University of Sharjah

What you'll study

The MSc in Data Science blends core theory with hands-on practise. Students take modules in probability and statistical inference, machine learning, data mining, and data management, alongside applied topics such as big data platforms, deep learning, natural language processing and data visualisation. Emphasis is placed on practical programming skills (Python/R), database design and cloud-based analytics.

The programme typically comprises a combination of taught courses and a substantial research or project component. Typical modules and subjects include:

• Statistical Methods for Data Science — probability theory, estimation, hypothesis testing and regression.
• Machine Learning — supervised and unsupervised methods, model selection, ensemble methods.
• Deep Learning — neural networks, CNNs, RNNs and transfer learning for unstructured data.
• Data Mining and Knowledge Discovery — pattern discovery, clustering and association rules.
• Big Data Systems — distributed storage and processing (Hadoop, Spark) and scalable architectures.
• Databases and Data Management — relational and NoSQL databases, data modelling and ETL processes.
• Natural Language Processing — text processing, information retrieval and language models.
• Data Visualisation and Communication — visual analytics, dashboards and storytelling with data.
• Research Methods and Ethics in Data Science — experimental design, reproducibility and responsible AI considerations.
• Capstone Project or Thesis — an independent research or industrial project that applies learned methods to a real dataset or problem.

Course delivery combines lectures, laboratory sessions in the university's computing facilities, and supervised research. Part-time and full-time study pathways may be available to accommodate working professionals.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree in computer science, information technology, statistics, mathematics, engineering or a closely related discipline. Degrees with substantial quantitative content are preferred. Candidates with relevant professional experience and a non-traditional but demonstrable quantitative background may also be considered.

Typical requirements include:

• A completed undergraduate degree with a satisfactory academic record from an accredited institution.
• Demonstrable proficiency in programming and quantitative methods — evidence can include prior coursework, transcripts or a portfolio of projects.
• English language proficiency for applicants whose prior education was not in English (standard tests or equivalent institutional proof may be requested).
• Some applicants may be asked to attend an interview or provide letters of recommendation and a statement of purpose outlining their interest and experience in data science.

Career prospects

Graduates from the MSc in Data Science go on to roles across private, public and research sectors. Common job titles include data scientist, machine learning engineer, data analyst, business intelligence developer, data engineer and research scientist. Employers span technology companies, finance, healthcare, telecommunications, oil and gas, government agencies and consulting firms.

The programme also prepares students for doctoral study in data science, machine learning and related computational disciplines. Practical project work and industry collaborations help build a portfolio useful for recruitment into applied analytics and AI teams.

Why study at University of Sharjah

The University of Sharjah offers a cosmopolitan campus environment and a faculty with expertise in computer science, engineering and applied mathematics. Students benefit from dedicated computing laboratories, access to cloud and big-data infrastructure, and opportunities to collaborate with regional industry partners and research centres.

Studying at the University of Sharjah provides exposure to application areas relevant to the Gulf region—such as smart cities, healthcare analytics and energy systems—while maintaining a curriculum aligned with international data science practices. The university supports student development through academic supervision, career services and opportunities for internships and funded research projects.