Data Science & Society

The course Introduction to Data Science and Society introduces students to the complex relationship between data science, artificial intelligence, and the societies in which these technologies are developed and used. Using generative AI as a central contemporary case, the course examines how data- and AI-driven technologies influence education, work, creativity, information, relationships, and public life. Students engage critically with questions concerning bias, misinformation and deepfakes, authorship and originality, AI-mediated relationships, persuasion, responsibility, and the future of human practices such as writing. Throughout the course, these topics are approached from multiple societal, ethical, political, cultural, and academic perspectives, encouraging students to question not only what AI technologies can do, but also how they shape, and are shaped by, human values, institutions, and practices.

The course has a strong emphasis on critical thinking, evidence-based argumentation, communication, and reflection. Students learn to identify and evaluate sources, examine competing perspectives, construct and defend arguments, and reflect on the development of their own understanding. These skills are developed through discussion-based classes and a range of applied and creative activities, including a presentation, a Learning Portfolio, future-scenario exercises, a simulation of a United Nations Convention on Artificial Intelligence, and the AI Love You art project and exhibition. Together, these activities invite students to analyse complex societal questions from different stakeholder perspectives while experimenting with different ways of communicating and reflecting on knowledge, from academic presentations and debate to visual and creative expression.

Programming for Data Science is a gentle introduction to programming concepts that are paramount to Data Science. Students learn how to read and understand existing code, as well as to write and debug their own code. Basic computing algorithms are introduced, implemented, and their computational cost is being assessed. Essential programming concepts like object-oriented programming, and primitive and compound data types are also introduced.

For this course, you will be learning the Python programming language. This is not the only programming language for Data Scientists, but it has grown to become the most popular, and for a few good reasons: easiness to read programs, fast program drafting, extensive (and growing) number of libraries, wrappers around code written in other languages, and wide support by fellow developers.

This course introduces students to key concepts and methods in Science and Technology Studies, with a particular focus on how to research, analyze, and communicate complex socio-technical issues. Working in groups, students investigate a case study drawn from contemporary society, exploring it through a series of weekly topics ranging from platform politics to actor-network theory and multimodal ethnography. Each week's workshop builds directly toward a collective essay, combining scholarly research with critical analysis and visual elements. Alongside this thematic exploration, the course functions as an introduction to scientific writing, teaching students how to gather and organize references, develop well-structured arguments, and present findings effectively. By the end of the course, students will have acquired practical skills in collaborative research, academic communication, and critical reflection on the role of technology in society.

This course introduces the main departure points of the Regulation, Governance (R&G) and Innovation stream. Overarching questions include the following: How does innovation occur? What are the consequences of particular types of innovations? What roles do state and non-state actors play in the processes and implications of innovation? How does datafication and digitalization shape & reshape innovation processes?

This course introduces you to the fascinating world of artificial intelligence, focusing on a wide range of machine learning models and algorithms. These models are capable of performing tasks such as target classification, attribute clustering, and trend forecasting. Machine learning applications are increasingly integral to our digital lives, making this field more relevant than ever.

This course offers an introduction to data visualization for students in the Data Science and Society bachelor and the Minor in Geospatial Data Science. It explores how visual representations can make complex data more understandable, helping students communicate their findings across disciplines and to non-specialist audiences. The course combines critical thinking with practical work, inviting students to experiment with digital tools and reflect on the choices they make when designing visual narratives.

Putting Human Dignity at the centre, students will explore why the respect, protection and promotion of human rights is crucial to regulate, guide and limit the use of data. This course explores the root of modern human rights law - Human Dignity - from a philosophical and socio-legal perspective. It has a particular focus on the modern international human rights framework which emerged after World War II. The course will focus on the European and international layers of the legal framework. To make abstract concepts and rules more concrete the right to privacy will be used as a lens. Finally, social developments that result from the mass-adoption of emerging technologies such as artificial intelligence or distributed ledger-technology will be studied.

In today's digital society, a critical understanding of data is essential for all graduates and professionals. Knowledge about data is often split into areas of expertise, so that processes that span algorithms, servers, users and institutions are rarely discussed coherently and accessibly. The two courses in STS in this programme provide a coherent and integrative approach to data.

Data Science uses statistics as a practical tool to solve problems. Such problems include how to use statistics to be able to compare, estimate, predict, and make causal inferences. This course is designed to introduce you to such problems and to equip you with some of the tools available and used to address them. The predominant focus of the course is on practical aspects and problem-solving strategies

This course aims to introduce database systems, which are tools for efficiently organising and retrieving large amounts of diverse data. The first part of the course focuses primarily on the organisation and storage of structured data in relational databases using Structured Query Language (SQL). Fundamental concepts associated with the relational data model and the SQL language will be discussed. The practice sessions will train students to apply SQL for the creation, access, and navigation of relational databases. Additionally, the online data sources and methods for accessing them will be discussed.

The second part of the course emphasises the organisation and storage of non-relational data, a relatively new yet highly versatile data structure. In the era of big data, non-relational databases enable the storage of vast quantities of information in a flexible and scalable manner. The lectures will provide a theoretical foundation for non-relational data structures, while the practical sessions will teach the use of MongoDB, a popular non-relational database system, to store, navigate, access, and edit remotely located non-relational databases.

