The Master of Artificial Intelligence and Data Science (AI&DS) at Uganda Technology and Management University (UTAMU) offers a dynamic learning experience that integrates theoretical foundations, hands-on practical skills, and cutting-edge research. The programme is designed to equip students with advanced competencies in artificial intelligence, machine learning, data analysis, big data technologies, and predictive analytics to a wide range of field (Biology, Agriculture, Chemistry, Medicine, Physics etc.).
Graduates of this programme are well-prepared for leadership roles in AI and data-driven industries. The curriculum includes courses on data management, AI ethics, machine learning algorithms, and statistical analysis, providing students with the essential tools to tackle the challenges of the rapidly evolving technological landscape. The learning structure combines face-to-face, online, and experiential learning, ensuring flexibility and real-world application of concepts.
Career Opportunities
Graduates of the Master of AI&DS programme will be well-equipped for a range of roles, including:
- Data Scientists
- AI Researchers
- Machine Learning Engineers
- Data Engineers
- Business Intelligence Analysts
- Data Analysts
- AI and Data Science Consultants
- Predictive Analytics Specialists
- Data-Driven Product Managers
These professionals can work in various sectors, including tech companies, finance, healthcare, government, and academia. The program’s focus on AI and data science ensures that graduates can contribute to the development of new technologies and innovative solutions in a wide range of industries.
Programme Objectives
The objectives of the Master of AI&DS programme are to produce graduates who can:
- Develop innovative AI-based solutions and data-driven insights.
- Design, implement, and optimize machine learning and deep learning models.
- Conduct scholarly research in AI, machine learning, and data science.
- Provide expertise in key AI&DS areas such as data analysis, machine learning, and predictive analytics.
- Lead teams of professionals to tackle complex data science challenges and deliver impactful solutions.
- Stay at the forefront of technological advancements in AI and data science through continuous learning and research.
Learning Outcomes
Upon completion of the Master of AI&DS, graduates will be able to:
- Innovate and develop AI-based solutions to real-world problems.
- Analyze and process complex data sets using modern tools and techniques.
- Conduct high-quality research in the fields of artificial intelligence and data science.
- Demonstrate expertise in machine learning, data analytics, big data technologies, and AI ethics.
- Lead teams and projects in AI and data-driven environments.
- Continuously update and refine their knowledge and skills in AI and data science through self-directed learning and research.
Admission Requirements
Students will be admitted to the Master of Artificial Intelligence and Data Science (AI&DS) through two main avenues: Degree Holders and Post-Graduate Diploma Entry Schemes.
Degree Holders Scheme
Candidates seeking admission through the Degree Entry Scheme are required to possess the following:
- A Bachelor's degree in any field with minimum of a Lower Second-class degree (from an accredited University.
Candidates with prior graduate training may, after admission, apply for the transfer of credits to this programme.
Program Duration
The LLM Programme shall run on a semester system and it is expected that students will graduate after two (2) academic years of their studies. The first two semesters will be devoted to coursework during which time the students must take courses relevant to their specializations to fulfil the total credits required for successful completion of the LLM degree. Each semester will comprise 17 weeks, of which 15 weeks shall be used for teaching and 2 weeks for examinations. Students will undertake the research for the dissertation in the two semesters of the second year of study.
Program Options
Year One Semester One (5 Cores) | |||||||
Code | Course Name | LH | PH | TH | CH | CU | |
CAI 7100 | Foundations of Artificial Intelligence | 30 | 15 | 15 | 45 | 3 | |
CAI 7101 | Foundations of Data Science | 30 | 15 | 15 | 45 | 3 | |
CAI 7102 | Programming for AI and Data Science | 30 | 30 | 30 | 60 | 4 | |
CRM 7100 | Research Methods | 30 | 30 | - | 45 | 3 | |
CSC 7102 | Machine Learning | 30 | 30 | - | 45 | 3 | |
Total |
| 16 | |||||
Year One Semester Two (4 Cores) | |||||||
Code | Course Name | LH | PH | TH | CH | CU | |
CIS 7202 | Data Mining | 30 | 30 | - | 45 | 3 | |
CAI 7200 | Big Data Analytics | 30 | 30 | 30 | 60 | 4 | |
CAI 7201 | Specialization Electives Introduction | 30 | - | 30 | 45 | 3 | |
CAI 7202 | Capstone Project I | 15 | 30 | 30 | 45 | 3 | |
CSC 7202 | Cryptosystems | 30 | 30 | - | 45 | 3 | |
Total | 19 | ||||||
Year Two Semester One (3 Cores) | |||||||
Code | Course Name | LH | PH | TH | CH | CU | |
CAI 8100 | AI for Inter-disciplinary Applications (Specialization Electives) | 30 | 30 | 30 | 60 | 4 | |
RMS 8100 | Research Methodology Seminars | 30 | 30 | - | 45 | 3 | |
CAI 8103 | Capstone Project II | 15 | 30 | 30 | 45 | 3 | |
Total |
| 10 | |||||
Year Two Semester Two (2 Cores) | |||||||
Code | Course Name | LH | PH | TH | CH | CU | |
RDS 8201 | Dissertation | - | 150 | 30 | 75 | 5 | |
CAI 8200 | Professional Development and Leadership in AI&DS | 30 | 15 | 15 | 45 | 3 | |
Total |
| 8 | |||||
Graduation Load = 53 Credit Units | |||||||
Fees per semester
All students will pay UGX 20,000 and UGX 25,000 per year as NCHE fees and Guild fees respectively.
| STUDY PERIOD | EAST AFRICAN COMMUNITY STUDENTS | INTERNATIONAL STUDENTS | ||
| Tuition Fees/UGX | Functional Fees/UGX | Tuition Fees/USD | Functional Fees/ USD | |
| Semester | 2,000,000/= | 300,000 | 900 | 150 |
| Academic Year | 4,000,000/= | 600,000 | 1,800 | 300 |
| Total (2 years) | 8,000,000/= | 1,200,000 | 3,600 | 600 |
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