Social Network Analysis
- 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
- Importing data from various sources
- Scraping data from most websites
- Using APIs to obtain data from websites
- Building simple Shiny web applications for customer analytics
- Presenting networked data in a format appropriate for quantitative analysis
- Solving CRM analytics problems using special methods for analyzing network data and machine learning techniques
- Processing texts using basic string manipulations, as well as sentiment analysis and topic modeling
- Advanced aspects of importing data to RImporting data from various file formats, including database files, statistical packages, html page
- Working with Web data in RDownloading Files and Using API Clients. Using httr to interact with APIs directly. Handling JSON and XML. Web scraping with XPATHs. CSS Web Scraping and Final Case Study
- Building Web Applications in R with ShinyEssentials of Shiny development. Plotting with Shiny. Interactive exploration of datasets. Creating wordclouds.
- Network Analysis in RFundamental concepts in social network analysis. Identifying important vertices in a network. Characterizing network structures. Identifying special relationships.
- Predictive Modeling with networked dataMeasures of Homophily. Network Featurization. Building predictive models using network features.
- Text MiningConverting texts to tidy text format. Word frequency analysis. Word clouds.
- Sentiment AnalysisSentiment analysis using various dictionaries
- String Manipulations in RString manipulations for data cleaning using stringr package. Concatenation. Substrings. Regular expressions.
- Topic ModelingLatent Dirichlet Allocation algorithm for topic modeling and its implementation in R.
- Kahoot (tests)Weekly tests using Kahoot.it platform covering the material studied in previous weeks. To compute Grade_Kahoot the sum of Kahoot points is calculated for each student. Then it is converted to a percentile from 0 to 100 using the corresponding Excel formula. An alternative % of max is calculated as the % of maximum score achieved by the top-performer. Grade_Kahoot=max(percentile, % of max)
- Midterm asessmentEach student should take a few Data Camp courses specified by the instructor (up to 4 courses). Free access will be granted to students of this course. The grading is based NOT on the DataCamp’s score, but on the student’s performance on the test given by the instructor. The test will check how well students mastered the material studied both in class and at DataCamp. Grade_Midterm is the score from 0% to 100% displayed by the LMS.
- Empirical case studies solved in class75-min. tests given at classroom every week. Each problem set consists of 2-5 problems. The total number of case studies equal the number of tutorials (around 10-12 case studies). For each case study a student can get the following scores: 0 (absent or everything is incorrect) 1 (present, but mostly incorrect solution) 2 (some mistakes) 3 (no mistakes) Grade_Cases is computed as the % of a student’s total score out of the maximum achievable score.
- Interim assessment (1 module)0.25 * Empirical case studies solved in class + 0.25 * Exam + 0.25 * Kahoot (tests) + 0.25 * Midterm asessment
- 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.
- Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.