Keep me informed
In autumn and spring you can attend live Meet & Ask sessions. Do you want us to keep you informed on news and upcoming events?
Programme Director Eelke Heemskerk and Student Assistant Tommy Blomvliet share all the ins and outs of the Bachelor's Computational Social Science, including career prospects for graduates.
If you are interested to learn more about projects and themes of the programme Computational Social Science, the value of interdisciplinary teaching and learning and all the opportunities that the city of Amsterdam offers its students? Please view the recording of our Meet & Ask session (March 8th, 2022). Our programme team answers a wide range of questions from those who joined this session, which might be helpful to you too!
Frequently asked questions
Is there a lot of math involved in the programme?
Mathematics is fundamental to learning statistics and programming. Therefore, various semesters of our programme in Computational Social Science will include a bit of mathematics teaching.
For programming, you will need to learn the basics of linear algebra and calculus to be able to understand algorithms. You will learn to design and implement them 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 concepts related to algorithms. However, the setup of our programme ensures that all students will be supervised to reach the same, 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. Please don’t get scared if the 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 if a result of a research project is significant, the probability that the finding is by chance is calculated and compared to a normal distribution.
How will you learn programming and what will you learn exactly?
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.
Do you need to have any prior knowledge of programming before starting your first year?
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.
What are the differences between Computational Social Science and other programmes?
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 Studies 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, 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.
How is student performance assessed in this programme?
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.
What will/does the student population look like?
As Computational Social Science is a brand new programme, we aren’t able to describe the current student population. However, the programme is English-taught, located in the vibrant city of Amsterdam and expects to attract a combination of Dutch and international students from all over the world.
We envision 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.
Do I need to bring my own laptop?
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 (email@example.com)