363-1091-00L  Social Data Science

SemesterSpring Semester 2020
LecturersD. Garcia Becerra
Periodicityyearly recurring course
Language of instructionEnglish


363-1091-00 GSocial Data Science
Block course: 10.02.2020-14.02.2020
30s hrs
10.02.09:15-16:00HG E 1.2 »
11.02.09:15-16:00HG E 1.2 »
12.02.09:15-16:00HG E 1.2 »
13.02.09:15-16:00HG E 1.2 »
14.02.09:15-16:00HG E 1.2 »
D. Garcia Becerra

Catalogue data

AbstractSocial Data Science is introduced as a set of techniques to analyze human behavior and social interaction through digital traces.
The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective.
ObjectiveA successful participant of this course will be able to
- understand a wide variety of techniques to retrieve digital trace data from online data sources
- store, process, and summarize online data for quantitative analysis
- perform statistical analyses to test hypotheses, derive insights, and formulate predictions
- implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization
- interpret the results of data analysis with respect to theoretical and testable principles of human behavior
- understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity
ContentSocial Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data.
SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales.
The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data.
Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior.
Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression.
These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior.

The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects.

The course will cover various examples of the application of SDS:
- Search trends to measure information seeking
- Popularity and social impact
- Evaluation of sentiment analysis techniques
- Quantitative analysis of emotions and social media sharing
- Twitter social network analysis

The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective.
The block course contains lectures and exercise sessions during the morning and afternoon of five days.
Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures.
Lecture notesThe lecture slides will be available on the Moodle platform, for registered students only.
LiteratureSee handouts. Specific literature is provided for download, for registered students only.
Prerequisites / NoticeParticipants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective.

Prior knowledge of advanced R, information retrieval, or information systems is not necessary.

Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform guided exercises.

Course evaluation is based on short quizzes at the end of each day from Monday to Thursday (5% each for a total of 20%), a 45-minute exam during the last session (30%), and on the grade of a final project report (50%). Final projects can be done individually or in pairs. Final projects will be composed of a text report (max 6 pages) and the R code to generate the results. The deadline to deliver the final project will be at the end of the Easter break, approximately 2 months after the course.

The course takes place between Feb 10th and Feb 14th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersD. Garcia Becerra
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Admission requirementAttendance to the datathon and submission of the final project are both mandatory.
Additional information on mode of examinationCourse evaluation is based on the project developed in the last session datathon (50%) and on the final project report (50%). Attendance to the datathon and submission of the final project are both mandatory.

Learning materials

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Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Data Science MasterInterdisciplinary ElectivesWInformation
Management, Technology and Economics MasterRecommended Elective CoursesWInformation
Science, Technology, and Policy MasterData and Computer ScienceWInformation