Understanding Social Network Analysis –
A social network is based on people and their relationships with others. People in the network are referred to as nodes. Mathematical mapping of these relationships between nodes is known as social network analysis.
Why we need social network analysis –
Large number of nodes and their relationships with other nodes lead to a huge volume of raw data available on a social network. Social network analysis of real-world data sets focuses on a range on tasks: identifying important nodes in the network, to detecting communities, to tracing information diffusion and opinion formation.
About the course –
The course teaches you methods and concepts used to identify communication patterns and mine useful data from them. Although, they sate that no mathematical or programming knowledge is required for the course, a basic understanding of data structures, clustering and eigenvectors is recommended. A wide range of topics and tools have been discussed in the syllabus, which help the student form a strong understanding of the subject. Plus, complex concepts have been simplified with the use of visual aids that make the learning process easier and interactive.
However, some of the shortcomings of the course are –
- The course only equips you with academic knowledge to develop the right intuition to analyze a network’s data. It does not teach you to scrap, mine, or analyze network data to get information from raw data.
- Since the course is new, the structure is a little confusing and certain concepts that need to be taught earlier are touched upon at a later stage. However, they will probably rectify this in future editions of the course.
This course is very useful for people interested in studying networks and social media marketing. GEPHI (http://gephi.org/) is the software used to mine and analyze data and basic usage of this software is taught. Although most of the programming problems are optional, I would strongly advice giving them a try as they help in real understanding of the topic, and not just the theory.