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General Description

History

Qualification Awarded

Level of Qualification

Specific Admission Requirements

Specific Arrangements for Recognition of Prior Learning (Formal, Non-Formal and Informal)

Qualification Requirements and Regulations

Profile of the Programme

Key Learning Outcomes

1   To be able to find and improve appropriate means and tools to collect data properly. 2   To be able to summarize data and generate information in order to help decision making. 3   To be able to make basic and advanced statistical analyses. 4   To be able to visualize and present data properly and meaningfully. 5   To be able to use current information technologies for data management and analysis. 6   To be able to effectively use information technologies, statistics and operations research tools and techniques in sync.

Occupational Profiles of Graduates with Examples

Access to Further Studies

Course Structure Diagram with Credits

T: Theoretical P: Practice L: Laboratory B: Spring Semester G: Fall Semester H: Full Year 1 .Semester: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS G 1 SBE 5000 TECHNIQUES OF SCIENTIFIC RESEARCH AND PUBLICATION ETHICS COMPULSORY 3 0 0 5 G 2 VYA 5013 INTRODUCTION TO BIG DATA AND DATA SCIENCE COMPULSORY 3 0 0 6 G 3 VYA 5019 APPLIED STATISTICS COMPULSORY 3 0 0 6 G 4 VYA 5017 STATISTICAL PROGRAMMING LANGUAGES COMPULSORY 3 0 0 7 G 5 VYA 5015 MATHEMATICS FOR DATA SCIENCE COMPULSORY 3 0 0 6 TOTAL:   30   1 .Semester Elective: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS   2 .Semester: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS B 1 VYA 5098 FIELD STUDY COMPULSORY 2 0 0 2 B 2 VYA 5096 SEMINAR COMPULSORY 0 2 0 2 B 3 VYA 5026 STATISTICAL LEARNING COMPULSORY 3 0 0 6 B 4 VYA 5022 SUPERVISED MACHINE LEARNING COMPULSORY 3 0 0 6 B 5 VYA 5016 UNSUPERVISED LEARNING METHODS COMPULSORY 3 0 0 6 B 0 - ELECTIVE COURSE ELECTIVE - - - 8 TOTAL:   30   2 .Semester Elective: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS B 1 VYA 5018 DATA VISUALISATION ELECTIVE 3 0 0 4 B 2 VYA 5024 TIME SERIES AND FORECASTING TECHNIQUES ELECTIVE 3 0 0 4 B 3 VYA 5020 SPATIAL DATA ANALYSIS ELECTIVE 3 0 0 4   3.Semester: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS G 1 VYA 5099 THESIS COMPULSORY 0 1 0 30 TOTAL:   30   4.Semester: Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS B 1 VYA 5099 THESIS COMPULSORY 0 1 0 30 TOTAL:   30

Examination Regulations, Assessment and Grading

Graduation Requirements

Mode of Study (Full-Time, Part-Time, E-Learning )

Programme Director or Equivalent