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Master of Business Analytics

Degree Summary

Program Outcomes

Program Structure

Faculty

Accreditation

Admission Requirements

Duration

12 - 18 months
9 - 12 subjects

Classes commence

September (Autumn)

January (Winter)

April (Spring)

Fees*

AED 9,415.35 / USD 2,565 per subject
*VAT 5% inclusive

The Information Age we live in today has compelled all businesses to undertake global disruption and transformation, driven by data and analytics.

A survey on the state of Data & AI from a list of 125 Fortune 1000 and Global Leadership Brands found that the percentage of organisations that had “created a data and AI-driven organisation” and “established a data and AI-driven organisational culture” both doubled (2X) in 2024.
Worldwide AI spending is expected to total $644 billion in 2025, an increase of 76.4% from 2024, according to a forecast by Gartner, Inc.
The global analytics market size was valued at USD 120.35 billion in 2024 and is projected to grow from USD 148.89 billion in 2025 to USD 600.46 billion by 2032.

The Master of Business Analytics curriculum is designed in collaboration with our industry partners to prepare students to develop sound knowledge and practical skills in data analytics for the challenges they will face as business leaders in the rapidly changing corporate environment. The program is taught as a combination of lectures and hands-on practical activities and case studies in our ‘Business Analytics Labs’ using state-of-the-art software used in large organisations. All ‘concepts’ will be delivered in the ‘context’ of business organisations across different industries namely retail, healthcare, banking, insurance, hospitality, travel and many others.

Theme 1 - Artificial Intelligence (AI): This includes techniques to simulate human decision-making through Machine Learning (ML) and Deep Learning (DL) algorithms. Students will learn to use ML and DL algorithms to solve various business problems such as pre-emptive prediction and prevention of fraud transactions in a bank, intelligent customer segmentation for marketing campaigns, and prevention of employee churn or burnout in organizations to name a few. Generative AI (GEN AI) and Large Language Models (LLMs) for business are also covered using Open AI’s GPT and Google Gemini models.
Learning Software used: SAS Viya, Python, R, Google Vertex AI and AI Studio

Theme 2 - Big Data: This includes the volumes of data lying around us including Structured Data, Semi Structured Data, Un-Structured Data. Students will learn to manage and use structured data from corporate database systems and spreadsheets, as well as with unstructured data from social media platforms, newspaper articles, corporate websites, images, and other forms of audio-visual data. Students will learn to build a data pipeline to ingest, store, clean, transform and process business data, whether in batch or real-time.
Learning Software used: Oracle Database for SQL, MongoDB for NOSQL, Google Cloud Storage and others

Theme 3 - Data Analysis and Visualization: This includes pre-processing, analyzing and visualizing data for gaining insights from complex information to make informed business decisions. Students will learn to build powerful data visualizations and interactive dashboards for communication with senior stakeholders and C-level suite of members and make recommendations about the direction of the business.
Learning Software used: Microsoft PowerBI, Microsoft Excel (Advanced), Google BigQuery

Theme 4 - Business Domain Knowledge: Students will gain knowledge of the business processes and functions such as Finance, Marketing, Operations etc. as well as macro factors such as Financial Markets, Consumer Markets and Policy Making, with a strong focus on sustainability and ethics.

Theme 5 - Industry-based practicum: This is an industry-based analytics project that is undertaken as the final component of the study program. Here, students will work on a real-life business case and develop an analytics solution in a live or a stimulated environment. Students will also gain access to the Google Cloud Platform (GCP) for Generative AI and Big Data Analytics solutions.
Students can choose to complete and achieve Professional Certificates from Google and/or Microsoft.

