GROUP PRESENTATIONS Introduction to Simulation Tool (%)

Random number generation Fitting a distribution to a data

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Simulation Tool (continued) Model Design

Discussion of various practical process improvement examples using the tool Measurements

Benchmarking

Assignment 2

7 FINAL GROUP PRESENTATIONS

Applicable Policies & Other Information Attendance

The George Washington University Bulletin, Graduate Programs, 2009–2010: "Regular attendance is expected. Students may be dropped from any class for undue absence…. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed."

University Policies Regarding Conduct and Academic Integrity

Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see http://www.gwu.edu/~ntegrity/code.html.

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Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion.

Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at 202-994-8250. For additional information refer to

http://gwired.gwu.edu/dss/.

Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via email, or on Blackboard and students are expected to complete the deliverables

incorporating such clarifications and additions. Thus, students should check email and Blackboard announcements and discussion forums frequently before submitting deliverables.

Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor

individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

Department of Decision Sciences

Course Title: Social Network Analytics Course Name: DNSC ____

Instructor: Shivraj Kanungo Room: Funger 415

Phone: (202) 994-3735 Email: [email protected].

Course description

This course introduces the concepts, applications, and methods of understanding the dynamics of networks, with a particular focus on social network analysis. The term

“social networks” has become a buzzword in popular culture. People now routinely talk about “networking” to advance their careers, that we are connected by “six degrees of separation,” and that it’s “who we know” rather than “what we know” that matters. Upon taking this course students will be able to analyze and describe real networks (power grids, WWW, social networks, etc.) as well as relevant phenomena such as disease propagation, search, organizational performance, social power, and the diffusion of innovations. Students will learn how to frame the research question, collect the data, run the analysis, and interpret the results. In addition, they will learn how to design and evaluate models of diverse networks to improve their understanding of the underlying principles.

Prerequisites

None; however, some exposure to basic math is useful.

Course objectives

This is a course in social network analysis and methods. While the course places emphasis on the theories associated with networks a working knowledge using

appropriate methods and tools is equally important. Over the course of the semester you will be expected to develop the following competencies:

1. Familiarity and fluency in the language of social network analysis (SNA)

2. Communicating social network concepts and methods to specialists and laypersons 3. Proficiency in organizational social network analysis including data collection,

analysis, and reporting

4. Working knowledge of one software tool used in network analysis

Learning objectives

Students who complete this course will be able to

1. Recognize a problem that lends itself to the SNA approach

2. Identify and use different formats for network data and choose the appropriate one 3. Relate network and node metrics to real world phenomena like social capital and

boundary spanning individuals.

4. Obtain large scale data from well-known networks like Twitter and Facebook.

5. Interpret and synthesize the meaning of the results with respect to a question, goal, or task.

Course delivery

Each class session will include a lecture component and, in some classes, instructor-led case studies. Students will use NodeXL (and R). Every class will be followed by an assignment that will be used to reinforce the concepts and tool-based skills covered in class.

Course material

1. All course material will be provided. It will be provided in the form of slides, tutorials, and program files. The slides and tutorials will be available as pdf files.

2. The following book will be used as the required text:

• Hansen, Derek, Shneiderman, Ben and Smith, Marc A. (2010). Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Burlington:

Elsevier Science. This is available as an electronic text book from with the Gelman library system:

http://surveyor.gelman.gwu.edu/?q=Analyzing%20Social%20Medi a%20Networks%20with%20NodeXL

Software used 1. NodeXL

2. R (primarily the sna package and igraph for visualization)

Grading

Component Weight

Individual Assignments (6) 30

Final Exam 30

Group Project 40

Assignments

Six individual assignments, each worth 5% of the final grade, are designed to reinforce learning.

Final exam

The final exam will be comprehensive in coverage and will be held after all classes are completed.

Course calendar

Session Date Topic Assignment

1 Network perspectives; types of networks;

Network analysis examples; Chapter 1 and 2 Assignment 1 2 Mathematical foundations; graph theory; types of

graphs; Visualizing networks; Assignment 2 3 Network metrics; node level metrics and network

level metrics; dyads, cliques and subgroups Assignment 3 4 Data collection; collecting data from the internet;

Twitter and Facebook data pipes. Assignment 4 5 Cohesive Sub-Groups and teams; Using social

network data in hierarchical linear models Assignment 5 6 Analyzing Ego Networks; Brokerage & social

capital Assignment 6

7 Structural Equivalence and Block Modeling;

testing hypotheses

Other information

1. Students can expect to spend at least 5 hours per week outside the classroom. This could vary depending on their prior preparation and background.

2. Students are expected to do their assigned readings before class

3. Assignments are to be turned in on the day they are due. Late assignments will not be accepted.

4. It is important for all students to be familiar with and adhere to the GW Code of Academic Integrity (http://www.gwu.edu/~ntegrity/code.html).

Decision Sciences Department Business Analytics Program