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Master of Science in Data Science and Analytics (MSDS)

Course ID
MSDS
Department
Computer Science
Campus
Main Campus
COURSE DESCRIPTION

The Master of Science in Data Science and Analytics is a modular multidisciplinary programme that equips students with the latest techniques, technologies, and tools for big data acquisition, analysis, modelling and reporting. The skills acquired in the programme can be applied to many other disciplines, such as finance, business, management, health, agriculture, medicine, law and social sciences. 

The MSDS programme emphasizes four key competencies (or knowledge areas) that are defined in the broader Data Science body of knowledge. The competencies include Data Analytics, Data Science Engineering, Data management and Scientific research methods. These competencies are commonly required for the successful work of Data Scientists in different work environments in the industry in research and throughout the whole career path.

The MSDS programme will be offered in two configurations (herein referred to as plans):

  1. Plan A (Master’s Degree by Research Only): This programme configuration will consist primarily of an extended, self-guided data science research project leading to at least one peer-reviewed publication and a thesis.
  2. Plan B (Master’s degree by coursework and project report): This programme configuration will consist of at least 75% taught modules and a short project undertaken as a self-guided project or an internship in the field of Computer Science with a short project report as a final output.

Master of Science in Data Science and Analytics by Research (Plan A)

The Master of Science in Data Science and Analytics (by research) emphasises the student’s own independent
research study and contribution to the Data Science body of knowledge over taught instruction.
MSDA plan A students undertake three cross-cutting taught modules (15 credits), two research modules
one of which is Data Science thesis (45 credits) and any one elective/audited module (5 credits). All this
accumulates to 65 credits to graduate with a Master of Science in Data Science and Analytics (plan A).
Any additional modules may be studied by MSDS plan A students as audited modules to support the
student’s research.

Publication requirement for Plan A students

Through the research modules, Plan A students may be required to make a contribution to the Data
Science body of knowledge. This contribution may be measured by either a journal paper accepted for
publication in the reputable journal in a relevant Data Science discipline, or two conference publications
in a related Data Science discipline.

Master of Science in Data Science and Analytics (by coursework or Plan B)

The Master of Science in Data Science and Analytics (MSDS) is a programme configuration that consists
majorly of taught modules to develop expertise in the data science subject knowledge. MSDS students
shall complete nine core modules, and three elective modules and undertake a short Data Science project either
through self-study or through an internship with a report as the final output.

Master of Science in Data Science and Analytics – Plan B Milestones

There are two milestones for plan B (based on credits accumulation system) that can be achieved by a
student enrolled on a MSDS by coursework programme.

  1. Postgraduate Diploma in Data Science and Analytics (PGDDS): The first milestone is
    achieved upon successful completion of cross-cutting modules (15 credits) and core modules (25
    credits). Then a student may opt to undertake a postgraduate diploma project (5 credits) and
    graduate with a Postgraduate Diploma in Data Science and Analytics (PGDDS). The
    minimum graduation load for a PGDDS is forty-five (45) credits.
  2. Master of Science in Data Science and Analytics (MSDS) The second milestone (MSDS)
    is achieved by a student upon successful completion of three cross-cutting modules (15 credits),
    five core modules (25 credits), atleast three elective modules (15 credits) and two research modules
    (10 credits). The minimum graduation load for MSDS is sixty-five (65) credits. The student may
    attempt extra elective modules to improve their skills in the field

Upgrading from PGDDS to MSDS

Within two years of completion, a PGDDS holder may apply to upgrade to MSDS (by coursework)
by accumulating twenty (25) additional credits. This can be done through undertaking three elective
coursework modules (15 credits), and two research modules (Seminar series and Data Science project)
(10 credits).
To upgrade to Plan A, a PGDDS holder will undertake atleast two years of independent research, within
which they MUST satisfy all the requirements for the plan A. The student may apply for exemption of
the relevant modules already passed during the PGDDS study.

Entry requirements

  1. 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 recognized higher institution. 
  2. 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. 
  3. Applicants must have passed Mathematics, Statistics or Applied Mathematics at their previous levels of study. Computer programming experience is also an advantage.