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Program Selection Guide

Entry Requirements

Students applying for a master's degree must have completed a bachelor's degree (or equivalent) at an accredited institution of higher education and have achieved the minimum grades outlined below:

Country	Direct Admission	Probationary Entry* GPA 2.5-2.99 or equivalent	Conditional Entry** GPA 2.0-2.49 or equivalent
Australia	65%	57%	50%
Brazil	7/10	6/10	5/10
China	75%	63%	50%
Colombia	3.5/5	3.25/5	3/5
Egypt	65% (Good)	57%	50% (Pass)
India	50%	45%	40%
	10 Point System: 7.0/10.0	10 Point System: 6.0/10.0	10 Point System: 5.0/10.0
	4 Point System: 3.0/4.0	4 Point System: 2.5/4.0	4 Point System: 2.0/4.0
Iran	14	13	12
Japan	70/100 or 4/5	65/100 or 3.5/5	60/100 or 3/5
Jordan	70%	60%	50%
Kenya	50%	45%	40%
Lebanon	11	10.5	10
Mexico	8/10	7/10	6/10
Nepal	50%	45%	40%
Nigeria	50%	45%	40%
Pakistan	50%	45%	40%
Russia	Average of 4	Average of 3.5	Average of 3
South Korea	80/100	75/100	70/100
UAE	3	2.5	2
UK	2^nd Class Honours with lower division (or min 50%)	45%	Pass (or min 40%)
USA	3	2.5	2

* Applicants with a bachelor’s degree in the relevant field of study, or its equivalent, with an overall GPA between 2.5 and 2.99 (on a 4.0 scale) may be conditionally admitted to a master’s program, only if they meet the necessary english language requirement. Such students must take a maximum of 18cp of subjects studied for the master’s degree during the period of conditional admission and must achieve an overall WAM of 55 in their first 3 subjects at the University. Students who fail to achieve this standard may have their enrolment discontinued.

** Applicants with a bachelor’s degree in the relevant field of study, or its equivalent, with an overall GPA between 2.0 and 2.49 (on a 4.0 scale) may be conditionally admitted to a master’s degree, only if they meet the necessary english language requirement and they will be required to complete a maximum of 18cp remedial non-credit bearing subjects prior to enrolment in their chosen master’s degree. Students must achieve a minimum WAM of 55 in these subjects in order to progress to the master’s degree or may have their enrolment discontinued.

Note:

[1] Unless otherwise stated, the entry requirements above are based on Section I institutions. Further details on alternate entry requirements can be obtained by contacting the Admissions department.
[2] As part of the admission requirements, prospective students for the Master of Business Analytics (MBAS) program will be required to provide a supporting statement of approximately 500 words of why they wish to enrol in the program, what benefits the program might provide them and what strengths they can bring to the program. Program Director may conduct an interview to assess the suitability of the application.

English Language Requirements

At UOWD, all classes are taught in English and in order to enrol in a master's degree you will need to submit one of the following certificate of English language proficiency:

English Language Test	Entry Score
IELTS Academic	Overall score of 6.0 with 6.0 in Reading and Writing and 5.0 in Listening and Speaking
TOEFL Internet based	79 with not less than 20 in Writing, 18 in Reading, 17 in Listening, 16 in Speaking
TOEFL Computer based	213 with a 4.0 TWE/Essay Writing
TOEFL* International paper based	550 with a 4.0 TWE/Essay Writing

Note: Students who have achieved an overall Academic IELTS score of 6, but with a minimum of 5 in reading and writing for their relevant program, may opt to take Academic Writing as an additional subject during their first semester at an additional fee of AED 1,050/USD 286. A 50% pass rate is required to clear the post enrolment condition. Alternatively, the student may opt to retake the IELTS test.

* Students admitted under this criterion must complete the test at AMIDEAST.

Graduate Foundation Course

If you hold a bachelor's degree from a non-business discipline, you may be required to undertake a Graduate Foundation Course (GFC). The course must be successfully completed in the first semester.

The GFC does not accrue any academic credit towards the master's degree but will provide you with a foundation in Statistics, that may be required for your chosen field of study.

Program Structure

The Master of Business Analytics requires the successful completion of 72 credit points of subjects. The Business Foundation Subjects should be completed within the first two sessions of study. However, if you have completed a bachelor's degree in Business or Commerce or a UOWD Graduate Certificate in Business, you may be able to complete the program in a total of nine (9) subjects in consultation with UOWD’s Faculty of Business office prior to the beginning of the program.

The capstone subject, BUS 922 Business Research Project should be completed in the final session of study.

