Program Mission Statement

Preparing future data scientists to design scalable solutions and extract knowledge from complex real-world data systems.

 

Program Overview

  • The Master of Science in Data Science and Engineering at Hamad Bin Khalifa University prepares students with a strong foundation in data engineering, big data science, and advanced data analytics in different disciplines. The program integrates expertise from HBKU and its research institutes to address the full lifecycle of data, including collection, management, and scalable knowledge discovery.
  • Students develop fundamental knowledge in areas such as applied statistics, machine learning, and modern technological tools, including cloud platforms for large-scale data analysis. The program emphasizes hands-on experience through real-world projects focused on big data storage, processing, and mining, enabling students to extract meaningful insights from complex datasets.
  • Graduates complete either a research thesis or an industrial project, working on original contributions under the supervision of world-class faculty and in collaboration with leading research environments.

 

Program Objectives

The program aims to:

  • Provide students with a strong foundation in data science, engineering, and analytics to address complex real-world challenges.
  • Equip graduates with advanced knowledge and practical skills in big data technologies, machine learning, and scalable data-driven systems.
  • Foster innovation, research excellence, and interdisciplinary collaboration through engagement with HBKU’s researchers, research institutes and industry partners.
  • Prepare graduates to design, develop, and deploy data-driven solutions that contribute to societal and economic impact in Qatar and globally.
  • Support the development of ethical, responsible, and lifelong learners capable of advancing the state of the art in data science and engineering.

This program is offered in both standard (2 years) and accelerated (1 year) formats.

Language

English

Program Duration

2 Years

Certification

Masters

Program Structure

The MSc in Data Science and Engineering is a 33-credit program delivered over approximately two years, with options for an accelerated pathway. It is structured to provide a balanced combination of foundational knowledge, advanced specialization, and practical experience.

The program includes a set of core courses covering key areas such as applied statistics, applied data analytics, and advanced data management systems, ensuring a coherent learning foundation for students from diverse backgrounds. Students complement this foundation with elective courses that allow specialization in topics such as machine learning, cloud computing, and emerging areas in data science.

In addition, students participate in research seminars to broaden their exposure to current developments. The program culminates in either a research thesis or an industrial project, enabling students to apply their knowledge to real-world problems.

Curriculum Overview

The curriculum of the MSc in Data Science and Engineering is designed to shape the next generation of data scientists and engineers capable of driving innovation in a data-driven world. It combines strong theoretical foundations in applied statistics, data analytics, and advanced data management with cutting-edge topics such as machine learning and scalable cloud technologies.

Through flexible electives, students tailor their learning to emerging domains and future career paths. The program emphasizes hands-on experience, enabling students to work on real-world problems and transform data into actionable knowledge. It culminates in a research thesis or industrial project, preparing graduates to lead the development of intelligent, data-driven solutions across disciplines.

Course Categories 

  • Core Courses (12 CH): Foundational courses covering statistics, data analytics, research methods, and advanced data management systems
  • Elective Courses (12 CH) : A broad selection of advanced and specialized topics such as machine learning, deep learning, cloud computing, bioinformatics, health informatics, and emerging data science areas
  • Graduate Research Seminars: Seminar series exposing students to current research trends, industry practices, and interdisciplinary topics
  • Thesis / Industrial Project (9 CH): Capstone experience through either a research thesis or a practical industrial project combined with an elective
  • Non-Course Requirements: Thesis defense and research dissemination, including preparation of a high-quality publication
Study Plan

Number of Career Pathways 
2

1st Pathway

Research Pathway

1st Pathway Description

Students complete a Master’s Thesis (ICT 695) and must produce scientifically sound research, typically leading to a publication if possible

2nd Pathway Title

Professional Pathway

2nd Pathway Description

Students complete an Industrial Project (ICT 698) combined with an elective, focusing on real-world, applied data science problems

Program Educational Objectives

  • Apply advanced knowledge and skills in data science, machine learning, and data engineering to solve complex real-world problems across diverse domains.
  • Design and implement scalable data-driven systems, leveraging modern tools and technologies for data collection, processing, and intelligent decision-making.
  • Conduct impactful research and innovation, contributing to scientific advancement through publications, applied projects, or the development of novel methodologies.
  • Collaborate effectively in interdisciplinary and professional environments, engaging with academia, industry, and research institutes to address societal and economic challenges.
  • Demonstrate ethical responsibility and lifelong learning, adapting to emerging technologies and leading innovation in a rapidly evolving data-driven world.

