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Spring 2019

Topics in Social Data Science

Course Coordinators: Andreas Bjerre-Nielsen, David Dreyer Lassen, Snorre Ralund, Ulf Aslak Jensen
Level: Full Degree Master, Bachelor, Ph.D.

The objective of this course is to teach students how to leverage the data science toolbox for use in social science. We emphasize the use of new data sources associated with communication, behavior, transactions, etc., which are increasingly available through the web and by collection from the various devices we use. These new sources of structured and unstructured data allow for testing and validation of existing theories in social science as well as development of new ones. Performing these analyses, however, requires an ability to understand and apply methods from the computational sciences. We build on the foundational course in social data science to teach these fundamental skills.

We introduce students to the essentials of data structure and structuring and teach state of the art methods for applying data science and machine learning techniques. We do this by using practical examples and provide students with hands-on experience. We will build on the knowledge from the basic Social Data Science course.

The first canonical data structure we introduce is network and relational data. This data type is ubiquitous when analyzing data from social media, communication on cell phones or data on physical meetings. The second data type is spatial data which includes data on shape and structure of shops, buildings, administrative boundaries, etc. but also includes personal data from GPS on smartphones, cars and much more. The final data type is text data which is present everywhere as documents, online discussions etc. For each of the three datatypes we will teach various tools to work with them in practice.

We teach students a high level of applied machine learning. We will provide an in-depth review of the advantages and disadvantages of standard machine learning techniques, i.e. supervised machine learning (regression, classification) and unsupervised learning. In addition we will teach tools from the frontier of applied social data science that leverages machine learning for causal inference.

The teaching is built around empirical examples: the course aims at developing good practices in data analysis, including thorough exploratory analysis, reliable collection and cleaning of data, visualization skills and statistical sensitivity analysis.

The course will emphasize a complete approach to working with data - from data collection - over data structuring (i.e. parsing, cleaning, transformation, and merging) - to exploratory analysis, and finally reporting of the results.

You can find the course description here.
More information about the course here.

Digital Methods: From Ethnography to Supervised Machine Learning

Course Coordinator: Hjalmar Alexander Bang Carlsen
Level: Full Degree Master, Full Degree Master choice 

It is widely recognized that growth in digital data formats enable new relations between quantitative and qualitative methods of inquiry and analysis, thus posing questions as to how such new complementarities are best exploited for social-scientific and practical purposes.
This course will run through a number of ways in which qualitative and quantitative modes of inquiry complement one another. They will compose a research strategy that ensures both qualitative methods strength in valid interpretation of the meaning and consequence of social actions and quantitative methods strength in generalization and structural analysis.
The course will structured around a mini research project from data collection to analysis. In the data collection part we will combine quantitative sampling theory and qualitative case selection theory to ensure a good sample. For exploring the data we will discuss and use a set of methods meant to map out ones data and display the varying densities and differences in ones data(network analysis, clustering, multidimensional scaling).
These maps work as navigational device to ensure that one digital ethnography gets hold of the relevant variation in ones data. The digital ethnography entails a practice of participant observation in digital spaces (e.g. a Facebook group, a company intranet board) for the purpose of learning the details of the values and motivations characteristic of social interaction in this setting.
The digital ethnography provides the interpretative grounding which insures that the categories and social processes, that in the later stage will be quantitatively assessed, have a hold in the meaning-making practices of the actors themselves. In order to explore, generalize and test large scale patterns we need to translate our ethnographic description into something quantifiable.
We will use supervised machine learning in order to scale our qualitative categorization/coding. The student will learn the pro's and con's of various strategies regarding ones sampling, optimization strategies, the uses of unsupervised methods as input.
Importantly we will pay a lot of attention to biases detection and correction to ensure the reliability and validity of our supervised machine learning model. This categorized dataset can then be used of more or less simple quantitative analysis which together with the digital ethnography will make the mini analysis.
More information about the course here.

