The first year consists of two semester-long, 20-week courses of 30 EC each. In these courses, you will work in small groups (4-5 students) on one (or two) overarching project challenges. Thematically, semesters 1 and 2 focus on climate change and digital surveillance, semester 3 on health and mobility, and semester 4 on inequality.

The first year provides you with the foundation for successful learning within a transdisciplinary programme. You will understand the complexity of societal challenges, appreciate that these are open to multiple interpretations, and value these different interpretations. You are also introduced to the basics of data science - including programming skills (Juypter notebook, Python) - and empirical research and the individual level of analysis and intervention.

See examples of project challenges.

In the second year, semesters 3 and 4 consist of semester-long, 20-week courses of 30 EC as well and are thematically focused on health and mobility and inequality respectively. The second year introduces you to the analytical level of social practices and systems as well as inviting you to turn your attention to the structural level of analysis. You will work on group assignments proposed by external partners. You are challenged to think about (systemic) digital interventions that may improve the interaction, coordination or communication between stakeholders in a digital system. Finally, you will become familiar with structural cleavages and inequalities in society. You learn how structural inequalities may translate into ‘biased’ applications of Artificial Intelligence (AI), and you are challenged to propose interventions that result in less biased AI solutions.

In the third year, you can opt for a minor or electives from other programmes, take an internship or study abroad in the fifth semester. You will complete your degree programme with a 30 EC capstone (or graduation) project for a real-world client organisation in semester 6.

If you are ambitious, you can choose to take part in our Honours and Talent Programme (HTP). You’ll take the HTP alongside your regular studies. You will be introduced to scientific research in an original way through a challenging package of in-depth or broadening courses. If you are up to it, then it's an opportunity not to be missed!

The UvA has partnerships and exchange agreements with more than 100 other universities. As part of your Bachelor's programme you can do an exchange semester abroad. This can be a valuable learning and cultural experience, and a great addition to your degree programme.

Note: You can only go abroad in your third year (fifth semester).

There are various opportunities during the Bachelor’s programme for you to shape your programme to your liking. You can gain 30 elective study credits with courses that are part of another Bachelor's programme at the UvA, thereby doing an additional specialisation. Or you can choose a minor: a cohesive programme lasting half a year (30 EC) taken outside of your own programme. You can choose a minor in Communication Science or Entrepreneurship, for example.

You can devote your fifth semester to taking an internship at an organisation of your choice. This internship will provide you with the opportunity to gain relevant work experience, and apply your academic knowledge in a professional setting. Moreover, the internship will enable you to develop and apply practical skills while putting to use the knowledge that you have gained during the programme.

Contact hours: On average, students will have 14-18 hours of classes per week

• Large-scale lectures: 4 hours per week
• Small-scale tutorials: 6 hours per week
• Practical sessions/workshops: 8 hours per week
• Self study: 22 hours per week
• Total study load: 40 hours per week

• Lectures: During lectures, a teacher explains the subject matter, and you have the opportunity to ask questions.
• Tutorials: In tutorials, you practice with the subject matter in smaller groups, under the guidance of a teacher.
• Practical sessions/workshops: During practicals, you learn practical skills and conduct experiments.

Every Monday morning, you will have a check-in meeting with your tutor in small groups. During these meetings, last week’s progress, this week’s plans and tasks, and students' need for support are discussed.

Every Friday, during the check out, you will present and review the progress your project group made and prepare for the project deadline at the end of the day. This creates a weekly deadline and an opportunity for you to reflect on your activities and participate in peer feedback.

Academic programmes within the Netherlands such as Data Science and Artificial Intelligence have a much stronger and exclusively technical focus, while programmes such as Information Studies, Business Information Technology and Science, Business & Innovation opt for a more business or management-oriented approach. Computational Social Science focusses on more than that: societal issues and change that can be set in motion with the help of data-driven digital innovations.

Interdisciplinary Social Sciences, Digital Society, Global Arts, Culture and Politics, Psychology, Law and Economics are programmes that have an exclusive focus on social sciences, humanities, economics and/or law without the accompanying digital expertise that Computational Social Science offers.  

Other interdisciplinary programmes, such as Future Planet Studies, Science, Technology & Innovation,  Beta-Gamma and ATLAS, are directed towards different themes than those of Computational Social Science and, generally speaking, address natural sciences-oriented subjects. A thematically related programme to Computational Social Science is Management, Society and Technology. However, this programme has its origins in and focuses on public administration.

To sum up, Computational Social Science enables students to gain hands-on experience with data science, artificial intelligence techniques and programming skills fully integrated with perspectives from social sciences and humanities on digital innovations in society.

An understanding of mathematical concepts is fundamental to learning statistics and programming. Therefore, various semesters of our programme in Computational Social Science will include some lectures on mathematical topics.

