Degree Structure
College
College of Business Administration
Department
Master of Science in Business Analytics
Level
Graduate Masters
Study System
Courses and Thesis
Total Credit Hours
33 Cr. Hrs.
Duration
2-4 Years
Intake
Fall and Spring
Language
English
Study Mode
Full Time and Part Time
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Degree Overview
The Master of Science in Business Analytics (BAM) is a joint program offered by the College of Business Administration and the College of Computing and Informatics. It prepares students for academic and professional careers in business analytics, a field shaped by the growing volume of data available to organizations. Students acquire the knowledge and skills to apply data analysis methods to business problems and to support evidence-based decision-making across functional areas.
The program comprises 33 credit hours: six compulsory courses (18 credit hours), two elective courses (6 credit hours), and a thesis (9 credit hours). The thesis is the program's capstone, in which students conduct an independent investigation of a business analytics problem under faculty supervision, drawing on the quantitative and research methods covered in the program.
What You Will Learn
Students learn to design and query relational databases, apply regression modeling and forecasting techniques, and use data mining methods for classification, clustering, and association analysis. The curriculum also develops competence in optimization and decision modeling, analytical programming in Python and R, and the design and execution of business research. Electives allow students to apply these methods in finance, marketing, healthcare, operations, and supply chain contexts.
University Requirements
College Requirements
Degree Requirements
Compulsory Courses (18 credit hours)
Elective Courses (6 credit hours)
Thesis (9 credit hours)
Study Plan:
Course Distribution
| Course Code | Course Title | Credit Hours | Pre-requisite |
| Compulsory Courses | |||
| 1503510 | Databases and Business Intelligence | 3 | - |
| 1503520 | Fundamentals of Business Analytics | 3 | - |
| 0308530 | Statistics and Forecasting | 3 | 1440264 |
| 1503540 | Data Mining | 3 | - |
| 1503550 | Optimization and Decision Models | 3 | 0308530 |
| 0302560 | Research Methods | 3 | 0308530 |
| Elective Courses | |||
| 1503530 | Advanced Business Analytics | 3 | 1503510,1503520 |
| 1503511 | Analytical Software Tools | 3 | 1503520 |
| 0302513 | Healthcare Analytics | 3 | 1503520, 1503540 |
| 0302514 | Marketing Analytics | 3 | 1503520, 1503540 |
| 0302515 | Supply Chain Analytics | 3 | 1503520, 1503540 |
| 0308516 | Finance Analytics | 3 | 1503520, 1503540 |
| 1503517 | Special Topics | 3 | 1503520, 0308530, 1503540 |
| 0307521 | Business Elective Course (MBA): Leadership and Organizational Behavior | 3 | - |
| 0307522 | Business Elective Course (MBA): Managing Operations | 3 | - |
| Thesis | |||
| 0302621 | Thesis | 9 | 1503510, 1503520, 0308530, 0302560 |
Course Description
| Course Number | Course Title | Credit Hours | Course Description |
| 1503510 | Databases and Business Intelligence | 3 | This course provides students with the theoretical foundation and technical skills required to implement a business database solution on a relational database management system using Microsoft Access (latest version). The course teaches students how to create tables, perform queries, create forms and reports according to storage needs and constraints and how to retrieve and modify specific data by using the Access (latest version) SQL components. Finally, students will learn how to develop and test a database application. |
| 1503520 | Fundamentals of Business Analytics | 3 | The course is an introduction to business analytics. This course will focus on teaching fundamental concepts and tools needed to understand the emerging role of business analytics in organisations. This course will also focus on teaching how to identify, evaluate, and capture business analytic opportunities that create value. Toward this end, students will learn basic analytic methods and analyze case studies on organisations that successfully deployed these techniques. |
| 0308530 | Statistics and Forecasting | 3 | Statistics and Forecasting is a course in applied statistics that examines the use of linear regression techniques for examining relationships between variables. The course emphasizes the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course. |
| 1503540 | Data Mining | 3 | This course introduces data mining terminology, data types and their properties, and methods for exploring and visualizing large datasets. It covers classification, clustering, and association analysis, together with the algorithms that allow computers to detect patterns in databases and perform prediction and forecasting. Students study data mining as a core stage of the knowledge discovery process, which includes data selection, cleaning, coding, the application of statistical and machine learning techniques, and the visualization of results. The course also addresses the protection of personal data and privacy in data mining practice. Methods are treated theoretically and practically through laboratory sessions in which students implement and test selected techniques on realistic datasets. |
| 1503550 | Optimization and Decision Models | 3 | This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. This course emphasizes the application of mathematical optimization models over the underlying mathematics of their algorithms. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: healthcare, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercises will involve the use of Microsoft Excel's “Solver" add-on package for mathematical optimization. |
| 0302560 | Research Methods | 3 | This course enables students to deeply understand the importance of business research and how research is carried out in various management and business settings. The course introduces a range of research paradigms and associated methodologies in the field of management, business studies, and statistics. Students gain advanced knowledge of the research process and relevant statistical techniques and develop the skills to plan and execute a research project. Topics covered include the nature, strategies, and process of business research; the nature of quantitative, qualitative, and mixed methods research; research designs; planning a research project; and using the IBM SPSS statistical package. |
| 0302621 | Thesis | 9 | This is a required capstone course, serving as the program's final component, and allowing an extended independent study and research. The primary goal of the course is to provide the student an opportunity to conduct an independent investigation of a business analytics problem through self-directed discovery and experiential learning process. It draws on various components of the program, most notably the quantitative and research methodologies. Additionally, students will benefit from personalized supervision by an academic supervisor. |
