By School of Science and Technology

In response to the growing global demand for data-driven professionals, the School of Science and Technology has launched a new Master of Science in Data Science and Analytics program. This is a cutting-edge program grounded on the belief that data-driven decision-making is central to organizational success in the digital era.

The program is designed to equip learners with the technical skills and professional expertise necessary to utilize various analytical tools required to analyze, understand, and engage with complex data, with the aim of meeting specific organizational needs and goals. Furthermore, it is delivered fully online, offering flexibility for working professionals while maintaining USIU-Africa’s high academic standards.

Learners will gain the expertise required to analyze data, improve decision-making, enhance critical thinking, and help organizations gain a competitive advantage. Upon successful completion of the program the learner will be able to; apply data analytics expertise and devise successful analytics strategies to a wide range of organizations; apply tools and techniques for building statistical or machine learning models to make predictions and decisions based on data; implement computerized decision support systems applied to specific real-life problems; conduct big data projects and manage innovation for developing data-driven solutions; and use data mining techniques to extract insights from data.

Furthermore, graduates of this program have a wide range of career opportunities, including roles such as data scientists, data architects, data engineers, machine learning engineers, MLOps engineers, business intelligence analysts, data analysts, statistical analysts, AI researchers, and risk/ fraud analysts, among others.

To be admitted into this program, the student must be a holder of a Bachelor’s degree in Data Science and Analytics, Statistics, Engineering, Computer Science, Mathematical Sciences (with adequate computing units) or a related discipline with at least; an upper second class honors degree or cumulative Grade Point Average ( GPA) of 3.0 on a scale of 4.00; or a 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. Candidates from other fields may also qualify by demonstrating adequate programming skills or completing prerequisite courses such as Introduction to Data Science, Principles of Machine Learning, and Statistical Modeling.