Business, Management & Analytics Subjects

Foundation Subjects

This foundation subject is intended for those who need to obtain some introductory understanding of the principles of accounting and financial management. No previous knowledge or experience is assumed. The subject will introduce you to the role that effective financial management makes within an organization. The aim is to increase your proficiency in the use of the accounting data that you receive in your work environment, as well as making you aware of the basis on which key financial decisions are made. You will be introduced to the basic concepts of financial decision-making and the role of financial management in organizations to allow you to become better acquainted with the planning and controlling of resources you have at your disposal. You will also learn how business analytics is influencing the different subdomains within accounting and financial management of an organization.

The subject examines the concepts underpinning the marketing process and theories relevant to the study and practice of modern marketing science. Key concepts covered include the creation and delivery of customer value, customer retention and return on marketing investment, marketing's role in an organisation as defined by the overall strategic plan, and its integration with other functional disciplines within the organisation, elements of marketing plan, competitor analysis and strategies, marketing environment, marketing research, consumer and business buying behaviour, strategic nature of segmenting markets, targeting, and positioning brands, marketing mix decisions, and marketing ethics and social responsibility. The subject provides a foundation for the development of effective market-oriented managerial thinking, communication and team-work skills.

This course presents the foundations of Operations Management. Our focus is on understanding the design and management of conversion processes by which goods and services are transformed into products and services. This course is about managing resources and flows – flows of information, goods, services, materials, equipment, people and the accompanying costs. Goods and service providers must manage the highly interactive process of designing, producing, delivering, and disposing of products to assure success. In this course, the Business Analytics paradigm will be introduced; Business analytics combines data, technology, and mathematical models to help managers make better decisions, identify new opportunities, and become more competitive. Every aspect of business can benefit from using analytics, from understanding how customers behave to improving operation and financial performance. Combining Operations Management with Business Analytics will equip students with the mindset necessary to be able to classify various operations management problems, identify the nature of the information needed to be able to address the problem, translate these problems into the appropriate statistical and/or mathematical framework and interpret the results of the models in a verbal manner to the relevant stakeholders.

This subject introduces students to Business Analytics. Students explore the fundamental techniques and approaches utilised in organisations such as problem identification, data preparation, descriptive statistics, inferential statistics, data visualisation, and decision making. The focus of the subject is upon the application of Business Analytics techniques to create insight that provides knowledge that can support decision making. The subject provides a solid base for Statistical Analysis and Business Analytics and leverages Excel and SPSS software.

Specialisation Subjects

Students are required to choose an additional FOUR Subjects from list below
Fundamentals of Business Analytics (MBAS900) is a prerequisite for each subject on the list below
(SAS Certification may differ based on subjects selected)

This subject explores both storytelling and visualisation as a means of simplifying complex data to improve adoption of Business Analytics for decision making. The criticality of effective data visualisation and the role it plays globally to facilitate decision making and sharing of information is investigated in detail. Students explore world class concepts for visualisation of data and storytelling in a range of situations including dashboards and recurring or ad-hoc analysis. Knowledge and skills are developed using appropriate techniques and Excel and Power BI software

This subject provides a comprehensive introduction to predictive analytics with a focus on time series forecasting. Students will explore key concepts in time-series analysis, including decomposition techniques, smoothing methods, and regression-based forecasting. Emphasis is placed on understanding and applying these methods to real-world commercial problems, equipping students with the analytical skills to anticipate future trends and support decision-making in business operations. The subject incorporates hands-on experience with Python or R and/or similar suitable software enabling students to implement forecasting models and analyse performance using practical datasets from various industries, enabling students to implement forecasting models and analyse performance using practical datasets from various industries

In this subject, students will explore the use of Artificial Intelligence (AI), and particularly Machine Learning techniques to solve common business problems faced by different industries such as real estate, retail, healthcare, banking and others. The subject covers both supervised and unsupervised machine-learning models to solve business problems. Key topics include regression analysis, (including linear and logistic regression), tree models for regression and classification (CART), K-nearest neighbours and Support vector Machines (SVM) for classification, Clustering methods (such as K-means), time-series analysis techniques and introduction to Artificial Neural Networks.
Ancillary concepts and issues surrounding the building of models for business such as data wrangling and preparation, model validation and comparison, and feature selection techniques will also be covered. The subject will be taught using a combination of theoretical concepts, real-life business use cases and datasets as well as relevant industry-standard software platforms - SAS Viya and/or Python.