| 1503530 | Advanced Business Analytics | 3 | The course moves beyond pattern detection, clustering, and correlation to build models of the consumer behavior that generates the data, following a model-based approach to prediction. It focuses on two aspects of customer and product behavior, timing processes and counting processes, and applies them to sales concentration and long-tail models. The course is hands-on and requires students to complete a semester project. |
| 1503511 | Analytical Software Tools | 3 | The purpose of this course is to provide everything a student needs to get started using Python and R for data analysis. Python and R are the top open-source data science tools in the world. Both programming languages are key building blocks of many business analytics courses. Indeed, being able to collect and transform data, perform analyses on them, and doing so efficiently, is the basic setup of many topics in business modelling, statistics, operational research, and so on. By providing a thorough background in the building blocks of programming and its applications, this course aims to provide students from non-technical backgrounds with the foundations needed to work confidently in a programming environment and apply it to business modeling. |
| 0308516 | Finance Analytics | 3 | This course provides students with knowledge of the application of financial data analytics techniques in different financial situations. Students will learn methods for analyzing the large volumes of structured and unstructured data generated in the finance sector. Students will gain knowledge of the most important subjects in finance analytics, including optimization for financial problems, financial econometrics, the securities market and macroeconomics, financial statement analysis, and financial time series analysis. |
| 0307521 | Leadership and Organizational Behavior | 3 | This course focuses on the leadership dimension of managers by dealing with the dynamics of human interactions in organizations. It addresses issues related to the influence of leadership on the behavior of individuals, teams, and networks in the context of organizational culture. In addition, it shows how to build productive relationships and manage performance for the long-term success of the organization. Overall, the course equips students with a balance of theory and practice on the major theories and research on leadership and managerial effectiveness in formal organizations. The topics covered in the course include: the nature of managerial work; leadership traits and skills; effective leadership behavior; power and influence; leading change and innovation; leadership in groups and teams; developing leadership skills; and a broad range of leadership theories encompassing contingency theories and adaptive leadership, participative leadership, strategic leadership, charismatic and transformational leadership, servant and authentic leadership. The course also covers cross-cultural leadership and diversity and deals with some contemporary issues in leadership. |
| 0302513 | Healthcare Analytics | 3 | This course will present students with an introduction to the field of health informatics and advanced healthcare analytics using core technologies and data analytics (computational and analytical methods) and the use of health information technology to improve decision making in healthcare. Specific topics will include overview of the healthcare analytics concept and related terminologies, data standards, security and confidentiality, health information exchanges, population health management and health data analytics, consumer health informatics, emerging health informatics innovations, and other topics related to health informatics. The course starts by examining how healthcare data is collected and stored. It then goes on to explore how information management methods, machine learning and data visualization are used in data analysis. |
| 0307522 | Managing Operations | 3 | This course provides an in-depth understanding of the topics and mathematical techniques for solving problems in the designing, planning, and controlling of operations and supply chain activities. Topics covered in this course include forecasting, product design and development, managing quality, layout strategy, supply-chain management, inventory and logistics management, sequencing and scheduling, and quantitative tools for operation managers. The course consists of two major parts: a body of knowledge component which is delivered through the textbook and lecture material, and a critical thinking part which is obtained through case analysis, discussion and presentations. Students learn relevant concepts, frameworks, tools, and techniques required to manage the operations and supply chain. |
| 0302514 | Marketing Analytics | 3 | The course explores customer data analysis techniques and their theoretical foundations to help students acquire analytic skills that can be applied to real world marketing problems. Students will study various tools for generating marketing insights from empirical data in such areas as segmentation, targeting and positioning, satisfaction management, customer lifetime analysis, customer choice, and product and price decisions. |
| 0302515 | Supply Chain Analytics | 3 | The purpose of this course is to provide an advanced understanding of the concepts related to managing supply chains using analytics. Students learn how supply chain design choices affect the responsiveness with which goods reach customers, how to use historical data to forecast product demand, and which practical techniques, including rescheduling and rapid response, improve supply chain performance. |
| 1503517 | Special Topics | 3 | Business analytics changes rapidly as data availability expands and new methods and practices emerge. This course examines current theoretical and practical developments in business analytics, including emerging issues, methodologies, and practices. On completion, students will be familiar with state-of-the-art topics in the field. |
Career Path
A Master of Science in Business Analytics (BAM) program prepares graduates for high-demand careers that blend business acumen with data-driven decision-making. This degree equips professionals with advanced skills in statistical analysis, data visualization, machine learning, and predictive modeling, enabling them to extract actionable insights from complex datasets. Graduates often pursue roles such as data analyst, business intelligence analyst, data scientist, or analytics consultant in industries including finance, healthcare, technology, retail, and consulting. With organizations increasingly relying on data to guide strategic decisions, an BAM offers strong career growth potential and opens doors to leadership positions in analytics and digital transformation initiatives
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