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Warsaw University of Life Sciences - SGGW

address: Nowoursynowska 166 St.
02-787 Warsaw, Poland
phone: +48 22 593 10 25
e-mail: masterstudies@sggw.pl    internet: www.sggw.pl

Informatics and Econometrics Specialization: Big Data Analytics

degree

second, MSc

duration

4 semesters

language

English

ECTS points

120

description

The specialization, realized fully in English, is focused mainly on methods of analysis of the massive datasets. Within this specialization the students will get acquainted with the technologies used for storing, processing and analyzing large data sets and with other quantitative methods of economic analysis, the computer science tools and their practical application. The students will acquire practical skills in building analytical solutions on Big Data platforms. They will become familiar with distributed and parallel processing systems. They will learn how to use basic tools to visualize large data sets. The specialization is focused on the use of high level programming languages, as well as on the design and programming of the databases. The graduates will be able to incorporate the available methods and tools into the computer analysis systems. In general, the Big Data Analytics specialization prepares future analysts of massive datasets that are stored in companies and economical institutions, such as banks, stock markets, telecommunications companies etc.
The detailed list of subjects is as follows:
• First year: Mathematical Economics, Microeconometrics, Multidimensional Data Analysis, Software Engineering, Computer Networks, Modeling and Optimization of Business Processes, VBA Advanced Programming / Advanced Programming in Java [optional], Dynamic Econometrics, Operational Research – Applications, Survey Sampling, Oracle Databases / Actuarial Methods [optional], Advanced Data Exploration Techniques for Big Data, Facultative Courses 1, 2, 3, Second Foreign Language, Master Seminar.
• Second Year: Theory of Forecasting and Simulations, Basics of Financial Engineering, Management Information Systems, Processing Massive Datasets, Project Management, Intellectual Property Management, Statistical Analysis in the Market Research, Event History Analysis, Selected Issues in Sociology and Psychology, Business Ethics, Facultative Courses 4, 5, 6, Master Seminar, Master Thesis.
The graduates of Big Data Analytics are expected to find job in centers of information processing, IT companies, analysis departments of banks, brokerage companies, investment funds, telecommunications companies, central or local administration, scientific and research institutions.

tuition

• for Polish citizens and foreigners undertaking studies on the rules applicable to Polish nationals (e.g. holders of a valid Polish Charter card), the studies are free of charges (no tuition fee),
• for non-national undertaking studies on the rules not-applicable to Polish nationals - the first cycle of studies - starting in the academic year - 2018/19 is free of charges (no tuition fee) thanks to co-financing from the POWER program - "Success in nature".
Charges for documents, accommodation in a student dormitory, repetition of classes - in accordance with internal regulations.

deadline

for Polish citizens and foreigners undertaking studies on the rules applicable to Polish nationals: http://www.sggw.pl/dla-kandydatow/rekrutacja/studia-ii-stopnia/kalendarium
for foreigners undertaking studies on the rules not-applicable to Polish nationals: http://www.sggw.pl/en/for-candidates/recruitment/recruitment-of-foreigners/recruitment-terms

contact

Office for Students' Affairs: rekrutacja@sggw.pl
Faculty coordinator: joanna_landmesser@sggw.pl or wzim@sggw.pl

www

http://www.sggw.pl/en/for-candidates/recruitment/recruitment-of-foreigners#Inform%20and%20Econ

institution

Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences – SGGW

education requirements

• diploma of the first-cycle studies in the field of computer science and econometrics, informatics, economics, finance and accounting, logistics, mathematics
• diploma of another field of the first cycle studies, for which the effects of education are convergent with the learning outcomes expected of the candidates; if the convergence is incomplete, the student will be obliged to supplement the competence gaps by completing the subjects specified during the interview, in an amount not exceeding 30 ECTS, which is the limit of admissible discrepancy;
• confirmed knowledge of English language.

other requirements

average grade from first-cycle studies