Course Details

Master of Science in Data Science and Analytics ( by Research or Plan A )

S/N CODE MODULE NAME TYPE LH PH SH FH CH CU DNH
1 CSC8101 Object Oriented Programming with Python C 30 60 45 – 75 5 4
2 TST8131 Advanced Christian Ethics C 30 60 45 – 75 5 5
3 RSM8101 Research Methods and Publications C 30 60 45 – 75 5 4
  Elective/Audited Modules (choose at least 1)                
1 CSC8204 Artificial Intelligence and Machine Learning E 30 60 45 – 75 5 4
2 DSC8305 Business, Management & Financial Data Analytics E 30 60 45 – 75 5 4
3 DSC8307 Data Mining, Modelling and Analytics E 30 60 45 – 75 5 4
4 DSC8306 Data Engineering and Cloud Computing E 30 60 45 – 75 5 4
5 CSC8307 Data Privacy and Security E 30 60 45 – 75 5 4
6 SYE8304 Data Intensive Systems E 30 60 45 – 75 5 4
7 DSC8203 Data Science Lifecycle C 30 60 45 – 75 5 4
8 MTH8201 Mathematics for Data Science E 30 60 45 – 75 5 4
9 CSC8203 Applied Machine Learning E 30 60 45 – 75 5 4
10 DSC8202 Data Analysis and Visualisation C 30 60 45 – 75 5 4
11 DSC8101 Big Data Analytics E 30 60 45 – 75 5 4
  Research and Project Modules                
1 DSC8410 Data Science Seminars and Practicum C 30 – 135 – 75 5 1.9
2 DSC8409 Data Science Thesis C – 750 675 – 600 40 5.2

Master of Science in Data Science and Analytics ( by Coursework/Plan B )

S/N CODE MODULE NAME TYPE LH PH SH FH CH CU DNH
1 DSC8203 Data Science Lifecycle C 30 60 45 – 75 5 4
2 MTH8201 Mathematics for Data Science C 30 60 45 – 75 5 4
3 CSC8203 Applied Machine Learning C 30 60 45 – 75 5 4
4 DSC8202 Data Analysis and Visualisation C 30 60 45 – 75 5 4
5 DSC8201 Big Data Analytics C 30 60 45 – 75 5 4
  Compulsory (Cross-Cutting) Modules                
1 CSC8101 Object Oriented Programming with Python C 30 60 45 – 75 5 4
2 TST8131 Advanced Christian Ethics C 30 60 45 – 75 5 5
3 RSM8101 Research Methods and Publications C 30 60 45 – 75 5 4
  Elective Modules (choose at least 3)                
1 CSC8204 Artificial Intelligence and Machine Learning E 30 60 45 – 75 5 4
2 DSC8305 Business, Management & Financial Data Analytics E 30 60 45 – 75 5 4
3 DSC837 Data Mining, Modelling and Analytics E 30 60 45 – 75 5 4
4 DSC8306 Data Engineering and Cloud Computing E 30 60 45 – 75 5 4
5 CSC8307 Data Privacy and Security E 30 60 45 – 75 5 4
6 SYE8304 Data Intensive Systems E 30 60 45 – 75 5 4
  Research and Project Modules                
1 DSC8410 Data Science Seminars and Practicum C 30 – 135 – 75 5 1.9
2 DSC8409 Data Science Project Report C – 90 90 – 75 5 3.6

Postgraduate Diploma in Data Science and Analytics

S/N CODE MODULE NAME TYPE LH PH SH FH CH CU DNH
1 DSC8203 Data Science Lifecycle C 30 60 45 – 75 5 4
2 MTH8201 Mathematics for Data Science C 30 60 45 – 75 5 4
3 CSC8203 Applied Machine Learning C 30 60 45 – 75 5 4
4 DSC8202 Data Analysis and Visualisation C 30 60 45 – 75 5 4
5 DSC8201 Big Data Analytics C 30 60 45 – 75 5 4
  Compulsory (Cross-Cutting) Modules                
1 CSC8101 Object Oriented Programming with Python C 30 60 45 – 75 5 4
2 TST8131 Advanced Christian Ethics C 30 60 45 – 75 5 5
3 RSM8101 Research Methods and Publications C 30 60 45 – 75 5 4
  Research and Project Modules                
1 DSC8410 Data Science PGD Project Report C – 90 90 – 75 5 3.6

CU – Credit Unit                                 CH – Contact Hours

LH – Lecture Hours                             PH – Practical Hours

TH – Tutorial Hours                            FH – Field Hours

SH – Student Hours                             NH – National Hours

DNH – Daily National Hours

How you study

Uganda Christian University offers its courses both online and physically at the university’s laboratories

Career Outcome

  1. Data Analyst
  2. Data Scientist
  3. Business Analyst
  4. Database Administrator
  5. Machine Learning Engineer