects.
Throughout the Master's programme you have ample choice of electives focusing on various topics such as Data-driven Business and Entrepreneurship and Information Visualisation. In addition you can follow the core courses of the Information Systems track as an elective.
The last part of the Data Science programme is dedicated to an individual research project, culminating in a Master's thesis. It provides a unique opportunity for you to apply your knowledge of the theories and methods used in data science to address data oriented problems in industry, government or the non-profit sector. The thesis combines the statistical, technical, managerial challenges with social issues involved in solving complex data science problems. The thesis project can be conducted at a research organisation, in collaboration with an industrial partner, or within an ongoing research programme at the UvA.
Accreditation and title
This Master's programme has been accredited by the Accreditation Organisation of the Netherlands and Flanders (NVAO). Upon successful completion of the programme (a total of 60 EC), students will receive a legally recognised Master's degree in Information Studies and the title of Master of Science (MSc).
The programme is also available on a part-time basis, in two years instead of one. You follow one half of the curriculum in the first year and the other half in the next. There are no special part-time lectures in the evenings or weekends. Part-time requires 20 hours per week of study, this include 4-6 hours on average of contact hours; however, this may differ across courses. Contact hours will most likely be spread across two days per week, however, we can't guarantee this. The exact schedule becomes available a few months before the start of the programme in September, and typically after the application deadline.
Bring your own device
All students enrolled in Information Studies are requested to bring their own laptop, due to the nature of the programme. More information on specific system requirements can be found here.