Big Data, Small Data

2019-2020

Course Objective

By the end of this course, you are able to
1. understand the nature of big data and the role they play in society
and social research in different disciplines;
2. critically reflect upon on the social and ethical aspects of the
use of big data in society and in social science;
3. access a variety of big data sources, to query these data and to
organize, analyze and visualize parts of the data using R;
4. apply both qualitative and quantitative methods to big data;
5. present research results based on an analysis of big data in a
reproducible way

Course Content

Today’s digital and information-dense society produces a massive amount
of data. Much of this data is generated by or related to human behavior
and can inform social scientists about societal dynamics. Examples
include social media data, parliamentary minutes, email collections or
collections of stories that have been made available in a digital
format. In this course, students learn about such digital trace data,
that are often voluminous, unstructured, and/or embedded in complex data
structures – we refer to such data as Big Data. Students learn about how
Big Data differs from data generated by traditional social science
research methods and the opportunities and challenges that Big Data pose
in present day society. They are introduced to R, a programming language
which they will use to gather and link data, and to make sense of these
data. Students learn about ways to analyze data derived from social
media such as forums or social network sites by using both computational
and interpretive approaches. The latter are key to Small Data, data with
meaning to individual citizens.

Teaching Methods

During most weeks of this course, we meet three times a week a week in
interactive workshops and practical seminars. Most meetings last
approximately 1 hours and 45 minutes with a 15-minute break. During the
workshops, students present the readings and discuss the assignments
with each other and the instructors. During the seminars, students
conduct exercises in the programming language R, in R packages that help
to organize, clean, analyze and visualize data, and in qualitative
skills to analyze big data.

Method of Assessment

Assessment will take place through presentations, exercises, and written
assignments.

Presentations. For each meeting, one student (or more, depending on the
number of students) prepares a 5-minute oral presentation on the
readings for that meeting. At the first meeting, a schedule is created
for the presentations. The presentation is graded (10% of the final
grade).

Exercises. During and following the practical seminars, students work on
(ungraded) exercises that aim to train them in computational and
qualitative skills.

Individual midterm essay. Big data challenges in governance, care and
welfare, interconnectedness or diversity (20% of final grade). In this
essay, the students reflect on issues related to the use of social media
data in one of the four ISR topics. In the 2000 word essay, attention is
paid to data quality, and social and ethical issues.

Individual midterm exercise. Handling data in R (20% of final grade)

Final written assignment. Social media analysis on a topic related to
governance, care and welfare, interconnectedness or diversity (two
students work together on one paper; 40 hours; 50% of final grade). In
this assignment, students will formulate a research question related to
one of the ISR topics for which social media data can be used to provide
an answer. They will collect data from a social media website (e.g.
Facebook, Twitter, Reddit) and will organize and clean the data. Data
analysis will be done using both qualitative and quantitative analyses
in order to formulate an answer to the research question. The assignment
will result in a written paper and the results will be presented in a
poster session.

Literature

Salganik, M. J. (2017). Bit by bit: Social research in the digital age.
Princeton University Press. Free online at
http://www.bitbybitbook.com/en/1st-ed/preface/ [358 pages]
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy,
transform, visualize, and model data. O'Reilly Media, Inc. Free online
at http://r4ds.had.co.nz. Ch. 1-16, 21 [324 pages]

Target Audience

Students Research Master Societal Resilience

General Information

Course Code S_BDSD
Credits 9 EC
Period P1+2
Course Level 500
Language of Tuition English
Faculty Faculty of Social Sciences
Course Coordinator prof. dr. P. Kerkhof
Examiner prof. dr. P. Kerkhof
Teaching Staff prof. dr. P. Kerkhof
dr. C. Moser
dr. M.A.C.G. van der Velden

Practical Information

You need to register for this course yourself

Teaching Methods Study Group, Reading