Master of Science in Data Analytics Engineering

1 Months On Campus Masters Program

American University of Sharjah

Program Overview

The Master of Science in Data Analytics Engineering is a two-year, full-time postgraduate programme that prepares you to harness data for strategic decision-making and engineering-level analytical solutions across industries like supply chain, healthcare, manufacturing and government. You’ll develop strong technical and managerial skills so you can extract insights from complex data and lead data-driven initiatives that support organisational improvement and digital transformation.

Curriculum Structure

Year 1 — Core Foundations & Analytical Skills:
In your first year, you’ll build solid analytical and engineering capabilities with core courses such as Data Analytics Engineering and Visualization, Optimization for Data Analytics Engineering and Predictive Analytics and Machine Learning, where you learn how to organise, process and model large data sets for meaningful insights and engineering decisions. These modules balance technical computation with analytical thinking, giving you the tools to tackle real-world challenges in data-rich environments.

Year 2 — Electives & Applied Expertise (Thesis Option):
In the second year, you deepen your expertise by choosing electives like Supply Chain Analytics, Big Data Analytics Engineering, AI in Industrial Engineering Systems and Emerging Technologies for Data Analytics, letting you tailor the programme to areas that match your career goals. If you choose the Thesis Option, you’ll also complete an independent Master’s Thesis where you apply advanced analytics methods to a significant engineering problem, demonstrating your ability to conduct research and deliver data-driven solutions.

Focus areas:
Data visualisation and interpretation; predictive modelling and machine learning; optimisation and big data analytics; application of analytics in engineering systems.

Learning outcomes:
You’ll graduate able to collect, process and analyse complex data sets; design data-driven solutions; communicate insights clearly; and lead data analytics initiatives that improve organisational performance.

Professional alignment (accreditation):
This MSc is accredited by the UAE Ministry of Higher Education & Scientific Research’s Commission for Academic Accreditation (CAA) and awarded by an internationally recognised American-style university, making it relevant for careers in analytics, engineering and digital transformation.

Reputation (employability):
AUS is a well-regarded private university in the UAE with a strong international student body and faculty; data analytics engineers from this programme are positioned for roles such as Data Analytics Engineer, Analytics Consultant, Predictive Modeller and Operations Optimisation Specialist across sectors in the UAE and internationally.

Experiential Learning (Research, Projects, Internships etc.)

Students develop applied expertise by conducting original research in a chosen specialization, utilizing advanced laboratory facilities, and engaging with industry and community-focused projects. This hands-on learning is facilitated by the university's research-oriented environment and partnerships. The experiential learning approach is implemented through several key components:

Primary Research Focus: The core practical component is a substantial Master's Thesis. Students must complete an independent research project (12 credit hours) that involves designing, implementing, and evaluating a novel solution to a computing problem under the supervision of a faculty advisor.
Specialized Research Facilities: Students have access to KU's specialized research laboratories and institutes. These include labs within the Center for Cyber-Physical Systems (C2PS), the KU Center for Autonomous Robotics Systems (KUCARS), and the KU Center for Digital Supply Chain and Operations Management, depending on their chosen specialization (e.g., Data Science, Robotics, Cybersecurity).
Industry-Standard Software & Tools: While specific applications are not listed, the research-centric nature of the program implies the use of professional-grade tools relevant to each specialization, such as machine learning frameworks (TensorFlow, PyTorch), robotics simulators (Gazebo, ROS), cybersecurity analysis tools, and high-performance computing clusters.
Industry and Community Engagement: The program encourages practical application through industrial projects and community service. Students may engage in collaborative projects with industry partners or apply their skills to address community needs, linking academic knowledge with real-world impact.

Progression & Future Opportunities

American University of Sharjah's MSc in Data Analytics Engineering prepares professionals to transform complex datasets into actionable insights through core training in optimization, predictive analytics, machine learning, and visualization, with blended learning delivery across 30 credit hours. Offered by the Department of Industrial Engineering, the program launches Fall 2026 with flexible thesis (7 courses + thesis) or course-only (10 courses) paths, targeting UAE's digital transformation needs in supply chain, healthcare, and manufacturing.

Progression & Future Opportunities

Graduates lead data-driven decision-making across UAE industries, applying engineering analytics to enhance operations, strategy, and innovation amid surging demand for skilled professionals. The curriculum's focus on ethical, sustainable solutions positions alumni for rapid advancement in data-intensive roles.

Typical roles: Data Analytics Engineer, AI Systems Analyst, Supply Chain Optimization Specialist, Predictive Modeling Lead.

University services: Graduate assistantships, career support, and industry networking through AUS College of Engineering.
Employment stats/salaries: AUS strong outcomes; UAE analytics roles AED 22,000-45,000/month starting.
Partnerships: Aligns with UAE AI/digital agendas; applied projects with regional organizations.
Accreditation value: AUS's US-model degree ensures global recognition and leadership mobility.
Graduation outcomes: Alumni optimize enterprise systems, drive AI implementations, inform policy through analytics.

Further Academic Progression

Thesis-track graduates qualify for AUS PhD in Engineering Systems Management or global doctorates in data science/AI, leveraging research for academic careers or executive R&D roles.