Tuesday, 9 July 2013

Introduction to Social Network Analysis (SNA)

Social Network Analysis has existed for a long time, but social media has fundamentally changed the way we do this analysis. Data has become more plentiful and easy to collect, but this has pushed the boundaries of existing techniques. Sociological methods do not easily scale to the size of these networks, but purely statistical methods miss the complex social interactions that take place. This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks. We will learn how to identify influential individuals, track the spread of information through networks, and see how to use these techniques on real problems.

Dr. Jennifer Golbeck will present his course “Introduction to Social Network Analysis (SNA)” online at Statistics.com. For more details please visit at Social Network Analysis.

Who Should Take This Course:
Anyone who wants to learn to analyze social data - people who work in organizations with social media presences that they want to manage and analyze, or those who work on these networks and want to better understand the details of the environment they create.

Course Program:

Course outline: The course is structured as follows
WEEK 1:  Network Analysis Basics
·         Basic Terminology
·         Metrics
·         Visualization

WEEK 2:  The Social Network
·         Tie strength
·         Trust - User attributes and behavior

WEEK 3:  Analytics
·         Modeling
·         Sampling
·         Content Analysis
·         Propagation

WEEK 4:  Applications
·         Location
·         Filtering and recommender systems
·         Business use

Dr. Jennifer Golbeck, instructor, is Director of the Human-Computer Interaction Lab and an Associate Professor in the College of Information Studies at the University of Maryland, College Park. She is a Research Fellow of the Web Science Research Initiative and in 2006, she was selected as one of IEEE Intelligent Systems' Top Ten to Watch, a list of their top young AI researchers.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 66009116


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