Introduction
The Master of Science in Data Science is a comprehensive 42-credit graduate program designed to equip students with a balanced blend of theoretical foundations and practical expertise. By integrating statistical modeling, machine learning, big data technologies, and cloud computing, this curriculum prepares graduates to address complex, real-world challenges and drive innovation across diverse sectors.
Program Objectives & Goals
• Equip students with advanced knowledge of statistical analysis, machine learning, and data visualization.
• Provide the skills necessary to collect, process, and analyze large datasets, transforming them into actionable insights.
• Prepare graduates to work in various fields such as healthcare, finance, and technology by applying data science techniques to real-world problems.
• Foster expertise in big data technologies, cloud computing, and advanced algorithms for data processing.
Career Opportunities
Graduates of this program can pursue roles such as:
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Data Engineer
• Business Intelligence Analyst
• Quantitative Analyst
• Data Science Researcher
Program Outcomes
Upon completing this program, students will be able to:
• Apply machine learning algorithms to predict trends and behaviors.
• Use statistical tools to analyze and visualize data patterns.
• Develop and deploy data-driven solutions for complex problems.
• Handle big data processing using cloud computing and distributed systems.
• Communicate findings through data visualizations and technical reports.
Admission Criteria
Applicants seeking admission into Master of Science in Data Science must meet the following criteria:
• Hold a bachelor’s degree in computer science, information technology, software engineering, data science, computer application, graphics and multimedia, business information technology, telecommunication engineering.
• Achieve a minimum Grade Point Average (GPA) of:
o 3 on a 4.0 scale, or
o 4 on a 5.0 scale.
• Demonstrate foundational knowledge relevant to the selected program, as assessed during the application review process. Submit supporting documentation such as academic certificate and transcripts.
Course Outline
Semester 1
| Fundamentals of Data Science | ||
| Programming for Data Science | ||
| Statistical Data Modelling | ||
Semester 2 |
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| Big Data Management | ||
| Research Methods in Data Science | ||
| Business Intelligence and Analytics | ||
Semester 3 |
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| Information Security Management | ||
| Machine Learning for Data Science | ||
| Data Mining | ||
| Dissertation 1 | ||
Semester 4 |
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| Deep Learning | ||
| Natural Language Processing | ||
| Cloud Computing for Data Science | ||
| Dissertation 2 | ||