This course covers different forms of data sources relevant for Data Scientists and builds on the course 'Visualizing Data'. Students revisit text-based formats such as JSON and CSV. From this basis they delve in more details about relational databases. Next, students are introduced to non-relational databases and distributed data storages (Apache Hadoop). Students deepen their understanding of SQL further and learn about various dialects (including Apache HIVE). Querying data storages might yield vast amounts of data that needs to be processed in limited time. Intuitively, parallel computing might be a solution. In this context, students learn about concepts and shortcomings of distributed computation models related to MapReduce, resilient distributed data sets, stream data processing and graph data processing. Weekly exercises foster the grip on those concepts. Apache Spark, a unified state-of-the-art computing engine for parallel processing on computer clusters, is the base for hands on training.

This course addresses two overarching questions: how can regulation and governance promote responsible innovation? How can responsible innovation promote responsible governance and regulation? Building on the courses Human Rights in the Digital Age and Science and Technology Studies 1: Data Creation and Circulation, this course explores the generation, transmission and impacts of responsible data. Topics to be discussed in this context will include how values such as dignity, privacy, and autonomy can be encoded into data in ways that avoid undesirable processes and outcomes like colonialism, discrimination and exclusion. How can effects and negative impacts on the environment and society remain embedded - rather than externalized - from innovation processes?

How can we understand and solve social problems using data? This course provides an introduction to identifying data analysis approaches to better understand urgent social problems of our time. To do so, you learn how to identify and operationalize projects. We take a critical look at the potential social biases and discussing strengths and weaknesses of various data science approaches.

First, the course introduces a real-world, business-oriented perspective on applying the data science techniques and methods that students have learned in prerequisite courses. Through both theoretical and practical approaches, students will learn to translate business problems in terms of data science questions. They will then identify suitable data science methods and apply them in practical sessions.

The second objective is to introduce students to new data science methods and tools for advanced business analytics. These include, but are not limited to, business forecasting models, customer segmentation through clustering, unsupervised learning for recommendation systems, rapid machine learning libraries, new classification and profiling methods, etc.

Ultimately, the course provides aspiring data scientists with the tools needed to understand and innovate within the business environments they will encounter in their future career.

In this course students consider how human rights and associated philosophical/ethical principles can be embedded in data use practices, while taking the interaction with and impact on society into account. Students learn about the essence of human rights (such as privacy) and ethical principles (such as non-discrimination, fairness, and justice) in order to explore, analyse, discuss and evaluate innovative and responsible data practices.

This course is a part of the Bachelor programme in Data Science & Society, which combines computer science, statistics, and social science to equip students with interdisciplinary skills. "Visual Rhetoric" complements this approach by focusing on the principles of visual design and their application in various forms of communication. Over the span of eight thematic weeks, students will explore topics such as typography, grid layout, point, line, and plane, the use of colors, and transparency techniques. Adobe Illustrator serves as the primary tool for hands-on exercises, bridging theory and practice.

This course focuses on the intersections of sustainability, governance, and innovation, examining how we can effectively foster sustainable development in a world increasingly defined by complex data flows and technological advancements. We will explore how governance structures, both formal and informal, impact sustainability efforts and how innovative solutions can address global challenges like climate change, resource depletion, and social inequalities.

A Field Project (FP) is a research concept which involves a co-creation process between students, researchers, and public or private organisations. This course is special, since FPs are a central element of the transdisciplinary component of DSS and within the faculty Campus Fryslân. FPs offer opportunities to develop new ideas, products, services and business models to serve as a solution and enable societal challenge. In addition, the FP is the only course in the programme besides the Bachelor thesis that runs for an entire semester.

For students, FPs are a way to get acquainted with the professional field and apply theoretical knowledge in practice. For host organisations in the private and the public sectors, FPs are an opportunity to work with young talents on societally relevant questions and challenges that concern the respective organisation.

We collaborate with regional, national and international institutions on FPs

Advanced Programming builds upon the fundamentals which the students have gone through in the earlier programming related courses in the Bachelor, starting with Programming for Data Science in the first year.

This course enables students to deal with different data modalities and perform specialized tasks. Later parts of this course also focus on concepts such as debugging, Object-Oriented Programming, vectorization etc. to enable efficient coding to write less complex and error-free Python programs. Also, concepts such as Web Scraping and file handling allow us to collect data from the internet and process it at a large scale. Finally, the course project enables us to create replicable and shareable code and understand version control and code review processes.

This course is a practical course that trains students in becoming an independent, methodologically sound, critical and rigorous researcher. In the course you focus on all methodological aspects in each step of the research process. From coming up with a topic, to defining data and methods, discussing the strengths and weaknesses of analytical strategies, to discussing the implications and output of your work.

In the final semester of year three, DSS students complete the Bachelor Thesis module of 15 EC.

The thesis is the academic culmination of a student's degree in which each student demonstrates the proficiency of knowledge and skills as developed throughout the degree programme. Each thesis must meet the standards and requirements of semi-independent academic research: students carry out a research project by identifying a topic, formulating a research question with potential sub-questions, perform a literature review, conducting research and presenting the analysis.