Degree Structure
College
Business Administration
Department
Level
Graduate Masters
Study System
Courses and Theses
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 COBA and Computing and Informatics College and is designed to prepare students for academic and practical careers in the emerging and vital field of the big data. The program aims to prepare the students with the knowledge and skills in various data analysis and methods to become effective decision-makers. Students will apply data-driven approaches to solve business challenges in different business areas.
The BAM is divided into Compulsory and Elective courses with a total of 33 credit hours. Six compulsory courses cover 18 credit hours, followed by two elective courses worth 6 credit hours and subsequently a Thesis of 9 credit hours. The thesis is a capstone course, serving as the program's final component, and allowing an extended independent study and research under the supervision of a senior faculty member. The primary goal of the thesis is to provide the student with an opportunity to conduct an independent investigation of a business analytics problem through self-directed discovery and an experiential learning process. It draws on various components of the program, most notably the quantitative and research methodologies.
What You Will Learn
Pursuing a Master of Science in business analytics equips individuals with advanced skills in data analysis, statistical modeling, and decision-making, making them highly sought-after in diverse industries. This degree not only facilitates entry into the field but also enhances career progression, offering roles such as business analyst, data analyst, business intelligence manager, or data scientist.
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 | 1440264 |
| 0308530 | Statistics and Forecasting | 3 | 1440264 |
| 1503540 | Data Mining | 3 | - |
| 1503550 | Optimization and Decision Models | 3 | 0308530 |
| 0302560 | Research Methods | 3 | 0308530 |
| 0302621 | Thesis | 9 | 1503510, 1503520, 0308530, 0302560 |
| Elective Courses | |||
| 1503530 | Advanced Business Analytics | 3 | 1503510 and 1503520 |
| 1503511 | Analytical Software Tools | 3 | 1503520 |
| 0302513 | Healthcare Analytics | 3 | 1503520 and 1503540 |
| 0302514 | Marketing Analytics | 3 | 1503520 and 1503540 |
| 0302515 | Supply Chain Analytics | 3 | 1503520 and 1503540 |
| 0308516 | Finance Analytics | 3 | 1503520 and 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 | 0307525 |
Course Description
| Course Number | Course Title | Credit Hours | Course Description |
| 1503510 | Databases and Business Intelligence | - | 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 | - | 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 | - | Regression Modelling is a course in applied statistics that studies 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 | - | Introduction to data mining, its terminology and overview over various types of data and its properties, an overview of different methods to explore and visualize large amounts of data, introduction to classification methods, introduction to clustering methods, introduction to association analysis, handling of personal integrity in the area of data mining. Course also includes data mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples. The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data. |
| 1503550 | Optimization and Decision Models | - | 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 | - | 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 will gain advanced knowledge of the steps comprising the research methods and relevant statistics and provides students with the skills of planning and executing 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 | - | 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 | - | This course provides the advanced knowledge, skills and tools to support data-driven decision making. Organizations generate, collect, and store massive amounts of data. The course will help examine aspects of data and analytics to gain an understanding of the principles and applications of the ideas that can lead to enhanced decision making. The main objective of this course is to present predictive and prescriptive analytics tools in the context of business cases, with an emphasis on implementing analytical approaches within an organization. The course will go beyond pattern detections, clustering, or correlation in data to build models of plausible consumer behavior that generates the data. Thus, a key goal of the course is to teach students a model-based approach to prediction. We will focus on two key aspects of user (or product) behavior; timing process and counting process. We will also examine issues of sales concentration and models of long tail using these processes. The course is hands-on and requires a semester project. |
| 1503511 | Analytical Software Tools | - | 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 do this is in an efficient way, 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 non-technical profiles with the necessary basics to be mature in programming environment and business modelling. |
| 0308516 | Finance Analytics | - | This course provides students with knowledge of the application of financial data analytics techniques in different financial situations. Students will learn the different solutions of analyzing huge volume of structured and unstructured data generated in the finance sector. Student 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 | - | 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 | Health Care Analytics | - | 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 | - | This course provides deep understanding to 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 circulated through the text and lecture material, and a critical thinking part which is obtained through case analysis, discussion and presentations. Students would learn relevant concepts, frameworks, tools, and techniques required to manage the operations and supply chain. |
| 0302514 | Marketing Analytics | - | 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 | - | The purpose of this course is to provide an advanced understanding of the concepts related to managing supply chains using analytics. Students will also learn; Supply chain design choices in managing the responsiveness with which goods are supplied to clients, learn how to use historical data to predict future demand for products, understand practical techniques to improve supply chain performance. The strategy may include rescheduling or rapid response. |
| 1503517 | Special Topics | - | The field of IT in general and the Business Analytics in particular changing rapidly, especially the increasing availability of data and new trends has been added. It is essential that graduate students should be aware of these changes and what are these trends. This course explores in depth to latest theoretical and practical aspects of business analytics, current issues and trends, methodologies and/or practice. After completing this course, students will explore to the state-of-the-art topics in business analytics. |
Career Path
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