To program, you will need to learn the basics of linear algebra and calculus to be able to understand certain algorithms, as you will learn to design and implement these during your studies. If you have prior knowledge of Wiskunde B (Math B, according to the Dutch educational system), you will have a slight advantage in understanding these concepts. However, the setup of our programme ensures that all students will be supervised to reach the  required level in linear algebra and calculus.

For example, it will be easier for you to understand a concept such as gradient descent. Gradient descent is a widely used algorithm in machine learning for finding local minima. It uses the partial derivative of a function  but do not worry  if this concept means nothing to you right now.

For statistics, statistical concepts and statistical testing are rooted in probability analysis and distributions. If you have prior knowledge of Wiskunde A (Math A, according to the Dutch educational system), you will have a slight advantage in understanding these sorts of concepts.

For example, you might have an easier time with problems relating to normal distributions and statistical testing. To certify thata result of a research project is significant, the probability that the finding is by chance is calculated and compared to a distribution.

Our programme consists of a study load of 52 ECTS in teaching and learning within Digital Expertise (DE) and is dedicated to a wide range of computational skills. Within the DE learning trajectory of our curriculum, you are going to learn programming and data wrangling in Python, user experience (UX) design applications and web development in JavaScript, and various modelling and machine learning techniques to interpret data and comprehend complex system structures.

Teaching and learning in DE will be mostly done via practical sessions in which you will actively work on exercises and assignments guided by a Teaching Assistant. You will continuously apply computational skills in your semester projects integrated with the knowledge and skills acquired through the other learning trajectories in out curriculum, such as Research Expertise (RE) and Change Making Expertise (CME).

An important aspect of our programme is, therefore, its focus on application. Once you have graduated, you will be highly competent in applying a proper computational tool, chosen from your elaborate toolset, to tackle real-life societal problems with real-life data.

No, you don’t. If you have experience programming, you might have a slight advantage at the beginning. However, our programme team assumes that you have no programming experience. There will be ample supervision and exercises to help you develop a basic level of programming skills to succeed in your studies.

Since Computational Social Science is solely focused on project-based teaching and learning, 50% of the total assessment in each semester takes place through the semester-long group projects. Small groups of about 4-6 students hand in weekly assignments leading up to a final product at the end of the semester. These weekly assignments can, for instance, be written project proposals and group presentations. The remainder of your final grade each semester (50%) is obtained through individual assessment, such as literature reviews, essays, reflective reports on team collaboration or programming assignments.

Find out more about group projects

Our programme in Computational Social Science endorses the policy of Bring Your Own Device (BYOD) for teaching and learning. As a student, you will be required to have a laptop of your own to be able to participate in various educational components of the programme.

You may decide on the laptop yourself. However, the programme has set minimum requirements to your device. Please see the list below.

• Internal memory (RAM): 4GB or more
• Storage (HDD or SSD): 128GB, SSD is recommended
• WiFi: Multiband (2.4GHz and 5GHz)
• Screen diagonal: minimum 11"
• Screen resolution: 1366x768 or better, 1920x1080 is recommended
• Webcam internal or external for distance learning
• Chromebook, iPad or tablet are not sufficient
• Make sure that a decent WiFi connection is possible

Please note: in principle, all operating systems (Windows, macOS, Linux) can be used, but bear in mind that most students will be using Windows. With other operating systems, more research may be needed. Some courses may use software that only runs on Windows; the programme will provide the necessary software and licenses to run Windows in a virtual machine for this purpose. If you are a Mac user, your laptop must have at least the OSX 10.9 (Mavericks) operating system.

If you have questions or remarks concerning BYOD for Computational Social Science, please do not hesitate to reach out to our Study Adviser (studyadviser-cssci@uva.nl)

• The student population of Computational Social Science is a combination of Dutch and international students from all over the world. About 30% of students completed their previous education in the Netherlands and 70% of the students have an international background. Last September, ±100 students started with this Bachelor’s programme.

  We aim to offer an international classroom that will provide you with an advantage in both the present and the future. During each lecture and seminar, you will be encircled by a group of students and programme staff of various languages and cultures, personal interests and prior education. Discussions and projects on those societal issues that are labelled ‘wicked problems’ at the core of our programme will thus be enriched by the broad range of perspectives and experiences.

  Precisely thanks to the diversity of our student body, the programme strives for a safe learning environment for each and every one of you and our team will do its utmost to make you feel at home within our Computational Social Science community.

The student population of Computational Social Science is a combination of Dutch and international students from all over the world. About 30% of students completed their previous education in the Netherlands and 70% of the students have an international background.

We aim to offer an international classroom that will provide you with an advantage in both the present and the future. During each lecture and seminar, you will be encircled by a group of students and programme staff of various languages and cultures, personal interests and prior education. Discussions and projects on those societal issues that are labelled ‘wicked problems’ at the core of our programme will thus be enriched by the broad range of perspectives and experiences.

Precisely thanks to the diversity of our student body, the programme strives for a safe learning environment for each and every one of you and our team will do its utmost to make you feel at home within our Computational Social Science community.