Master Thesis in Statistics

Aim

After completion of the course, the student should be able to:

• search, collect and integrate relevant scientific literature in relation to your own problem formulations
• independently identify, formulate and solve statistical problems by using relevant methods for analysis and evaluation
• apply mathematical models underlying the selected analysis methods and adapt these models to the given problem
• efficiently implement statistical machine learning methods in a programming language, and apply the implemented technological solution to the problem identified in the thesis
• plan and carry out scientific work within given time frames
• identify relevant sources of information, conduct information searches, critically assess the obtained information, and to use correct referencing to prior work
• communicate the problem formulations of the scientific work, its solutions, and other relevant information, both orally and in written form
• critically assess and evaluate own work and the work of others with respect to relevant scientific, societal and ethical dimensions
• apply analytical tools in the practice of a profession in Statistics

Content

The following is covered in the course:

• problem formulation and its presentation to a number of research problems
• identification of relevant research articles
• selection of suitable models from machine learning, data mining, statistics and related fields
• studies of the underlying mathematical models, adaptation/modification of such models to the given research context
• implementation of the models in a computer language, code verification and simulations
• analysis of simulation results, model and code adjustments
• interpretation of the obtained output, comparison of the selected models and their output with the results published by the research society
• summary of the studies and results in terms of a master thesis
• oral presentation and opposition of the scientific work