The Anthropology of Design

Course Coordinators: Simon Westergaard Lex, Henrik Hvenegaard Mikkelsen
Level: Bachelor, Bachelor choice, Full Degree Master choice

In recent years “design anthropology” has been considered a feature of anthropology that is “exportable” to the world beyond academia. But while being highly praised it often seems unclear what design anthropology is and what it does—and how it might be used. In this course we explore design anthropology as an approach that persistently seeks to push the boundaries of the discipline into adjacent areas.
Bringing together academic scholarship and applied practice the scope is interdisciplinary, collaborative and interventional. The students will explore the merging of design and anthropology in small projects concerning current societal challenges within new tech and sustainable transition. Moreover, the course will open for grander questions, especially concerning the social consequences of these developments. During the course we approach such questions through discussions of both ethnographic accounts as well as anthropological theories.
The course thereby seeks to open up the “black box” of design anthropology by exploring how it is carried out in different settings, by different people and for different purposes. In an equally critical and exploratory manner the course seeks to identify what an anthropology of design might be.
More information about the course here.

Summer School 2019

Social Data Science

Course Coordinators: Andreas Bjerre-Nielsen, David Dreyer Lassen
Level: Full Degree Master, Bachelor

The objective of this course is to learn how to analyze, gather and work with modern quantitative social science data. Increasingly, social data that capture how people behave and interact with each other is available online in new, challenging forms and formats. This opens up the possibility of gathering large amounts of interesting data, to investigate existing theories and new phenomena, provided that the analyst has sufficient computer literacy while at the same time being aware of the promises and pitfalls of working with various types of data.
In addition to core computational concepts, the class exercises will focus on the following topics
1. Gathering data: Learning how to collect and scrape data from websites as well as working with  APIs.
2. Data manipulation tools: Learning how to go from unstructured data to a dataset ready for analysis. This includes to import, preprocess, transform and merge data from various sources.
3. Visualization tools: Learning best practices for visualizing data in different steps of a data analysis. Participants will learn how to visualize raw data as well as effective tools for communicating results from statistical models for broader audiences.
4. Prediction tools: Covering key implementations of statistical learning algorithms and participants will learn how to apply and interpret these models in practice.
You can find the course description here.
More information about the course here.

Data Governance: Ethics, Law and Politics

Course Coordinator: Kristoffer Albris, Morten Axel PedersenDavid Dreyer Lassen
Level: Bachelor, Bachelor choice, Full Degree Master choice 

The age of social big data brings with it a range of ethical, legal and political issues. From the ethics of protecting individual online privacy, to the legal frameworks regulating internet giants such as Facebook and Google, new data governance issues surface at a rapid pace. This course provides students with an introduction to key legislative, political and ethical principles and debates from the perspectives of anthropology, law, sociology, political science, and related disciplines, concerning the governance of data, needed for a range of analysis and management positions across private, public and non-profit organizations.
Data governance concerns the overall management of the availability, usability, integrity and security of data used in private, public and non-profit organizations. Comprehensive data governance addresses issues of data stewardship, ownership, compliance, privacy, data risks, data sensitivity and data sharing, including how such issues exist between different entities within the same organization. It involves thinking through issues such as: What do new forms of data-driven surveillance mean for relations between citizens, businesses and nation states, and how are new legal issues such as the legal basis for decision support systems and algorithmic decisions in public and private organizations addressed within current European legislation?
Students will be taught how to develop and implement ethically and politically informed procedures and infrastructures for organizing, managing and maintaining data and data products in public and private organizations. The course also introduces the most recent ethical and social-scientific models of data governance, including organizational models and risk assessments, and asks students to apply them to a real-world case of problem solving.
Casework takes students through the main phases of data governance analysis and practice: identification of a data-related problem and its internal and external stakeholders; analysis of how legal, technical-infrastructural and social-organizational components of the problem interrelate; pre-screening of possible solutions, including their respective risks; and final proposal and pilot check of a new data governance scheme expected to be robust in the face of foreseeable near- and mid-term challenges. By drawing on cutting-edge research in anthropology, law, sociology, and related disciplines, the students will also be able to contenxtualize and situate the case-based work within existing scientific debates concerning data governance and ethics.
The course is organized into three parts. First, we begin with an introduction to what can be done with social big data under current Danish and EU laws. This is followed by a consideration and discussion of what should (and should not) be done in more political and ethical terms. And finally, the course will discuss what could be done in terms of governance in different sectors of public administration (health, education, etc.), in the private business sector and in the non-profit sector.
More information about the course here.

