MASTER OF SCIENCE IN DATA SCIENCE
Rationale of the Programme
The Msc.Data Science curriculum draws upon social sciences, computer science, mathematical sciences, statistics, management, and law. Students use the latest tools and
analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems. The programme is anchored on relevant learning theories and principles and seeks to produce, through relevant theories and hands-on training, competent data scientists to effectively support various sectors such as communications, manufacturing, banking and finance, education, transportation, entertainment, medicine, agriculture, and law with special focus on their Cooperative sub-sectors.
Drawing on insights from the social sciences, computer science, statistics, management, and law, this multidisciplinary curriculum prepares students to use the latest tools and analytical methods to interpret and communicate their findings.
Admission Requirements
Candidates wishing to study MSc. Data Science programme must be a holder of Bachelor’s degree in Computer Science/Information Technology/Mathematical Sciences (with adequate computing units)or a related discipline with at least;
Upper second class honors degree or cumulative Grade Point Average ( GPA) of 3.0 on a scale of 4.00
OR
Lower Second Class Honors or cumulative GPA of 2.50 on a scale of 4.00 with additional relevant training, evidence of research capacity through research, presentations or peer reviewed publications and relevant working experience of two years,
Table 2. 1 Units distribution
| S/N | COURSE CODE | COURSE TITLE | LECTURE
HOURS |
CREDIT HOURS |
| YEAR 1 SEMESTER 1 | ||||
| 1 | MCSC 5101 | INTRODUCTION TO DATA SCIENCE AND
APPLICATIONS FOR DECISION MAKING |
42 | 3 |
| 2 | MCSC 5102 | PYTHON FOR DATA SCIENCE | 42 | 3 |
| 3. | MCSC 5104 | FUNDAMENTALS OF DATA ENGINEERING | 42 | 3 |
| 4. | MSTA 5101 | STATISTICS FOR DATA SCIENCE | 42 | 3 |
| 5 | MCCD 5105 | COOPERATIVE PHILOSOPHY | 42 | 3 |
| YEAR 1 SEMESTER 1I | ||||
| 8 | MCSC 5203 | DATA WAREHOUSING | 42 | 3 |
| MCSC 5205 | MACHINE LEARNING | |||
| 10 | MSTA 5207 | STATISTICAL PROGRAMMING | 42 | 3 |
| 11 | MCSC 5207 | ETHICAL AND LEGAL ISSUES FOR DATA
SCIENTISTS |
42 | 3 |
| 12 | MSTA 5203 | RESEARCH METHODS | ||
| YEAR 1I SEMESTER 1 | ||||
| 15 | MCSC 6108 | DATA VISUALIZATION | 42 | 3 |
| 16 | MCSC 6109 | MACHINE LEARNING AT SCALE | 42 | 3 |
| 17 | MCSC 6106 | BIG DATA ANALYTICS | 42 | 3 |
| 18 | MCSC 6111 | NATURAL LANGUAGE PROCESSING
WITH DEEP LEARNING |
42 | 3 |
| 19 | MSTA 6102 | STATISTICAL METHODS FOR DISCRETE
RESPONSE, TIME SERIES, AND PANEL DATA |
42 | 3 |
| YEAR 1I SEMESTER 1I | ||||
| 22 | MCSC 6210 | SEMINAR | 42 | 3 |
| 23 | MCSC 6212 | PROJECT (4 UNITS) | 168 | 3 |