Bachelor of Data Science
Admission to Program
Admission to the program is explained in the HCT Admission Policy described in the Academic Policies section of this Catalogue.
Program Mission
Prepare graduates to be Information Communication Technology (ICT) professionals in technical and organizational leadership roles, embracing innovation and discovery and striving for professional growth through lifelong learning in fields associated with Data Science, with a strong focus on enhancing Information Systems.
Program Description
The Bachelor of Data Science program is designed to equip students with the necessary knowledge and skills to apply ethical values to complex and unpredictable problems, and to plan, design, implement, evaluate, and manage an organization’s ICT infrastructure.
The program provides a comprehensive understanding of information technology assets, archival, and information processing systems within the context of data science applications. Throughout their studies, students will develop proficiency in fundamental concepts and skills across various information technologies, preparing them for roles where they can harness the power of data to drive organizational success.
The Bachelor of Data Science program is structured as a set of cores, elective, general studies, and specialized courses. Within the core curriculum, students gain fundamental knowledge, skills, and competencies crucial for Information Systems, which are further enhanced by specialized courses aligned with current industry trends in Data Science. To integrate theoretical knowledge with practical experience, the program offers a year-long apprenticeship, enabling students to acquire valuable real-world skills. This holistic approach ensures graduates are well-prepared to succeed in the dynamic field of Data Science.
Program Goals
- Equip graduates with the necessary technical knowledge and skills to design and develop data-driven solutions to specific business challenges in accordance with industry best practices in data science.
- Prepare graduates for a successful career as effective decision makers with strong communication and teamwork skills and an understanding of global, ethical, and social implications of the industry and Data Science profession.
- Prepare graduates with technical and entrepreneurial leadership qualities, who support the development of innovative computing solutions in response to local, regional, or global challenges.
- Equip graduates with strong commitment to lifelong learning, continuing education, and professional growth.
Program Learning Outcomes
1. Demonstrate an understanding of critical analysis, research systems and methods, and evaluative problem- solving techniques, showing familiarity with sources of current and new research in the field of computing.
2. Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
3. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program's discipline.
4. Communicate effectively in a variety of professional contexts.
5. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
6. Function effectively as a member or leader of a team engaged in activities appropriate to the program's discipline.
7. Apply theory, techniques, and tools throughout the data science lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
Completion Requirements
Bachelor of Data Science
Students must successfully complete a minimum of 120 credits, including:
| Code | Title | Credit Hours |
|---|---|---|
| CIS Core Courses | 30 | |
| Data Science Specialisation Courses | 66 | |
| Elective Courses | 6 | |
| General Studies | 18 | |
| Total Credits | 120 | |
To qualify for the bachelor's degree, a student is required to:
- Successfully complete the required number of credits and courses specific to the program with a minimum cumulative GPA of 2.0.
- Complete 100 hours of volunteering.
- Meet the residency requirement that a minimum of 50% of the program credit requirements have been completed at the HCT.
Note: Exit with Diploma
Students who have completed the 60 credits of the program may exit with a Diploma in Communication and Information Technology (typically after year 2).
