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Master of Science in Business Analytics

College of Business Administration
Study System
Thesis and Courses
Total Credit Hours
33 Cr. Hrs
2-4 Years
Fall & Spring
Sharjah Main Campus
Study Mode
Full Time and Part Time

Master of Science in Business Analytics

The College of Business Administration, in collaboration with the College of Computing and Informatics, is offering a Master of Science (M.Sc.) degree in Business Analytics, which is a credit hour-based graduate degree designed to prepare students for academic and practical careers in the emerging and vital field of dealing with large sets of data. The M.Sc. program will 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 implementation of this program is consistent with the vision and mission of the University of Sharjah (UoS), which is to educate through the educational process, to further human discovery through research, and to enhance wellbeing through involvement with the community. The University of Sharjah will benefit from the M.Sc. in Business Analytics in many ways: 
  • Contributes to the expansion of UoS research activities, 
  • Brings closer the IT industry and business in UAE and the region to UoS through expected collaboration on pressing IT problems affecting and directly related to the region, 
  • Creates greater synergy between academia and the local/regional industry, 
  • Brings additional international recognition to the University. 

The M.Sc. graduates, therefore, will contribute to the needs of the UAE and the region for well-qualified individuals in the field of Business Analytics.

Program Objectives
Upon the successful completion of the program, student will be able to:

  1. Integrate theoretical foundation and quantitative skills in Business Analytics.
  2. Apply the concepts and methods of business analytics to real life scenarios.
  3. Interpret results and solutions and identify appropriate action for managerial situations and decision making.
  4. Work effectively as a member of a team and communicate effectively with a variety of audiences.
  5. Use leadership and responsible behavior in a group setting.
  6. Identify and manage ethical challenges in their career.

Special Admission Requirements
To be admitted to the M.Sc. in Business Analytics Program the following requirements:

  1. The applicant must have a bachelor's degree in Business, Information Systems, Computer Science and Engineering (or a closely related field) from a recognized college or university.
  2. The undergraduate degree should be in a subject that will qualify students for the graduate specialization of their choice. Otherwise, students may be admitted upon the recommendation of the Department and after their study for required prerequisite courses assigned by the Department.
  3. These candidates are required to enroll in prerequisite courses, which they have not taken in their prior studies, as deemed necessary by the Department's “Graduate Studies Committee" and approved by the College and University. These prerequisite courses should be completed within no more than two semesters (Full-Time) and will not be considered as part of the required credit load for the graduate degree.
  4. To be admitted to the Master's Degree Program, the candidate shall have obtained a CGPA of at least (3.0) out of (4.0), or its equivalent, for his/her first university degree. Students who have obtained at least (3.0), and no less than (2.5) out of (4.0), may receive conditional admissions in accordance with the executive regulations of the University.
  5. Applicants must provide certified transcripts from the institution where they received their B.Sc. degree, along with course descriptions, and must provide letter(s) of reference.
  6. Candidates are required to demonstrate English language proficiency by obtaining a minimum of 550 on the Institutional TOEFL (administered at the University of Sharjah) or its equivalent on the iBT or CBT; or 6 on the academic IELTS for programs taught in English. Students may be admitted conditionally if they obtain 530 or higher on TOEFL if they enroll in an English language course and receive a TOFEL score of 550 by the end of their first semester of study. Students who do not meet these two conditions will be dismissed from the program.

    Applications will be reviewed and recommended for acceptance by the “Research and Graduate Studies Committee" by the Department and approved by the “Department Council".


    Full-time candidates for the Master's degree must complete their requirements within a minimum of 3 semesters and a maximum of 8 semesters from the date they are admitted into the program. The admission requirements as cited above are almost like all the other Science Master's programs at the University of Sharjah.


    Part-time candidates for the master's degree must complete their requirements within a minimum of 6 semesters and a maximum of 10 semesters from the date they are admitted into the program (This does not include the allowed postponed semesters).  The admission requirements as cited above are almost like all other Science Master's programs at the University of Sharjah.
Program Structure & Requirements
The Master program consists of 33 credit hours distributed as follows.

Requirements Compulsory Elective Total
Courses Credit Hours Courses Credit Hours Courses Credit Hours
Courses 6 18 2 6 8 24
9 0 0 1 9
Total Credit Hours 27 6 33

Study Plan
Study Plan: Course List
  1. Compulsory courses (18 credit hours)
  2. Elective Courses (6 credit hours)
  3. Thesis (9 credit hours)
  4. Study Plan: Course Distribution

Course Code Course Title Credit Hours Pre-requisite
Compulsory Courses
1503510 D​atabases 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
Advanced Business Analytics 3 1503510 and 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
0307522 Business Elective Course (MBA):
Managing Operations

FIRST YEAR                                                                                    

​Fall Semester 
Spring Semester
Course # ##
Cours​​e Title Cr.Hr Course # Course Title Cr.Hr
1503510 Databases and Business Intelligence 3 1503540 Data Mining 3
1503520 Fundamentals of Business Analytics 3 1503550 Optimization and Decision Models 3
0308530 Statistics and Forecasting 3 0302560 Research Methods 3
  Total 9   Total 9

​Fall Semester
Spring Semester
Course #

Course Title Cr.Hr Course # Course Title Cr.Hr
0302621 Thesis 3 0302621 Thesis 6
  Elective Course 3      
  Elective C​ourse
  Total 9   Total 6

Course Description

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.

1503510 Databases & 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.

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.

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. 


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.

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.

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.

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.