Master of Science in Data Science & Analytics
MSc DSA
2 Years (Blended)All UCU Study Centres (70% online)Intakes: May & September
Overview
Data science, artificial intelligence, and human-computer interaction are transforming how companies do business. At Uganda Christian University, we’re researching breakthrough technologies and techniques that harness complex data sets and turn them into meaningful insights.
Entry Requirements
- Bachelors degree in a STEM field (STEM = Science, Technology, Engineering & Mathematics)
Registration & Mode of Delivery
- Registration: All UCU Study Centres (programme offered 70% online).
- Mode of delivery: Blended (70% online).
A) MSc Data Science and Analytics Entry Requirements
- Applications may be submitted by students who attained at least a second-class lower Bachelor’s degree in Science, Technology, Engineering, and Mathematics (STEM) disciplines from a recognised higher institution.
- Students with a degree in any other discipline other than STEM disciplines, who have completed a postgraduate diploma in Data Science and Analytics, Computer Science, or Engineering with at least a second-class upper.
- Applicant must have passed Mathematics, Statistics, or Applied Mathematics at their previous levels of study. Computer programming experience is also an advantage.
B) Postgraduate Diploma in Data Science and Analytics Admission Requirements
- To qualify for admission, a candidate must fulfil the general UCU entry requirements for a Postgraduate Diploma, and in addition be a holder of either:
- Bachelor’s Degree in Computer Science/Information Technology/Systems, Computer/Software Engineering or Engineering or any other computing-related area; or other STEM-related degree from a recognised higher institution of learning with a strong background in computer programming and mathematics.
- Any other degree with evidence of having passed acceptable Courses in mathematics or statistics or economics or computer programming.
- The minimum class of a bachelor’s degree is second class and above (or its equivalent) from a recognised University in a programme relevant to the programme applied for.
MSc Data Science and Analytics (Plans and Configurations)
- The MSc Data Science & Analytics programme is offered in two configurations (plans):
- Plan A: Master of Science in Data Science and Analytics by Research (extended, self-guided research project leading to a thesis; emphasis on independent research over taught instruction).
- Publication requirement for Plan A students: may be required to contribute to the Computer Science body of knowledge via either a journal paper accepted for publication in a reputable journal, or two conference publications in a related discipline.
- Plan B: Master of Science in Data Science and Analytics by Coursework and Project Report (at least 75% taught modules plus a short project/internship and project report).
Postgraduate Diploma in Data Science and Analytics (PGDDS)
- The PGD in Data Science and Analytics is a one-year programme (First-year milestone), embedded within MSc of Data Science and Analytics Programme.
- PGDDS is achieved upon successful completion of cross-cutting modules (15 credits), core modules (25 credits), and a Postgraduate Diploma project (5 credits).
- Within two years of completion, a PGD Data Science and Analytics holder may apply to upgrade to MSc Data Science and Analytics by accumulating twenty-five (25) additional credits as guided by the Department of Computing and Technology.
- To upgrade to MSDS (Plan A), a PGDDS holder will undertake at least one year of independent research and MUST satisfy all requirements for MSDS Plan A; students may apply for exemption from relevant modules already passed during PGDDS.
Course Curriculum
MSc Data Science & Analytics (Plan A)
Compulsory (Cross–Cutting) Modules
Total CU: 15
Compulsory (Cross–Cutting) Modules
| Code | Course | Type | CU |
|---|---|---|---|
| CSC8101 | Object Oriented Programming with Python | C | 5 |
| TST8131 | Advanced Christian Ethics | C | 5 |
| RSM8101 | Research Methods and Publications | C | 5 |
Elective/Audited Modules
Elective/Audited Modules
| Code | Course | Type | CU |
|---|---|---|---|
| CSC8204 | Artificial Intelligence and Machine Learning | E | 5 |
| DSC8305 | Business, Management & Financial Data Analytics | E | 5 |
| DSC8307 | Data Mining, Modelling and Analytics | E | 5 |
| DSC8306 | Data Engineering and Cloud Computing | E | 5 |
| CSC8307 | Data Privacy and Security | E | 5 |
| SYE8304 | Data Intensive Systems | E | 5 |
| DSC8203 | Data Science Lifecycle | E | 5 |
| MTH8201 | Mathematics for Data Science | E | 5 |
| CSC8203 | Applied Machine Learning | E | 5 |
| DSC8202 | Data Analysis and Visualisation | E | 5 |
| DSC8201 | Big Data Analytics | E | 5 |
Research and Projects Modules
Total CU: 45
Research and Projects Modules
| Code | Course | Type | CU |
|---|---|---|---|
| DSC8410 | Data Science Seminars and Practicum | C | 5 |
| DSC8409 | Data Science Thesis | C | 40 |
Postgraduate Diploma in Data Science and Analytics (PGDDS)
Core Modules
Total CU: 25
Core Modules
| Code | Course | Type | CU |
|---|---|---|---|
| DSC8203 | Data Science Lifecycle | C | 5 |
| MTH8201 | Mathematics for Data Science | C | 5 |
| CSC8203 | Applied Machine Learning | C | 5 |
| DSC8202 | Data Analysis and Visualisation | C | 5 |
| DSC8201 | Big Data Analytics | C | 5 |
Compulsory (Cross–Cutting) Modules
Total CU: 15
Compulsory (Cross–Cutting) Modules
| Code | Course | Type | CU |
|---|---|---|---|
| CSC8101 | Object Oriented Programming with Python | C | 5 |
| TST8131 | Advanced Christian Ethics | C | 5 |
| RSM8101 | Research Methods and Publications | C | 5 |
Research and Projects Modules
Total CU: 5
Research and Projects Modules
| Code | Course | Type | CU |
|---|---|---|---|
| DSC8411 | Data Science PGD Project Report | C | 5 |
Competencies to Obtain
Advanced analytics and ML practiceData engineering foundationsResearch methods and scientific communicationResponsible AI and governance awareness
Career Prospects
- Data scientist/analyst roles
- ML engineering roles
- Research and innovation roles in analytics-driven sectors