| Code | Title | Credit Hours |
|---|---|---|
| CIS Core Courses | ||
| Required Credits: 30 | ||
| CIS 1203 | Web Technologies | 3 |
| CIS 1213 | Introduction to Information Security | 3 |
| CIS 1303 | Database Systems | 3 |
| CIS 1313 | Introduction to Computer Systems and Networks | 3 |
| CIS 1603 | Programming I | 3 |
| CIS 1613 | Programming II | 3 |
| CIS 2023 | Applied Discrete Mathematics | 3 |
| CIS 2033 | User Centered Design | 3 |
| CIS 3503 | Technopreneurship | 3 |
| CIS 3603 | Project Management | 3 |
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Specialisation Courses | ||
| Required Credits: 66 | ||
| CDS 2313 | Introduction to Data Science | 3 |
| CDS 2323 | Data Engineering | 3 |
| CDS 2413 | Programming for Data Science | 3 |
| CDS 2423 | AI Workflow Automation | 3 |
| CDS 2433 | Business Process Modeling and Optimization | 3 |
| CSE 2623 | Algorithms and Data Structures | 3 |
| CDS 3513 | Data Mining Techniques | 3 |
| CDS 3523 | Statistical Inference | 3 |
| CDS 3533 | Big Data Analytics | 3 |
| CDS 3543 | Data visualization for Decision making | 3 |
| CDS 3613 | Enterprise Solution Management | 3 |
| CDS 3623 | Data Mining for Enterprise Solutions | 3 |
| CDS 3633 | Machine Learning for Business Analytics | 3 |
| CDS 3643 | Time Series Analysis and Forecasting | 3 |
| CDS 4716 | Apprenticeship I (*) | 6 |
| CDS 4723 | Capstone Project I | 3 |
| CDS 4733 | Business Process Automation | 3 |
| CDS 4816 | Apprenticeship II (*) | 6 |
| CDS 4823 | Capstone Project II | 3 |
| CDS 4833 | IT and Data Strategy and Governance | 3 |
| *Apprenticeship Courses | ||
| Code | Title | Credit Hours |
|---|---|---|
| Elective Courses | ||
| Required Credits: 6 | ||
| CIB 4003 | E Business Applications Development | 3 |
| CIS 4003 | Green Computing | 3 |
| CIS 4703 | Blockchain Technology | 3 |
| CSF 4613 | Security Intelligence | 3 |
| Code | Title | Credit Hours |
|---|---|---|
| General Studies | ||
| Required Credits: 18 | ||
| AES 1003 | Emirati Studies | 3 |
| LSM 1013 | Mathematics for Computing | 3 |
| LSC 1103 | Professional Written Communication | 3 |
| LSS 1133 | AI Literacy and Critical Inquiry | 3 |
| CIS 1703 | Introductory Statistics and Probability | 3 |
| CIS 1813 | Artificial Intelligence Fundamentals | 3 |
Note:
Students who have completed the 60 credits of the program may exit with a Diploma in Communication and Information Technology (CIT)
| Description | Data |
|---|---|
| Total Required Credits | 120 |
| Maximum Duration of Study | 5 years |
| Minimum Duration of Study | 4 years |
| Cost Recovery Program | No |
| Program Code | BADSC |
| Major Code | CDS |
Recommended Sequence of Study
| Year 1 | ||
|---|---|---|
| Semester 1 | Credit Hours | |
| LSC 1103 | Professional Written Communication | 3 |
| LSM 1013 | Mathematics for Computing | 3 |
| CIS 1203 | Web Technologies | 3 |
| CIS 1313 | Introduction to Computer Systems and Networks | 3 |
| CIS 1603 | Programming I | 3 |
| Credit Hours | 15 | |
| Semester 2 | ||
| CIS 1213 | Introduction to Information Security | 3 |
| CIS 1303 | Database Systems | 3 |
| CIS 1613 | Programming II | 3 |
| CIS 1703 | Introductory Statistics and Probability | 3 |
| CIS 1813 | Artificial Intelligence Fundamentals | 3 |
| Credit Hours | 15 | |
| Year 2 | ||
| Semester 3 | ||
| LSS 1133 | AI Literacy and Critical Inquiry | 3 |
| CIS 2023 | Applied Discrete Mathematics | 3 |
| CIS 2033 | User Centered Design | 3 |
| CDS 2313 | Introduction to Data Science | 3 |
| CDS 2323 | Data Engineering | 3 |
| Credit Hours | 15 | |
| Semester 4 | ||
| AES 1003 | Emirati Studies | 3 |
| CDS 2413 | Programming for Data Science | 3 |
| CDS 2423 | AI Workflow Automation | 3 |
| CDS 2433 | Business Process Modeling and Optimization | 3 |
| CSE 2623 | Algorithms and Data Structures | 3 |
| Credit Hours | 15 | |
