Data Analytics for Business and Economics 2400-ENSM057C

This seminar is to support the MA thesis in the data analytics, used in a practice and a theory of business and economics.
There are mainly three thematic areas on focus: 1) unsupervised learning methods (k-means, PAM, CLARA, PCA, MDS, association rules etc.), 2) spatial analysis (for geo-located data), 3) Monte Carlo simulation models and bootstrapping. These methods can be addressed in an empirical and as well as theoretical approach.

In three semesters time span students are to review the literature, develop own study and complete the thesis. Seminar is conducted as a set of individual regular consultations. Programming is in R.

A goal of this seminar is to develop, validate and revise the quantitative methodology and models. Both applied and theoretical works will be supported. Students will refer mainly to current journal literature of the topic.

Potential types of thesis:
- Comparison of the methods on theoretical and /or empirical data to test similarity and sensitivity of methods, as well as its content capacity
- Development of the existing studies by refreshing the results on another datasets and by complementing the conclusions on the results and literature/methodology.
- Theoretical features of methods for different datasets, distributions, applications etc.
- Case studies for business applications

The very desired outcome of the works is a publishable paper.