Master of Science in Business Analytics

About the program
Business Analytics is where data science meets business strategy. This advanced and practice oriented program, will enhance students' ability to use data analytics and machine learning to extract quantitative insight and build predictive models as well as make evidence-based decisions. Courses on network science, strategy management or behavioral economics will broaden the understanding of data driven decision-making. Learning about data architecture, big data computing and technology management will prepare for managing data as a strategic asset.

FULL-TIME program
11-MONTHS (September-July)

PART-TIME program
16-MONTHS (September- December - may be extended to 22 months; core course classes are on weekends and Friday afternoons, some electives may be on weekday evenings)

Program Head: Prof. Gábor Békés (room: N13 505)

Program Administrator: Ms. Eszter Fuchs (room: N13 414, Contact person by email)

Entry Requirements for the Program

A minimum of four years spent in higher education. A bachelor's (or higher) degree from a reputable institution in business, economics, statistics, computer science, engineering. mathematics, social sciences, the physical sciences or other quantitative-oriented fields is generally required.

Admission is based on previous studies including specific courses, professional experience, a statement of purpose, and letters of recommendation.


Title Instructor Credit
Agile Project Management István Ottó Nagy 2.0
Banking IT and Fintech: Bank to the Future Szabolcs Szalay 2.0
Big Data Computing (full time) Zoltán Tóth 2.0
Big Data Computing (part-time) Zoltán Tóth 2.0
Business Economics Marc Kaufmann 2.0
Business Intelligence in SPSS Gyorgy Kormendi 1.0
Business Intelligence in Tableau Ivett Kovács 1.0
Consultative Selling and Negotiations Achilles Georgiu 1.5
Data Analysis 1a: Foundation of Data management in R (full-time) Gergely Daroczi 2.0
Data Analysis 1a: Foundation of Data management in R (part-time) Gergely Daroczi 2.0
Data Analysis 1b: Foundation of Data management in Stata (full-time) Gábor Békés 2.0
Data Analysis 2: Foundations of Statistics Arieda Muço 2.0
Data Analysis 3: Pattern discovery and regression analysis Gábor Békés 2.0
Data Analysis 4: Prediction Analytics with introduction to Machine Learning Gábor Békés 2.0
Data Analysis 5: Experiments and causal analysis of interventions Gábor Békés 2.0
Data Infrastructure in Production (full-time) Zoltán Tóth 2.0
Data Infrastructure in Production (part-time) Zoltan Toth 2.0
Data Management and Analysis in Python * 2.0
Data Science and Machine Learning 1: Concepts Szilard Pafka 2.0
Data Science and Machine Learning 2: Tools Szilard Pafka 2.0
Data Visualization Krisztina Szűcs 1.0
Different Shapes of Data László Salló 2.0
Digital Marketing 1 Tibor Farkas 2.0
Digital Marketing 2 - From mass to segment of one Gabor Bacsa 1.0
Digital Strategy Zsolt Szeleczki 2.0
Digital Transformation Achilles Georgiu 2.0
e-Leadership Zoltan Buzady 2.0
Ethical Leaders and Intergity Davide Torsello 2.0
Ethics of Big Data Chrys Margaritidis 2.0
Financial Trading Design and Technology Ferenc Meszaros 1.0
Information Lifecycle Management Tibor Voros 2.0
IoT - Industry 4.0 Tamas Boday 2.0
Organizational Behavior and HR Management Olaf Zylicz 2.0
Scientific Python Fall 2017/2018 Roberta Sinatra 3.0
Security and Data Protection Peter Papp 1.0
Seminar Series on Applied Data Science in Companies Gábor Békés 1.0
Technology Innovation (Cognitive and Smart Systems) Norbert Sepp 2.0