Overview of the MS in Business Analytics program
CEU's MS Business Analytics offers a full spectrum of skills and knowledge necessary for business analysts to create value from big data and other sources of quantitative information while emphasizing the management and economic aspects of how data creates value.
Vast Number of Diverse Career Opportunities
Graduates of the program will be familiar with all aspects of Business Analytics, with an emphasis on Big Data and will be especially well versed in applications assisting business decision making. Understanding the pressing questions posed by managers, the methodological challenges of statistics, as well as the power and limitation of technologies, our graduates will be sought for key positions within organizations and may even become entrepreneurs in the booming analytics startup arena.
More than 93 percent of our students find a job straight after graduation, 38 percent of them advance in seniority. Our graduates work at EPAM Systems, GE, Morgan Stanley, MSCI, OTP Bank, Citi, among others.
Coursework in an Interdisciplinary Program
The core coursework program combines courses from several disciplines. In particular, courses will cover coding, statistics, machine learning, data science and big data/cloud computing and data engineering.
In the Fall term, Foundations courses will cover an introduction to the R language, statistical foundations, basics of data management, as well as provide an overview of the open-source and commercial data science and big data tools. Concepts of business intelligence, data visualization and storytelling will be also covered. In the Winter term, Advanced courses will include prediction methods and machine learning, as well as practical and data engineering aspects of putting analytics into production. Use-case courses including hands-on examples will help bring methods learned closer to real life experiences.
Electives will allow students to either deepen their knowledge in analytics and engineering or take up management and business courses. In the Winter term electives include advanced machine learning and engineering courses as well as a course for learning Python as another language of data science. In the Spring term, Mastering courses will offer short discussion of narrow topics such as Deep learning, data science team and project management. Students will be able to choose from a set of cross-listed courses from the Technology Management and Innovation program, too. Furthermore, up to four credits (typically 2 classes), students take any course from the University, from network science to environment or energy economics.
At the end of the program each student is matched with an industry partner to carry out a capstone project. The goal of the final capstone project is to expose students to a complete analytics workflow with a variety of tasks at a company or other organization. You will use the full spectrum of skills acquired in the program, challenge yourself, have a valuable learning experience in the process and create value for the partner company. Students will have to carry out a project that generates useful content for the partner organization. Past projects included designing a data warehouse in a large organization, building a predictive model of customer behavior, fraud detection or designing and evaluating experiments through data analysis.