Topics in Econometrics

• Businesses can predict future sales results by combining their customers’ preference profiles with website click-stream data, social network interactions, and location data.
• Police and fire departments collaborate with emergency managers to develop more accurate models of automotive and pedestrian traffic by using GPS data from cars, buses, taxis, and mobile phones.
• Emergency room physicians are able to reduce time to initial treatment and, as a result, patient mortality, by fusing aggregate patient histories with the results of up to the minute lab tests.
• Web scraping analytic tools applied to for example twitter can be used to measure real-time international conflict sentiment levels and to measure political tendencies and their movements prior to important elections etc.
• With the development of electronic health records, remote treatment, and the ability to share data online, we have an array of new healthcare solutions available. The use of mobile technologies to collect and distribute information might help significantly with the prevention and treatment of disease.

• Marketing analyses: How can preferences of customers be identified from a survey? How can customer segments be identified?
• Political science: How can theoretical characteristics of voting behavior be identified using a survey? How can typical taxonomies of voters be revealed from a survey?
• International economics: How can we identify and separate between developed and underdeveloped countries using national account data?
• Public health: How can we identify patients who are potentially at risk of developing Type II Diabetes?
• Law: How can different characteristics of the Danish judges in the 20th century be exposed? Is it possible to provide a classification of such judges?
• Language: How are the relationships between use of digital media and school children’s motivation, competences and attitudes towards language learning?

• Datafication and data collection: Methods to generate and structure data in an expedient and operable format
• Data analysis and data visualization: Methods to process, analyze and visualize the data within a big data and multivariate data method setup

The student, should after the course be able to:

• Competently use R to solve the problems based on Big Data Analytics.
• Competently use R and related software to do analyses on large multivariate data sets
• Account for and discuss all three phases of working with big data and specific methods for generating, processing/analyzing and making predictions and informed decisions on the basis of Big Data and multivariate data.
• Identify and assess big data and multivariate data resources relevant in social sciences.
• Identify proper selection of methods for analyzing large multivariate data sets, including cluster analysis, factor analysis and discriminant analysis

Examples:

• James, G., Witten, D., Hastie, T., Tibshirani, R (2013). An Introduction to Statistical Learning with Applications in R. Springer (can be downloaded free of charge from the SDU Library)
• Loebbecke, C. & Picot, A. (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24, (3) 149-157
• Lycett, M. 2013. 'Datafication': making sense of (big) data in a complex world. European Journal of Information Systems, 22, (4) 381-386
• Sharma, S. (1996). Applied Multivariate Techniques. John Wiley & Sons, 1996.