Topics in Social Data Science
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 computational methods.
In this course, we build on the introductory course in social data science (AØKK08216U). We introduce students to three new essential data structures and teach state of the art methods for applying data science and machine learning techniques. We do this by practical examples and provide students with hands-on experience. We also build on the machine learning techniques from the introductory course. We introduce advanced techniques including ensemble learning and deep learning. We discuss how social science leverage these tools and the increasing role they might play.
The first canonical data structure we introduce is networks and relational data. Networks are essential for representing systems of interaction such as information transmission, social behavior as well as for risk in the interbank markets. We then introduce spatial data, for representing locations, shapes, and boundaries within the built environment, as well as mobility traces. These data increasingly play a role in sociology, economics and political science. Finally, we cover text as data, which is unarguably the most abundantly and readily available data source in the form of news articles, speeches, forum threads, social media posts, encyclopedia, etc.
MSc programme in Economics – elective course.
Bacheloruddannelsen i økonomi – valgfag på 3. år
The Danish BSc programme in Economics - elective at the 3rd year
The PhD Programme in Economics at the Department of Economics - elective course with resarch module (PhD students must contact the study administration and the lecturer in order to write the research assignment)
After completing the course, the student should be able to:
Account for the structure of complex networks and understand modeling of social relations based on network statistics like node degree and centrality measures.
Understand fundamental concepts in machine learning: model generalization, overfitting, loss functions, the bias variance trade-off and cross-validation.
Account for various learning strategies, algorithms as well as approaches: clustering and unsupervised learning, supervised learning, semi-supervised learning, transfer learning, multi-task learning.
Know spatial data structures and shapes including points, lines and polygons and account for the choice of coordinate system.
Understand the potential of different representations of text: structured and unstructured, graph-based, and latent representations.
Comprehend how network, spatial and text data as well as machine learning can be applied in the social sciences.
Apply fundamental machine learning tools, including model selection, hyperparameter search and robust model validation. We specifically require an ability to estimate work with ensemble learning and artificial neural networks.
Extract reliable information from text data using supervised learning and techniques from natural language processing.
Structure spatial data for analysis by manipulating shapes, compute local network statistics and spatially combining various sources.
Compute network measures including centrality, clustering, sorting as well as contagions effects.
Integrate theoretical and applied knowledge within the field of Data Science and formulate powerful research questions given an interesting dataset.
Communicate results using comprehensive statistics and modern visualization methods in particular plotting new data types.
Critically evaluate the implications of results, taking into account model limitations and biases, and systematic noise introduced by data collection and sampling methods.
Lectures. Main work will be exercise individually and in groups which will focus on applying methods.
- Bishop, Christopher: Pattern Recognition and Machine Learning. Web book available free at https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book. Spring Publishing, 2006.
- Raschka, Sebastian, and Vahid Mirjalili. Python for Machine Learning, 2nd Ed. Packt Publishing, 2017.
- Barabási, Albert-László. Network science. Web book avaialable free at http://barabasi.com/networksciencebook/. Cambridge university press, 2016.
- Gimond, Manuel. Intro to GIS and Spatial Analysis. Web book available free at https://mgimond.github.io/Spatial/index.html. Preprint, 2017.
- Jurafsky, Dan, and James H. Martin. Speech and language processing. Vol. 3. London: Pearson, 2014.
(The list is changed 21-1-2019)
It is strongly recommended to have followed the course Social
Data Science or or a similar data science course, e.g. R for Data
Analysis or Political Data Science. Linear algebra is also strongly
All students are expected to have strong skills in Python for data science as we begin the course. These skills should include data structuring (pandas, numpy), visualization skills (matplotlib, Seaborn), collecting data by scraping (requests, regular expressions, BeautifulSoup/Scrapy) and finally machine learning fundamental (sklearn). Note if you come from an R background you should have no problem making the transition but preparation is required!
2 hours lectures once a week from week 6 to 20 (except holidays)
2 hours exercise classes once a week from week 6/7 to 20/21 (except holidays)
The overall schema for the BA 3rd year and Master courses can be seen at KUnet:
MSc in Economics => "courses and teaching" => "Planning and overview" => "Your timetable"
BA i Økonomi/KA i Økonomi => "Kurser og undervisning" => "Planlægning og overblik" => "Dit skema"
Timetable and venue:
To see the time and location of lecturesplease press the link/links under "Se skema" (See schedule) at the right side of this page (F means Spring).
You can find the similar information English at
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-F20; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”
For foreign students not enrolled: Admission requirements, registration etc: Study Economics.
For gæste- og enkelfagsstuderende: Tilmelding via Uddannelse i Økonomi.
- 7,5 ECTS
- Type of assessment
Written assignment, 3 weeksproject exam. It is allowed to work in groups of 3 to 4 participants. The plagiarism rules must be complied and please be aware of the rules for co-writing assignments.
The project paper must be written in English.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
for the written exam. The exam may be chosen for external censorship by random check.
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
To receive the top grade, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
In particular, the student should in this course be able to independently analyze new data sets using the tools and theories covered in the course.
Single subject courses (day)
- Class Instruction
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
Information about admission and tuition fee: Master and Exchange Programme, credit students and guest students (Open University)
Go to "Remarks".
Exam and re-sits: Go to "Exam".
- Department of Economics, Study Council
- Department of Economics
- Faculty of Social Sciences
- Andreas Bjerre-Nielsen (4-68756975476c6a767535727c356b72)
- David Dreyer Lassen (19-6663786b66306674677b6774306e63757567704267657170306d7730666d)
- Snorre Ralund (4-717a6c79476c6a767535727c356b72)
- Ulf Aslak (3-7b6770466b69757434717b346a71)
Lectures: See ‘Course responsibles’
Exercise class: Kristian Urup Olesen Larsen
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