This subject explores in-depth problems related to services and operations. Topics related to services and operations include cloud service, eHRM, social media, online business, financial services automation, manufacturing, retails and logistics. Massive business datasets are used to explore a range of business analytics scenarios related to services and operations. Data mining, especially text mining, products profiling, predictive forecasting and predictive analysis are the main focus. Visual analytics, data mining and predictive/forecasting software tools are used in this subject.

This subject explores the usage of Big Data and Artificial Intelligence (AI) techniques to create customer insights as well as gain market intelligence using both structured and unstructured data using natural language processing (NLP) techniques. The subject covers concepts of customer lifetime value and KPI reporting as well as market research design. It demonstrates the power of customer insights through customer sentiment analysis and topic modelling using a range of big data sources such as social media posts, news articles and online customer reviews. The subject will also include advanced customer analysis techniques such as association rule mining to develop market-basket analysis for the retail industry as well as collaborative filtering to build product recommendation engines. The subject also delves into market intelligence by combining NLP techniques with time-series forecasting techniques to predict market performance using investor sentiments. The subject will conclude with a note on the importance of Responsible AI in the context of customers and markets. The subject will be taught using a combination of theory, industry case studies, live datasets as well as relevant industry-standard software platforms.

This subject focuses on prescriptive analytics techniques for business decision-making. Students will learn to formulate and solve optimisation problems using linear and integer programming, apply simulation methods (including Monte Carlo simulation), and use simulation-optimisation techniques to make informed business decisions. They will gain practical experience in using Excel, and solvers such as Gurobi (implemented in Python) to develop and implement these models.

Capstone

In this capstone subject, students will plan and undertake a strategic research project, applying appropriate research methods to design, collect, and analyse data on an approved business topic. They will draw on specialised knowledge from relevant discipline(s) to critically assess findings and develop innovative, evidence-based solutions to complex challenges. Throughout the subject, students will present their ideas and recommendations clearly to both specialist and general audiences through oral and written communication, while ensuring their decisions reflect principles of social responsibility.

Elective Subjects

Choose THREE Subjects from the list below

This subject deals with the issues facing digital marketers to establish the distinctly different environment in which consumers operate on digital platforms. This grounding is then used as a basis to build an understanding of the digital environment to key applications in marketing such as research, adding value in the areas of product, distribution, pricing and promotion. A key focus is the link between delivering positive user experiences and developing customer relationships over time using digital marketing platforms.

The subject introduces big data and marketing analytics tools and models for marketing decision making. The rise of big data creates an opportunity to make marketing more scientific and data driven. Using marketing theories and practical exercises, students learn various descriptive, predictive and prescriptive techniques. Based on case studies, this subject allows students to make marketing decisions by understanding the relationship between data, models and marketing outcomes.

This subject explores the transformative potential of cutting-edge Generative Artificial Intelligence (Gen AI) technologies in driving business decision-making and strategic innovation. Students will gain knowledge of artificial neural networks and deep learning, examining how these architectures enable the capabilities of Generative AI across varied business contexts. The subject will also introduce students to foundation models—specifically Large Language Models (LLMs) and image models, highlighting their roles in automating complex tasks such as natural language processing, visual recognition, and intelligent customer engagement. Through case studies and practical applications, students will critically analyse how these models are reshaping industries from finance to retail.
In the subject, students will focus on designing and deploying enterprise-scale AI solutions. Emphasis will be placed on understanding the architecture of scalable AI systems, managing pipelines, and ensuring model governance and ethical AI use. Learners will also be guided through the development of a comprehensive AI strategy and implementation plan, equipping them with the skills to align AI initiatives with organizational goals and assess Return on Investment. By the end of the course, students will be equipped not only to evaluate and use AI techniques effectively but also to lead AI-driven transformations in modern business environments.

With the rapid digital transformation in the workplace, the need for data-driven decision-making for managing people has become critical for organizations. This subject equips students with critical knowledge related to HR analytical models, methodologies and tools to support data-centric human resource decision-making. As companies increasingly identify the strategic value of HR analytics, this course will enhance students’ capabilities to identifying effective data sources and leveraging analytics to gain new insights in support of organizational strategy and tactics. The students will have exposure and hands on learning in different types of analytics where they will be positioned to address HR matters such as hiring top talent, workforce planning, performance and talent management, engaging the workforce, ensuring diversity, and managing retention using analytics. The students will be able to demonstrate advanced knowledge on pillars of HR analytics success, potential pitfalls, and importance of becoming a responsible HR analytics leader. By undertaking this course, the students will be industry-ready to engage in data-driven people management.