Re-tooling Social Analysis: Behaviors, Networks, Ideas in the digital age

Course Coordinator: Hjalmar Alexander Bang Carlsen
Level: Bachelor, Bachelor choice, Full Degree Master, Full Degree Master choice 

This course equips students with the analytic skills and reflexive capacities needed to engage critically but productively with various new 'device-aware' styles of social analysis assisted by digital and computational means. It does so, firstly, by way of reading paradigmatic analyses of the nature of social behaviors, networks and ideas, focusing both on classical concepts and contemporary research frontiers.
Examples are drawn from across all the social-science disciplines, and core interdisciplinary convergences are identified. Second, the course takes students through all the methodological steps of social research design, analysis and interpretation, tying these steps to practical examples and to students' own projects (from other courses). Here, key initial questions include: what are the implications of working with different data types (static vs. dynamic; broad vs. deep); how to think about and practically handle data biases stemming from digital platforms and devices (noise, bots etc.); how to build ethical considerations in from the start of data harvesting (digital research ethics)?
In a next step, students are introduced to key methodological traditions often underlying the analysis of behaviors, networks and ideas, respectively (causal-experimental; pattern search; meaning-oriented), as well as to ways of working across them using various digital data sources as well as combining with other sources (including both quantitative and qualitative). In a final step, students learn how to think critically about the interpretation of their social data analyses, including issues of internal and external validity, representativeness and generalizability, as well as analytical induction and concept work.
Rounding up, thirdly, students are introduced to frameworks for thinking about the changing place of social research in digital societies, including the possibilities and challenges opened up by greater interdisciplinary collaboration as well as new types of academia-industry-government partnerships.
More information about the course here.

Previous courses

Innovation og co-design (in Danish)

Course Coordinator: Simon Westergaard Lex
Level: Bachelor, Bachelor choice, Full Degree Master choice

Innovation og design er på samfundets agenda, og de to fænomener og begreber bliver diskuteret og praktiseret i politiske, akademiske og erhvervsmæssige sammenhænge. Innovation og design har mange former og definitioner, og begrebernes flertydighed udsætter dem for både lovprisning og modstand.
Men hvad består disse fænomener af, når vi dykker ned i dagligdagens arbejde med innovation og design? Kan vi styre kreativitet og forandring? Hvordan influerer sociale magtkampe på den gode idés overlevelsesmuligheder? Hvad betyder ’design thinking’? Og hvad kan antropologer byde ind med, når innovation og design er på agendaen?
For at finde svar på disse og andre spørgsmål vil dette kursus i samarbejde med eksterne aktører udfolde innovation og design i et praktisk udviklingsforløb, som sætter fokus på energi og bæredygtighed.
Det praktiske fokus vil løbende blive komplementeret af refleksioner, analyser og diskussioner, som bunder i antropologiske metoder og teorier. Begreber som kurset blandt andet fokuserer på er: Innovation, design, organisation, kreativitet og energi.
Mere information om kurset her.

KOVIKO - Kortlægning af videnskabelige kontroverser (in Danish) 

Course Coordinators: Ayo Wahlberg, Anders Blok
Level: Bachelor, Bachelor choice, Full Degree Master choice

I dag ser vi, at en række centrale samfundsmæssige spørgsmål i stigende grad bygger på kompliceret teknologisk og videnskabelig viden, hvorom eksperterne ofte er uenige. Det gælder f.eks. i spørgsmålet om genmanipulerede fødevarer, digitale formater, energiforsyning, klimatilpasning, vurdering af miljørisici, stamcelleforskning, vacciner, etc. Men tillige sociale og kulturelle temaer såsom kulturarv, kommasætning, multikulturalisme, økonomiske prognoser, retsnormer, osv. udgør vidensfelter, hvor eksperter ofte strides.
KOVIKO har til formål at sætte den studerende i stand til at kortlægge, analysere og grafisk visualisere sådanne teknologiske og/eller videnskabelige kontroverser, primært gennem brug af digitale metoder og redskaber (såsom Issue Crawler). Herigennem opnås kompetencer, som er centrale for at navigere i et globalt videns- og risikosamfund.
Mere information om kurset her.