AØKK08216U Summerschool 2020: Introduction to Social Data Science
The objective of this course is to learn how to analyze, gather and work with mordern 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 tools and methods for 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.
After completing the course the student is expected to be able to:
- Understand use cases for different kinds of data (survey, webbased, experimental, administrative, etc.) to answer various questions in the social sciences.
- Account for benefits and challenges of working with different kinds of social data.
- Identify and account for strengths and weaknesses of linear statistical prediction algorithms and estimate these models in practice.
- Discuss ethical challenges related to the use of different types of data.
- Discuss how prediction tools relate to existing empirical tools within social sciences such as linear regression for inference.
- Program in basic Pythion, write and debug code.
- Use data manipulation and data visualization to clean, transform, scrape, merge, visualize and analyze social data.
- Generate new data by collecting and scraping web pages (import and export data from numerous sources) and work with data APIs.
- Apply and interpret machine learning algorithms and models in practice.
- Conceptualize and execute basic projects in social data science
- Independently master and implement computational methods and methods for working with social and behavioral data in the social science literature.
- Present modern data science methods needed for working with computational social science and social data in practice.
The main textbooks are:
- Python for Data Analysis, 2nd ed. (2017) by Wes McKinney
- Python Machine Learning, 2nd ed. (2017) by Sebastian Raschka & Vahid Mirjalili
- Big by Bit - Social research in the digital age by Matthew J. Salganik
A comprehensive reading list as well as detailed information about the course will be available on the course website soon. For last year’s reading list see:
This course is available to students and practitioners who are interested in social data science.
Because the course builds on a wide range of techniques, we do not have any hard requirements,but students are expected to have an interest in at least one of the following: Statistics, econometrics, linear algebra and a scripting language (we will focus on Python in this course).
The third week of the summer school will consist of peerfeedback, guidance and project writing.
Note: Due to the Corona crisis, the lectures and exercises may be conducted online or part online/part fysically at campus. Please consult Absalon to be informed of the teaching, schedule and changes.
Week 1 and 2:
- 3 hours lecturing, 9 AM to 12 noon
- 3 hours of exercise 1 PM- 4 PM (13-16)
- The students participate in peer feedback. Monday to Wednesday the students can groupevise participate in meetings with the TAs for guidance of the project.
To se the course description of the summer school 2021 please go to
Please note that it is the student´s own responsibility to constantly be aware of and search for information about the study, teaching, schedule, exam etc. through the study pages, the course description, the digital exam portal, Absalon, KUnet, myUCPH app, curriculum etc.
- Class Instruction
- Project work
The students receive:
- Written feedback from assignments (correction and solution).
- Written feedback from responses to quizzes.
- Oral feedback and supervision sessions by TAs.
- Feedback by their peers on the project assignment.
For gæste- og enkelfagsstuderende: Tilmelding via Uddannelse i Økonomi.
- 7,5 ECTS
- Type of assessment
- Written assignment, 7 daysThe exam is a project paper. The project can be written individually or in groups of 3 to 4 participants. The students can give peer feedback to the project assignment of each other.
Please be aware of the rules for co-writing assignments for the groups as stated in the curriculum. As well as the plagiarism rules must be complied. The project paper must be written in English.
The groups are randomly assigned at the beginning of the course.
- Exam registration requirements
Full participation at the 3 weeks of the summerschool is mandatory and the student must actively participate in all activities.
Students are expected to complete at least 2 out of 3 mandatory assignments.
- All aids allowed
for the project.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
For the summerschool 2020:
The project paper must be uploaded to Digital Exam no later than
August 29, 2020 at 10 AM
Note: In special cases, the exam date can be changed to another day and time.
The written reexam takes place:
In December 2020 - January 2021
Note:If only few students register for the written re-exam, the re-exam might change to a 20 minutes oral examination with 20 minutes preparation time.
Written aids allowed during the preparation time. No aids allowed during the examination.
If changed to an oral exam, the exam date, time and place might change as well, which will be informed by KU e-mail.
In Digital Exam early December.
Criteria for exam assesment
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.
To pass the exam, the student must be able to demonstrate a performance meeting the minimum requirements for acceptance of the relevant material and of the knowledge, skills and competencies listed in the learning outcomes.
- Course code
- 7,5 ECTS
- Full Degree MasterBachelor
- 1 semester
- Summer 2020:
10th to 29th of August, 2020
Go to: "Remarks" and "Exam".
- Course is also available as continuing and professional education
- Study board
- Department of Economics, Study Council
- Department of Economics
- Faculty of Social Sciences
- Andreas Bjerre-Nielsen (3-66677345787469667833707a336970)
- Nicklas Johansen (2-726e447773686577326f7932686f)
- Terne Sasha Thorn Jakobsen (11-796a77736a33796d74777345787469667833707a336970)
See "Course Coordinators"
- Johan Snorre Engberg Ralund
- Ulf Aslak Jensen