| Year 3 | ||
| Semester 5 | ||
| CDS 3513 | Data Mining Techniques | 3 |
| CDS 3523 | Statistical Inference | 3 |
| CDS 3533 | Big Data Analytics | 3 |
| CDS 3543 | Data visualization for Decision making | 3 |
| CIS 3603 | Project Management | 3 |
| Credit Hours | 15 | |
| Semester 6 | ||
| CDS 3613 | Enterprise Solution Management | 3 |
| CDS 3623 | Data Mining for Enterprise Solutions | 3 |
| CDS 3633 | Machine Learning for Business Analytics | 3 |
| CDS 3643 | Time Series Analysis and Forecasting | 3 |
| CIS 3503 | Technopreneurship | 3 |
| Credit Hours | 15 | |
| Year 4 | ||
| Semester 7 | ||
| CDS 4716 | Apprenticeship I | 6 |
| CDS 4723 | Capstone Project I | 3 |
| CDS 4733 | Business Process Automation | 3 |
| 4000 Level Elective | 3 | |
| Credit Hours | 15 | |
| Semester 8 | ||
| CDS 4816 | Apprenticeship II | 6 |
| CDS 4823 | Capstone Project II | 3 |
| CDS 4833 | IT and Data Strategy and Governance | 3 |
| 4000 Level Elective | 3 | |
| Credit Hours | 15 | |
| Total Credit Hours | 120 | |
Note:
Students who have completed the 60 credits of the program may exit with a Diploma in Communication and Information Technology (CIT)
| PCQ Title | Courses |
|---|---|
| Certified Associate in Project Management (CAPM) | CIS 3603 Project Management |
Ahmed Bani Mustafa, Ph.D., Artificial Intelligence/Data Science, University of Wales (Aberystwyth University), UK
Aisha Ghazal Fateh Allah, MSc., Data and Information Management (Informatics), The British University in Dubai, UAE
Aiswarya Babu, MSc., Artificial Intelligence, Heriot-Watt University, UAE
Akram Al-Kouz, Ph.D., Data Science & Artificial Intelligence, Technical University of Berlin, Germany
Alexandros Alexandropoulos, Ph.D., Computer Science, University of Manchester, UK
Amala Rajan, Ph.D., Computer Science & Engineering, Middlesex University, United Kingdom
Ammar Alrefai, MSc., Data Science, Rochester Institute of Technology, USA
Anang Hudaya Bin Muhamad Amin, Ph.D., Artificial Intelligence, Monash University, Australia
Anas Arram, Ph.D., Optimization and Data Mining, National University of Malaysia, Malaysia
Anatoliy Lut, MSc., Computer Science, University of Electronic Science and Technology of China, China
Asad Safi, Ph.D., Artificial Intelligence/Medical Imaging, Technical University of Munich (TUM), Germany
Asem Kasem, Ph.D., Computer Science, University of Tsukuba, Japan
Asem Omari, Ph.D., Computer Science, University of Düsseldorf, Germany
Ashraf Abou Tabl, Ph.D., Data Science & Artificial Intelligence, University of Windsor, Canada
Aya Alanani, MSc., Artificial Intelligence and Computer Science, University of Birmingham, UK
Eslam Badran, Ph.D., Computer Science – Artificial Intelligence, The University of Staffordshire, United Kingdom
Eyad Marazqah Btoush, Ph.D., Artificial Intelligence, University of Southern Queensland, Australia
Faouzi Bouslama, Ph.D., Electronic Engineering, Shizuoka University, Japan
Fatema Abdulla Mohammed Ghallab Ali, MSc., Information System Management, Higher Colleges of Technology, UAE
Fatimah Ishowo-Oloko, Ph.D., Artificial Intelligence, Khalifa University, UAE
Ghazala Bilquise, Ph.D., Computer Science, The British University in Dubai, UAE
Hassan Migdadi, Ph.D., Computer and Communication Engineering, University of Bradford, UK
Hatem Tamimi, Ph.D., Management Information Systems, Anglia Ruskin University, United Kingdom
Heba Mohammad, Ph.D., Electronic Business, University of Salento, Italy
Imad Ahmed, Ph.D., Intelligent Systems and Networks, University of Leeds, United Kingdom
Jaber Jemai, Ph.D., Computer Information Systems, University of Tunis, Tunisia
Jess Sanchez, MSc., Applied Mathematics, University of Southern Philippines, Philippines
Jim Otieno, Ph.D., Enterprise Systems, Middlesex University, United Kingdom