Student Outcomes

  • Apply advanced knowledge of mathematics, statistics, and computing to model, analyze, and solve complex data-driven problems.
  • Design and develop data-driven systems and solutions that meet specified requirements, considering scalability, efficiency, and real-world constraints.
  • Conduct experiments, analyze and interpret data, and draw meaningful conclusions to support informed decision-making.
  • Communicate effectively with both technical and non-technical audiences through reports, presentations, and data visualizations.
  • Function effectively in multidisciplinary teams, contributing to collaborative problem-solving in academic, industrial, and research environments.
  • Apply ethical and professional principles, considering societal, environmental, and economic impacts of data science technologies.
  • Recognize the need for continuous learning, adapting to emerging technologies and advancing knowledge in data science and engineering.

Academic Requirements

To graduate with the MSc in Data Science and Engineering, students must successfully complete a total of 33 credit hours (CH), including core courses, electives, and a capstone experience.

Students are required to complete 12 CH of core courses, providing a strong foundation in statistics, data analytics, and data management, along with 12 CH of elective courses that allow specialization in various advanced topics.

In addition, students must pass the Graduate Research Seminar (0 CH). The program culminates in either a research thesis (9 CH) or an industrial project (6 CH) combined with an elective, followed by a thesis or project defense.

Students are also expected to produce a high-quality research output, such as a conference or journal publication, as part of their graduation requirements.

English Language Requirements

All applicants whose first language is not English must demonstrate proficiency in English by submitting a valid standardized test score. The minimum required score is typically 6.5 on the IELTS (Academic) or 79 or above on the TOEFL (iBT).

Test scores must be valid at the time of application submission (usually within two years of the test date).

Applicants may be exempt from this requirement if they provide official documentation confirming that their undergraduate degree was completed at an institution where the language of instruction is English.

Additional English language qualifications or equivalent certifications may be considered in line with university admission policies.

Standardized Tests (if applicable)

GRE / GMAT (Optional / Program-Dependent):
Submission of GRE or GMAT scores may be encouraged for some applicants to strengthen their application, but is generally not mandatory for admission to the MSc in Data Science and Engineering

Required Documents

Applicants to the MSc in Data Science and Engineering must submit a complete application package, including the following documents:

  • Completed Online Application Form
  • Official Academic Transcripts from all previous institutions
  • Bachelor’s Degree Certificate (or equivalent qualification)
  • Curriculum Vitae (CV) / Resume highlighting academic and professional experience
  • Statement of Purpose outlining academic interests, goals, and motivation
  • Two Letters of Recommendation from academic or professional referees
  • Proof of English Language Proficiency (IELTS, TOEFL, or equivalent), if applicable
  • Valid Passport or Identification Document

Student Funding Information 

HBKU offers a range of competitive funding opportunities to support students enrolled in the MSc in Data Science and Engineering. Many admitted students may be considered for full or partial scholarships, subject to availability and merit. •Tuition Waivers: Full or partial coverage of tuition fees may be awarded to qualified students based on academic excellence. •Stipend Support: Eligible students may receive a monthly stipend to assist with living expenses during their studies. •Research and Conference Funding: Financial support may be provided for research activities, publications, and participation in international conferences. •External Funding Opportunities: Students are encouraged to apply for competitive awards such as the Qatar Graduate Sponsorship Research Award (GSRA), which offers comprehensive support including tuition and research funding. Funding packages are awarded on a competitive basis and may vary depending on the applicant’s profile, academic performance, and availability of resources.

Program Accreditation

  • Institutional Accreditation: 
    HBKU is officially licensed and recognized by Qatar’s Ministry of Education and Higher Education, ensuring that all degrees meet national standards and are internationally recognized.
  • Quality Assurance and Continuous Improvement: 
    The university maintains rigorous academic standards through internal quality assurance processes led by its Office of Institutional Effectiveness, which oversees assessment, evaluation, and compliance with accreditation standards.
  • International Alignment: 
    HBKU operates within a global academic ecosystem under Qatar Foundation, with strong research collaborations and partnerships with leading international institutions, supporting alignment with international best practices

Program Coordinator Name

Samir Brahim Belhaouari

Coordinator Email

sbelhaouari@hbku.edu.qa