Data Science Methods

Uitgebreide vaknaam	Data Science Methods
Leerdoelen	Upon completion of the course the student is able to: 1. select, from a set of state-of-the art data management methods, an appropriate way to retrieve, store, and combine data from different data sources 2. explain and work with several methods for structuring unstructured data 3. explain and work with several methods of cleaning raw data so that they become useful for further analyses 4. explain and work with several methods to address the issue of missing data 5. explain and apply state-of-the art machine learning techniques to address a given research question 6. interpret the output of machine learning methods and to translate the outcomes into business insights that are managerially and/or theoretically relevant 7. present the insights and outcomes of the application of data science methods (both orally and written) 8. critically reflect on their own application of data science methods 9. critically evaluate other applications of data science methods
Omschrijving	This course deals with a broad range of data science methods for assessing issues in (large-scale) business data that are collected from different sources, and are possibly unstructured. The course covers several state-of-the-art methods for data acquisition and data management. Furthermore, we will discuss methods for preparing data for further analyses, and several machine learning techniques (both supervised and unsupervised) such as Support Vector Machines, Neural Networks, and (ensemble) Tree Methods. We will discuss the (statistical) background of the techniques, and apply them to real-world data.
Uren per week	4
Onderwijsvorm	-computer practicum,  -hoorcollege
Toetsvorm	-groepsopdracht,  -individuele opdracht (Individual assignment: 50%, Group assignment(s): 50%)
Vaksoort	master
Coördinator	prof. dr. J.E. Wieringa
Docent(en)	A.E. Tatar, PhD. ,prof. dr. J.E. Wieringa
Verplichte literatuur	Titel Auteur ISBN Prijs Book(s)/book chapters: t.b.a. Various Relevant scientific articles f.e. from: Journal of Marketing, Journal of Marketing Research, Marketing Science, International Journal of Research in Marketing Various
Entreevoorwaarden	For students in the Research Master in Economics & Business, particularly those selected for the Business Analytics & Econometrics profile. Also for PhD students.
Opmerkingen	Secretary: L.C. Molog-Kwant, room 5411-0334, phone +31(0)50 3633686, e-mail marketing.education@rug.nl
Opgenomen in	Opleiding Jaar Periode Type Research Master in Economics and Business  (keuzevakken ReMa-Research Methods) 1 semester I b keuze