Keletso Letsholo, Ph.D., Computer Science/Software Engineering, University of Manchester, United Kingdom
Khaled AlMiani, Ph.D., Computer Science, The University of Sydney, Australia
Khawla Abdulla Al Shehhi, MSc., Information System Management, Higher Colleges of Technology, UAE
Lama Al Ibaisi, MSc., Management Information Systems, University of Wollongong in Dubai, UAE
Lina Daouk, MSc., Instructional Technology, New York Institute of Technology, USA
Loay Alzubaidi, Ph.D., Computer Science & Engineering, Vienna University of Technology, Austria
Melina Silva, MBA, Management of Information Technology, Nanyang Technological University, Singapore
Mohamad Jaber, Ph.D., Computer Science, University of Nice, France
Mohammed Abdul Rahim, Ph.D., Computer Science, Anglia Ruskin University, United Kingdom
Mohammed Alshehhi, MSc., Data Science, University of Birmingham, United Kingdom
Mohammed Hassouna, Ph.D., Information Systems and Computing, Brunel University London, UK
Mohd Salihan Ab Rahman, Ph.D., Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia
Muhammad Wannous, Ph.D., Computer Science and Electrical Engineering, Kumamoto University, Japan
Mustafa Akpinar, Ph.D., Computer and Information Engineering, University of Sakarya, Türkiye
Nasser Nassiri, Ph.D., Virtual Reality, Leeds Metropolitan University, United Kingdom
Neha Gupta, MSc., Physics and Computer Applications, Guru Nanak Dev University, India
Nishant Singh, MSc., Data Science, Boston University, USA
Nor Azizah Hitam, Ph.D., Computer Science, International Islamic University Malaysia, Malaysia
Nour Abujabal, MSc., Computer Engineering, University of Sharjah, UAE
Nour Haruni, Bachelor's, Computer Engineering, University of Sharjah, UAE
Noura Helal Alnuaimi, Ph.D., Information Technology, United Arab Emirates University, UAE
Nur Siyam, Ph.D., Computer Science, The British University in Dubai, UAE
Omar Abuzaghleh, Ph.D., Computer Science and Engineering, University of Bridgeport, USA
Oussama Hamid, Ph.D., Agentic AI (Reinforcement Learning), Otto-von-Guericke University, Germany
Pedro Flores, Ph.D., Information Technology, St. Paul University, Philippines
Rachael Miller, MSc., Mathematics Teaching, Duke University, United States
Raiza Borreo, Ph.D., Information Technology - Data Mining, AMA University, Philippines
Raj Kamal, Masters (Computer Applications), Indian Institute of Technology, Delhi, India
Ramakrishnan Raman, MSc., Computer Science & Engineering, Anna University, India
Reem Atassi, Ph.D., Data Science, Sapienza Università di Roma, Italy
Rejitha Ravikumar, MSc., Operations Research and Computer Applications, National Institute of Technology Tiruchirappalli, India
Rima Dessi', Ph.D., Computer Science, Karlsruhe Institute of Technology, Germany
Sharmila Siddartha, MSc., Data Science, The British University in Dubai, UAE
Shazia Asif, MSc., Business Administration, University of Strathclyde, United Kingdom
Shinju Philip, Ph.D., Computer Applications, Karunya University, India
Suaad Al Mansoori, Ph.D., Computer Science, The British University in Dubai, UAE
Sujni Paul Arulraj, Ph.D., Computer Applications, Karunya University, India
Syed Habeebullah Shah Khan, MSc., Electrical Engineering and Applied Physics/AI, Case Western Reserve University, USA
Thaeer Kobbaey, Ph.D., Computer Science, De Montfort University, United Kingdom
Vishwesh Akre, Ph.D., Software Engineering, University of Salford, United Kingdom
Yun-Ke Chang, Ph.D., Information Science, University of North Texas, USA
Zainab Ibrahim, Ph.D., Data Science, University of Portsmouth, United Kingdom
Zakea IL-Agure, Ph.D., Computer Science/Artificial Intelligence, Staffordshire University, United Kingdom
Zamhar Ismail, Ph.D., Informatics, The University of Manchester, United Kingdom
Ziad Rafhi, Ph.D., Education - Higher Education, University of Liverpool, United Kingdom