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Master of Science in Data Science
Master of Science in Data Science
The Master of Science in Data Science is a cutting-edge program designed to produce highly skilled professionals and researchers capable of unlocking value from data across diverse domains. The program provides rigorous training in statistics, mathematics, computer science, and applied data analytics while integrating real-world problem-solving, ethical considerations, and emerging technologies.
Introduction
The Master of Science in Data Science is a cutting-edge program designed to produce highly skilled professionals and researchers capable of unlocking value from data across diverse domains. The program provides rigorous training in statistics, mathematics, computer science, and applied data analytics while integrating real-world problem-solving, ethical considerations, and emerging technologies.
In Somalia and the wider Horn of Africa, digital transformation, financial services, public health systems, and e-government are generating unprecedented volumes of data. Skilled data scientists are urgently needed to extract insights that support governance, business innovation, and social development. Globally, demand for data science experts continues to expand in industries such as finance, healthcare, telecom, energy, and artificial intelligence. This program equips graduates to meet both local and international workforce and research needs.
Specific Objectives
The program aims to:
- Provide advanced training in data science theory, tools, and applications.
- Develop proficiency in programming, statistical modelling, machine learning, and big data analytics.
- Foster critical thinking, problem-solving, and ethical use of data in complex contexts.
- Enable students to conduct independent research in applied data science.
- Prepare graduates for leadership roles in industry, government, academia, and international organisations.
Expected Learning Outcomes
Graduates of the program will be able to:
- mApply statistical, algebraic, and probabilistic concepts in data-driven modeling.
- Design, implement, and evaluate machine learning algorithms.
- Analyze text, vision, and speech using natural language processing and multimodal data analytics.
- Handle, process, and interpret large datasets using big data tools.
- Visualise data effectively with R, Power BI, and other platforms.
- Conduct predictive and business analytics for organisational decision-making.
- Perform social media analytics for communication, policy, and marketing insights.
- Plan, execute, and defend a research project in applied data science.
Exam Regulations
Assessment emphasizes both continuous learning and summative evaluation. Typical components and indicative weight ranges include participation (5–10%), assignments (15–20%), group projects/presentations (15–25%), case analyses (10–15%), and final examinations (20–30%). The capstone/research component is assessed independently (100% of its 6 credits). Many course outlines specify that students must pass both midterm/continuous assessment and the final exam to pass the course (commonly 60% coursework, 40% final). Progression follows successful completion of semester requirements and maintenance of satisfactory standing per university policy.
Award of the Degree
To be awarded the
Master of Science in Data Science, a candidate must:
- Complete core, specialization, electives, and thesis with the minimum GPA required.
- Satisfy ethical and research compliance requirements.
- Demonstrate English proficiency (B2 via Linguaskill or approved IELTS/TOEFL).
- Clear all financial and administrative obligations.
The Master of Science in Data Science is a cutting-edge program designed to produce highly skilled professionals and researchers capable of unlocking value from data across diverse domains. The program provides rigorous training in statistics, mathematics, computer science, and applied data analytics while integrating real-world problem-solving, ethical considerations, and emerging technologies.
In Somalia and the wider Horn of Africa, digital transformation, financial services, public health systems, and e-government are generating unprecedented volumes of data. Skilled data scientists are urgently needed to extract insights that support governance, business innovation, and social development. Globally, demand for data science experts continues to expand in industries such as finance, healthcare, telecom, energy, and artificial intelligence. This program equips graduates to meet both local and international workforce and research needs.
Study Program (Structure & Credits)
Code | Course | Credit |
DS7101 | Principles and Practices of Data Science and Analytics | 3 |
DS7102 | Statistics for Data Science | 3 |
DS7103 | Natural Language Processing | 3 |
DS7201 | Machine Learning | 3 |
DS7202 | Big Data Analytics | 3 |
DS7203 | Data Visualization (R & Power BI) | 3 |
DS7301 | Research Methodology for Data Science | 3 |
DS7302 | Multimodal Data Analysis (Vision, Speech) | 3 |
DS7401 | Social Media Analytics | 3 |
DS7402 | Predictive Business Analytics | 3 |
DS7403 | Dissertation | 12 |
Applicants must hold:
- A bachelor’s degree in computer science, Statistics, Mathematics, Engineering, or a closely related discipline, with at least a Second Class Honours or equivalent.
- Applicants from non-technical fields may be admitted if they demonstrate strong quantitative aptitude and may be required to take bridging modules.
- Semester Fees: USD 475
- Tuition Fees: USD 2,850 for two years
- The school has three intakes
- January, May, September