SOCY709P: Advanced
Special Topics in Data Analysis; Network Analysis
SPRING 2016
Wednesdays 3:30 to 6pm
Christina Prell (cprell@umd.edu)
Office hours: TBA
This course is intended as a survey of the theory and methods
pertaining to social networks. Class time will be devoted to learning
principles, theoretical perspectives, and appropriate software packages (mainly
those in R) for analyzing social network data. The readings are a combination
of introductory-level material, classic, scholarly readings in the field, and
empirical studies that apply social network analytic techniques to topics
relevant to sociology and the social sciences as a whole. The first weeks are
structured around readings, group discussion, and lab (e.g. going through
example scripts in R). The last few weeks are geared more towards students’
individual projects, culminating in small presentations (similar to conference
paper presentations) on a topic of your choosing, and a final paper/project,
again shaped according to your own needs/interests. Please note: this syllabus
is subject to change, based on classroom discussion and needs.
PSYC798W, R Programming for
the Behavioral Sciences
Meets January 4, 2016-
January 15, 2016
MTuWThF 9am – 12pm
BPS 1236
Scott Jackson (scottrj@umd.edu)
R is a programming language and
environment designed for statistical analysis. It is free and open-source, and
it includes integration with thousands of cutting-edge packages contributed by
users from around the world. It has become a de facto standard
and lingua franca for statistical analysis. It is an incredibly
powerful tool for data analysis and visualization, and thus an indispensable
tool for any kind of quantitative work in the behavioral and social sciences.
However, because many (if not most) students and researchers in these fields
are not otherwise trained in programming techniques, learning R can be
difficult, and poor understanding of programming concepts and techniques can
create problems in analysis and reporting of results.
This course aims to give you a
foundation in programming, in order to facilitate future work with R. It is not
a stats course, though we will probably discuss some statistics in passing. The
focus of the course is building concepts, skills, and habits to make you a
better programmer, in order to get the most out of R. The class involves
lot of hands-on practice and feedback and plenty of opportunities for
questions, and it requires you to get your hands dirty with a data set
(preferably one of your own). The class should accommodate a range of
experience levels, from completely novice users to more experienced users that
feel like they could use some “polish” to their skills.
