Bachelor of Artificial Intelligence and Data Science
Department of Artificial Intelligence and Data Science
Prepares the specialists who turn raw information into insight and automated capability, combining a strong mathematical and statistical foundation with machine learning, deep learning and the practical data-engineering skills the work requires.
Degree Awarded
Bachelor of Artificial Intelligence and Data Science
Duration
5 Years (10 Semesters)
Total Credit Hours
215 Credit Hours
Total Courses
70 Courses
Language of Instruction
English
Campus Location
Mogadishu, Somalia
Overview
Demand for artificial intelligence and data science competence in Somalia substantially exceeds supply, and the country's economic development increasingly depends on professionals able to collect, manage, analyse and act on data. Growth in commercial banking, telecommunications, remittance services, e-government initiatives and donor-funded programming has raised the demand for staff able to build databases, dashboards and intelligent systems, while employers consistently report that graduates arrive with theoretical knowledge but without the software, data-handling and deployment skills the work requires. The curriculum builds systematically from programming, mathematics and computing fundamentals through data structures, databases and networks to artificial intelligence, machine learning, deep learning and specialised electives such as computer vision, natural language processing, robotics and generative AI.
Vision & Mission
To become a leading centre of excellence in Artificial Intelligence and Data Science education, research and innovation, producing ethical, skilled and solutions-oriented graduates who contribute to sustainable development locally and globally.
To provide high-quality education, practical training and innovative research in Artificial Intelligence and Data Science, preparing ethical and competent graduates to solve real-world challenges through data-driven solutions.
Objectives & Learning Outcomes
- 1Build Foundational Competence — provide a rigorous foundation in artificial intelligence and data science principles, covering mathematics, statistics, programming and data management.
- 2Develop Intelligent-Systems Design Skills — develop the ability to design, build and evaluate intelligent systems and data-driven solutions using contemporary tools and frameworks.
- 3Build Practical Data Competence — build practical fluency in data collection, processing, visualisation and interpretation to support evidence-based decision-making.
- 4Instil Ethical and Governance Awareness — instil an understanding of the ethical, legal and governance dimensions of artificial intelligence and data use.
- 5Promote Research and Innovation — promote applied research, innovation and entrepreneurship in artificial intelligence and data science.
- 6Prepare for Professional and Advanced Study — prepare graduates for professional careers, entrepreneurship and advanced studies in artificial intelligence, data science and related fields.
- 1Explain the theoretical principles of artificial intelligence, machine learning and data science relevant to real-world problem-solving.
- 2Explain the ethical, legal and governance implications of artificial intelligence and data-driven systems.
- 3Apply principles of artificial intelligence, machine learning and data science to address real-world challenges.
- 4Analyse and interpret data-driven findings to support evidence-based decision-making.
- 5Evaluate alternative intelligent-systems designs against defined technical and ethical criteria.
- 6Design, develop, and evaluate intelligent systems using appropriate tools, techniques and technologies.
- 7Collect and process data using appropriate statistical and computational methods.
- 8Apply software-engineering and programming principles to build reliable data and AI solutions.
- 9Operate industry-standard data, machine-learning and cloud platforms competently in a laboratory or applied setting.
- 10Conduct a supervised research investigation into an artificial intelligence or data science problem and report the findings to an academic standard.
- 11Demonstrate teamwork, leadership and professional communication in individual and group work.
- 12Commit to ethical conduct, data privacy and continuous professional learning in the practice of artificial intelligence and data science.
Programme Duration
- Total duration: five (5) academic years, comprising ten (10) semesters, each sixteen (16) teaching weeks in length.
- Academic load: 70 courses and 215 credit hours in total, distributed across the ten semesters as prescribed in the Study Plan.
- Credit-hour equivalence: one (1) credit hour equals sixteen (16) contact hours of instruction per semester, together with the independent study needed to meet the course's learning outcomes.
Admission & Graduation Requirements
Entry Criteria
- Somali Secondary School Certificate or a recognised equivalent, with a minimum aggregate of 60%, with good grades especially in Mathematics and Physics.
- Satisfactory performance on the English and mathematics placement tests, sat by all admitted students before Semester I registration.
Required Documents
- Original secondary school certificate and one certified copy.
- Four passport-size photographs, white background.
- Letter of good conduct from the applicant's secondary school.
- Payment of the non-refundable $30 registration fee and completion of the university enrolment form.
- Complete all 70 required courses, totalling 215 credit hours, across ten semesters.
- Pass all University, Faculty and Department Requirement courses, with a minimum grade of D in each.
- Maintain a minimum cumulative CGPA of at least 2.00.
- Complete the One-Year Professional Skill Programme at the SIU Innovation and Skills Centre.
- Complete six (6) months of supervised Internship.
- Successfully complete and defend Graduation Project I and II.
- Clear all financial and administrative obligations.
Career Opportunities
Data Analyst
Business Intelligence Analyst
Junior Data Scientist
Machine Learning Engineer
Artificial Intelligence Engineer
Data Engineer
Database Administrator or Data Platform Specialist
Data Visualization Specialist
Natural Language Processing Practitioner
Computer Vision Practitioner
AI Governance and Data Ethics Assistant
Research Assistant
Technology Consultant
AI and Data Science Entrepreneur
Delivery & Assessment
- Lectures and guided learning
- Practical laboratories (Python, SQL, notebooks, data visualisation, ML libraries, version control)
- Project-based learning
- Case-based learning
- Seminars and guest lectures
- Research and capstone projects
- Blended and independent learning
| Assessment Component | Weighting |
|---|---|
| Coursework and Assignments | 20% |
| Practical and Skill Demonstrations | 20% |
| Midterm Examination | 30% |
| Final Examination | 30% |
| Total | 100% |
Practical Training
Internship or Practical Training is a compulsory component of the programme that enables students to apply classroom knowledge in a professional environment such as AI and machine-learning companies, data-analytics organisations, telecommunications and financial institutions, government ministries, and research and innovation centres.
- Bridge the gap between academic learning and professional practice.
- Gain hands-on experience, understand industry expectations, and apply AI and data science concepts in real-world settings.
- Develop workplace skills such as teamwork, communication and problem-solving.
- Students must complete a minimum of 8 weeks of supervised training in an approved organisation, submit a report and deliver an oral presentation.
Faculty and Learning Facilities
- AI and Data Science Laboratories equipped with modern computers, relevant software and reliable internet connectivity.
- Machine Learning and Data Analytics Tools for practical training in data analysis, modelling and intelligent system development.
- Multimedia-enabled Classrooms supporting interactive teaching and learning.
- Library and Digital Resources providing textbooks, journals and online academic resources.
- E-learning Platform for accessing course materials, assignments and academic activities.
- Research and Innovation Spaces supporting student projects, collaboration and AI-based research.
Contact Information
Faculty of Computer Technology, Somali International University (SIU)