This 21-month full-time thesis-based M.S. program emphasizes theory, concepts, modeling approaches, modeling and analysis tools in the context of Business Analytics.
The primary objective of Master of Science in Business Analytics Program (MScBA) is to teach students descriptive, predictive, and prescriptive analytics tools that enable them to develop managerial insights for decision making from complex, large, and structured or unstructured datasets. The program welcomes applicants with quantitative backgrounds in management, economics, engineering, and natural and social sciences with a strong appetite for data analysis, modeling, and data-driven decision making.
MScBA is a rigorous two-year program with a master thesis. The first year is mainly dedicated to courses and seminars to cover the fundamentals of various descriptive, predictive and prescriptive analytics methods, while the second year is mainly dedicated to developing and defending a master thesis under direct supervisorship of our faculty members. The master thesis is expected to involve a business or policy problem and offer solutions by using various business analytics tools. The students will also have the opportunity to participate in the research projects offered at the Behavioral Analytics and Visualization Lab and other research programs conducted by faculty members. Upon successful completion of this program, the graduates are prepared to take the role of data scientist or business analyst in leading corporations in a wide range of industries or pursue doctoral studies.
The program seeks to achieve the following learning outcomes:
- Demonstrated understanding of data-driven decision modeling and analysis concepts and frameworks,
- Knowledge of and hands-on experience with fundamentals of business analytics, management information systems, statistical and prediction models,
- Ability to transform complex data into valuable insight and resulting value-adding actions,
- Skills in hands-on data-mining tools and techniques thereby leading to graduates that are competitive in the Analytics job market,
- Deeper expertise in a selected line of research using real data.
The required number of courses is 8, which is equivalent to 24 SU credits. After completing the 8-course, students will conduct thesis-based research over the Fall and Spring semesters of their second year.
| Fall Term I | SU Credits | ||
| BAN 500 | Introduction to Business Analytics | Required | 3 |
| BAN 527 | Descriptive Analytics | Required | 3 |
| Elective Course 1 | Elective | 3 | |
| BAN 599 | Graduate Seminar | Seminar | 0 |
| GR 501M | Academic Practices and Development | Required | 0 |
| Spring Term I | |||
| BAN 502 | Introduction to Decision Making | Required | 3 |
| BAN 505 | Predictive Analytics | Required | 3 |
| Elective Course 2 | Elective | 3 | |
| GR 502M | Academic Practices and Development 2 | Required | 0 |
| Fall Term II | |||
| BAN 600 | Master Thesis | Thesis | 0 |
| Elective Course 3 | Elective | 3 | |
| GR 555M | Academic Writing for Graduate Students | Required | 0 |
| Spring Term II | |||
| BAN 600 | Master Thesis | Thesis | 0 |
| Electice Course 4 | Elective | 3 | |
| GR 503M | Academic Practices and Development 3 | Required | 0 |
| TOTAL | 24 | ||
Fall Term 1
BAN 500 Introduction to Business Analytics
As an introductory course to the program, the course will cover topics on the conceptual framework of business analytics, various sectoral application areas and a general introduction to analytical methods used. The course will also cover success stories from different sectors where business analytics is applied, and big data analytics in general, including its application areas, as a new and emerging area of interest.
BAN 527 Descriptive Analytics
This course aims to provide a review of methods for statistical inference, and develop an understanding of how these tools can be applied in a variety of business problems. The emphasis of this course would be on applications, through practical examples and cases. A variety of statistical software will be introduced. Topics covered include descriptive statistics, probability distributions, hypothesis testing, regression, design of experiments and analysis of variance.
BAN 599 Graduate Seminar
This seminar course provides a non-credit framework for the continuous monitoring and collegial discussion of MA students' thesis research and writing, which they are expected to accomplish under the supervision of a Faculty member from the relevant field.
GR 501M Academic Practices and Development
This course introduces fundamental principles in teaching and learning for graduate students in the event that they choose a career in academia. In addition, this course acculturates students to the practices and culture of Sabanci University. Through a series of workshops, graduate students will learn about best practices in teaching and learning, ethics, publication of research and how to apply them to the current student population at Sabanci University. Once students have participated in this series of workshops, they will help out with one or more courses offered at the University, which will allow them to apply the principles learned in the workshop.
Spring Term I
BAN 502 Introduction to Decision Making
This course presents an overview of decision-making support methodologies and emphasizes the design of decision support systems using management science models such as production planning, logistics, employee scheduling, stock trading simulation, and portfolio optimization. These systems are developed using Microsoft Excel and VBA. VBA fundamentals are also covered in the course.
BAN 505 Predictive Analytics
This course introduces basic concepts and models of supervised and unsupervised statistical learning models. The topics include, multiple regression, logistic regression, classification, resampling methods, subset selection, the ridge, the lasso, tree-based methods, support vector machines, principal component analysis, and clustering.
GR 502M Academic Practices and Development 2
This course is a follow-up to GR 501M and aims to provide further opportunities for graduate students to develop their skills in teaching and learning in higher education. This course will enable graduate students to reflect on their first semester helping out with a course, plan for the coming semester, and develop new skills in areas such a teaching with technology and grading. Through a series of workshops, graduate students will learn about best practices in teaching and learning, and how to apply them to the current student population at Sabanci University. Students will continue to helping out with one or more courses offered at the University, which will allow them to apply the principles learned in the workshop.
Fall Term II
BAN 600 Master Thesis
Master Thesis provides a non-credit framework for the continuous monitoring and collegial discussion of MA students' thesis research and writing, which they are expected to accomplish under the supervision of a Faculty member from the relevant field over the second year of their course-work.
GR 555M Academic Writing for Graduate Students
This course aims to enhance the academic communication skills of graduate students with the goal of facilitating research output. To this aim, this course helps master’s and doctoral students develop skills essential for writing academic papers and theses in English. It provides guidelines on writing for the rhetorical genre’s characteristic of academic writing in the social sciences and humanities such as literature review, abstract, proposal, article for publication, and thesis. Students will also help out with one or more courses offered at the University.
Spring Term II
BAN 600 Master Thesis
Master Thesis provides a non-credit framework for the continuous monitoring and collegial discussion of MA students' thesis research and writing, which they are expected to accomplish under the supervision of a Faculty member from the relevant field over the second year of their course-work.
GR 503M Academic Practices and Development 3
This course is a follow-up to GR501M and GR502M, and aims to provide further opportunities for graduate students to develop their skills in teaching and learning in higher education. Students in this course will be observed by a supervisor or another observer at least once per semester in order to provide feedback on the student’s teaching performance as well as have the option to participate in additional professional development activities. Students will continue helping out with one or more courses offered at the University, which will allow them to apply the principles learned in this and previous courses.
| BAN 503 | Management Information Systems |
| BAN 504 | Data Mining with SAS Enterprise Miner |
| BAN 521 / OPIM 523 | Decision Models |
| BAN 522 | Revenue Management |
| BAN 523 | Group Decision Making under Multiple Criteria |
| BAN 524 / OPIM 506 | CRM using Location Intelligence |
| BAN 526 / OPIM 526 | Business Intelligence and Decision Support Systems |
| BAN 525 / ECON 501 | Microeconomics I |
| BAN 528 / ECON 502 | Microeconomics II |
| BAN 529 / ECON 506 | Econometrics |
| BAN 533 / IE 503 | Stochastic Processes |
| BAN 531 / IE 523 | System Simulation |
| BAN 537 / IE 527 | System Dynamics |
| BAN 535 / CS 515 | Neural Networks |
| BAN 532 / CS 512 | Machine Learning |
| BAN 539 / CS 525 | Data Mining |