DSC 480 Social Network Analysis
Social Network Analysis is an exciting field that studies the impact of social networks on many areas including business, health, and the social sciences. When measuring the behavior of individuals using surveys and other data sources it is the attributes that are focussed on. Individuals are regressed, clustered, and measured by these attributes. Social Network Analysis instead focusses on the connections between individuals. Instead of clusters, networks of these connections are studied to understand the reasons why individuals connect.
Uses of social network analysis have helped prevent terrorism, predict viral messaging and diffusion, in marketing, for human analytics, and many other areas. This is a fast growing area that in previous years has been largely taught in PhD seminars. This class will emphasize the data science relationships and how it fits in with data centric approaches. The eventual goal is for the students to be able to gather social network data, process it, and develop predictions based on it. ERGM, which is the Social Network Analysis equivalent of regression, will be used as will other static network measures.
Grading is based:
50% Assessments on 2 projects for midterm and final
In general work that is above expectations gets an A, work that meets expectations gets an A-, and work that has deficiencies gets B+ or below depending upon the level of deficiencies. If work is not turned in then this is graded to F. Work is expected to be on time, however if there is something preventing this contact me prior to the due date.
An understanding of linear regression at the level of DSC 423, SOC 412, or PSY 411. Parts of the course will build upon these concepts. A basic familiarity with R is also needed. There are many resources for R that are free on the web and a book is recommended for the class. This is not a programming class, however loading files, putting them into data frames, adding libraries, running commands both interactive and through scripts will be needed. Other R commands that come from the Tidyverse will be introduced in class based on need.
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course
content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The
evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely
separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation.
Students complete the evaluation online in CampusConnect.
This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you
have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.
All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that
you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002