Overview of the MS in Business Analytics program

Offering Skills for Business Analytics
There is already a shortage of data scientists and business analysts due to the Big Data boom. Businesses and other organizations increasingly recognize the potential value of the huge amounts of data produced and archived each day, indeed every minute and second, if such data is analyzed correctly and creatively. The skills necessary to analyze and act upon insights from data are dispersed among managers, economic analysts, computer scientists, engineers and statisticians. Business analytics, especially involving Big Data, can help organizations make smarter and more effective decisions, such as finding ways to increase sales or to cut costs. Indeed, Big Data especially will be a source of future business growth and has a major trend in business that will play out over many years. Thus, Big Data increases the need for well-prepared professionals in Business Analytics

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.

Capstone project

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.

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