2022/2023
Social Network Analysis
Type:
Mago-Lego
Delivered by:
Department of Management
When:
2 module
Online hours:
14
Open to:
students of one campus
Instructors:
Evgeny A. Antipov
Language:
English
ECTS credits:
3
Contact hours:
16
Course Syllabus
Abstract
The course finalizes the customer analytics track by teaching students essential skills that are especially useful for the analysis of various social networks (e.g. networks of social media users, buyers, etc.): automated data collection from various sources (including the Web), analysis of network data and text mining. The course has two parts: in the online part students will be given access to 4 courses at the DataCamp platform which will serve as lectures and practical tutorials on how to collect and manipulate data from various sources with a special emphasis on scraping web data and using APIs. Academic support for the course is provided via LMS, where students can find guidelines and recommendations for self-study and sample questions for exam preparation. The exam is also conducted using LMS testing functionality. DataCamp platform is used in this course so that students can improve their R coding skills. DataCamp is an interactive learning platform for R, Python & SQL for Data Science. They teach cutting edge data analysis tools in an easily accessible manner. For every topic an introductory tutorial-style lecture is given to familiarize students with the topic A set of case studies every week is solved in class 90% of time is allocated to practicing R programming skills
Learning Objectives
- Scrape data from most websites
- Use APIs to obtain data from websites
- Present networked data in a format appropriate for quantitative analysis
- Develop and apply new research methods by combining and modifying existing techniques
- Solve CRM analytics problems using special methods for analyzing network data and machine learning techniques
Expected Learning Outcomes
- Importing data from various sources
- Building simple Shiny web applications for customer analytics
- Presenting networked data in a format appropriate for quantitative analysis
- Processing texts using basic string manipulations, as well as sentiment analysis and topic modeling
- Scraping data from most websites
- Solving CRM analytics problems using special methods for analyzing network data and machine learning techniques
- Using APIs to obtain data from websites
Course Contents
- Advanced aspects of importing data to R
- Working with Web data in R
- Building Web Applications in R with Shiny
- Network Analysis in R
- Predictive Modeling with networked data
- Text Mining
- Sentiment Analysis
- String Manipulations in R
- Topic Modeling
Assessment Elements
- Assignments
- QuizMultiple choice and short answers quiz based on lectures and seminars
- Midterm ExamAn empirical task where a student needs to apply their knowledge and skills from the course for data collection and analysis.
Bibliography
Recommended Core Bibliography
- Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
- Munzert S. Automated data collection with R: a practical guide to Web scraping and text mining. Chichester, West Sussex, United Kingdom: Wiley, 2014. 1 p.
Recommended Additional Bibliography
- Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.