The role of data has become critical as a means of understanding contemporary business practises. Appropriate quantitative techniques are required to solve business and supply chain problems. This subject will introduce students to fundamental concepts associated with supply chain analytics in order to develop capabilities towards the analysis and interpretation of supply chain performance. By the end of this subject students will be able to make evidence-based decisions through a quantitative evaluation of complex supply chain systems.

Program Outcomes

Prestigious and Accredited Degree: Upon completion of the program, all students will get a Master’s degree from University of Wollongong Australia, a university in the top 1% of the world universities, accord to QS World University rankings. This degree is fully accredited in the UAE by Commission for Academic Accreditation (CAA) and Knowledge and Human Development Authority (KHDA).
Professional Certification: As a part of the SAS Joint Certification Program®, a collaboration of leading global universities using the industry leading vendor of analytics software, SAS upon completion of the Master of Business Analytics program, all students will get an Advanced Professional Certification from SAS.
Career-Ready Skills: The completion of the program will equip the students with practical, hands-on and job ready skills in business analysis such as software like Statistical Analysis and Systems (SAS) Viya, Structured Query Language (SQL) and Python. This will enable them to be productive from day one at work.
Doctoral Study Opportunities: Should students wish to pursue further research after completing this Master’s degree, UOW Dubai offers a PhD program in Business Analytics under the Faculty of Business.

The completion of the Master of Business Analytics program will open up a world of global career opportunities like

Data Scientist
Business Intelligence Analyst
Business Analyst
Data Engineer
Management Consultant
Market Research Analyst and many others

With the global nature of this degree, you will be equipped to work in any industry in private, semi private or government sector in any region of the world.

Accreditation and Recognition

All degrees at the University of Wollongong in Dubai are accredited by the Commission for Academic Accreditation (CAA) of the UAE Ministry of Education and are licensed by the Knowledge and Human Development Authority (KHDA). In addition, the degree is quality assured by UOW, which is registered with the Tertiary Education Quality and Standards Agency (TEQSA), the national regulator of the higher education sector in Australia.

Students will be issued a UOW Australia degree upon graduation.

Dr Osama Al-Hares

Associate Professor
Director (Postgraduate Programs)

Dr Osama Al Hares's research interests lie in financial performance, corporate valuation and value relevance, corporate governance, earnings management, accounting for goodwill and asset impairments, emerging financial markets, and accounting disclosure practices and analysis. He has extensive consultancy and training experience in banking, investment, services, and small businesses sectors.

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Master of Business Analytics

Degree Summary

Program Outcomes

Program Structure

Faculty

Accreditation

Admission Requirements

Duration

Classes commence

Fees*

Enquire Now

Program Selection Guide

View Program Structure

Program Structure

Foundation Subjects

Accounting and Financial ManagementACCY801 | 6 cp

Principles of Marketing ManagementMARK804 | 6 cp

Operations ManagementOPS 802 | 6 cp

Fundamentals of Business AnalyticsMBAS900 | 6 cp

Specialisation Subjects

Data Storytelling & Visual IntelligenceMBAS920 | 6 cp

Predictive Analytics and ForecastingMBAS921 | 6 cp

Applied Machine Learning & AI for BusinessMBAS922 | 6 cp

Big Data Management & IntegrationMBAS923 | 6 cp

AI-driven Customer Insights and Market IntelligenceMBAS924 | 6 cp

Prescriptive Analytics and Business Decision MakingMBAS925 | 6 cp

Capstone

Business Research CapstoneBUS 922 | 6 cp

Elective Subjects

Digital MarketingMARK901 | 6 cp

Big Data & Marketing AnalyticsMARK902 | 6 cp

Leveraging Generative AI for BusinessMBAS926 | 6 cp

Human Resource AnalyticsMGNT947 | 6 cp

Supply Chain AnalyticsOPS 910 | 6 cp

Program Outcomes

Accreditation and Recognition

Dr Osama Al-Hares

Faculty

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