Wednesday, November 11, 2015

Winter and Spring Computational Social Science Courses

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.