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Bachelor
Computational Social Science
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Study programme

Computational Social Science encompasses three years, six semesters and 180 credits (EC). All learning activities are uniquely organised around projects in a realistic context or provided by a real-world client organisation. This will support you in developing and integrating social sciences and humanities expertise (SSHE), digital expertise (DE), research expertise (RE) ánd changemaking expertise (CME).

Programme structure
  • Year 1

    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.

  • Year 2

    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.

  • Year 3

    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.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Foundation: Appreciating the complexity of social challenges
    Period 1
    Period 2
    Period 3
    30

    Throughout the semester, students tackle climate change and surveillance challenges. Design thinking, literature research, and workshops on collaboration and time management are emphasized. Theoretical exploration delves into societal complexities and stakeholder perspectives. Practical skills are developed through data analysis techniques, Python programming, and data visualization. The semester culminates in project reflection and individual growth assessment.

  • Building blocks: Experimenting with digital interventions of behavioural change
    Period 4
    Period 5
    Period 6
    30

    Create a digital behavioral change prototype focused on climate change or surveillance. The process involves sensing, visioning, prototyping, and testing cycles, including focus groups and website creation. You learn about project management, stakeholder analysis, collaboration, and persuasive writing.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Connections: Linking data for better interventions in health or mobility systems
    Period 1
    Period 2
    Period 3
    30

    Engage in exploring (systemic) digital interventions to enhance stakeholder interaction in mobility or health systems. Collaborate in project teams on challenges proposed by external partners. Instead of creating a technology, you'll produce an intervention document for implementation. Consider business, ethics, theory, and methods in the document. Learn data collection and analysis techniques like content analysis and machine learning, study complex processes, and reflect on your group project and personal growth.

  • Structures: Applying responsible AI to reduce inequality
    Period 4
    Period 5
    Period 6
    30

    Collaborate in project groups to develop predictive models for real-world challenges using data science. Navigate the entire data science lifecycle, reflecting on decisions and engaging stakeholders. Consider the ethical implications of AI applications and address bias concerns. Create presentations, demos, and reports showcasing responsible AI systems, and reflect on personal growth and project outcomes.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Minor / Elective
    Period 1
    Period 2
    Period 3
    30

    During this period no mandatory Computational Social Science courses are scheduled. You are free to follow a minor programme, an internship or student exchange programme of your choice.

  • Capstone: Making social change with digital innovations
    Period 4
    Period 5
    Period 6
    30
Compulsory course
Elective

See more information in the online Course Catalogue.

Copyright: UvA
The subject of this programme and the many possibilities you have with it appeals a lot to me. You really get a chance to make a difference. Read what Sanne tells about this programme
Additional options
  • Honours programme

    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!

  • Exchange

    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).

  • Electives

    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.

  • Internship

    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.

Time distribution and tutoring
  • Time distribution - hours in lectures vs. practicals

    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
  • Teaching methods at Computational Social Science
    • 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.
  • Tutoring during your studies

    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.

Copyright: CSSci
You've never programmed a computer in your life? Don't worry: you will by the time we've finished with you! Lecturer Steve Pickering shares his thoughts on Computational Social Science

Projects

The final products of your group projects can range from policy briefs and manifesto’s to websites, experimental designs or prototypes of other digital tools and interventions. Your individual assignments will include e.g. literature reviews, research proposals, case study reports, essays, simulations and information visualisations. Every semester, both group projects and individual assignments will be graded (50%/50%).

Assignments or group projects may relate to topics such as:

  • nudging towards sustainable behaviour in people’s homes through interface design of electric appliances
  • applying big data to counter aggression among adolescents in particular regions of the world
  • researching issues of blockchain technology, such as users becoming both consumer and producer, in the pursuit of transparency and sustainability in logistics
  • gaining insight into the spread of infectious diseases
  • signalling human rights violations by means of information technology
  • investigating societal possibilities of digital forensics
  • mapping social movements in an age of (digital) surveillance
  • empowering displaced people (refugees) with technology-enabled solutions

See more examples of project challenges.

See more information in the online Course Catalogue

Course materials

Examples are:

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509-514.

Montano DE & Kasprzyk (2002). The theory of reasoned action and the theory of planned behaviour. In Glanz K, Rimer BK, & Lewis FM, Eds. Health Behaviour and Health Education, 67-98.

Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019. (https://serialmentor.com/dataviz/)

Frequently asked questions
  • 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 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.

  • Is there a lot of math involved in the programme?

    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.

  • How will you learn programming and what will you learn exactly?

    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.

  • 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.

  • 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.

    Find out more about group projects

  • 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 (studyadviser-cssci@uva.nl)

  • What does the student population look like?
    • 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.

  • What is the ratio between international and Dutch students?

    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.