University of Greenwich, United Kingdom
Our Master of Science program in Data Science and its Applications is strategically crafted to enhance the global workforce by fostering diversity among qualified data science professionals, irrespective of your academic background. Whether you are transitioning into data science roles or delving into specialized areas, this program provides you with tangible skills to analyze, solve, and assess data-intensive projects—skills crucial for both employment and further academic pursuits. Delve into a variety of engaging topics in contemporary data science and apply your data handling expertise to address real-world challenges across diverse fields. This program, available as a one-year full-time or a two-year part-time option, offers flexibility with optional modules covering various data science applications. This flexibility allows you to tailor your learning experience to align with your interests and career objectives. Graduates of the program discover opportunities in private and public companies, government sectors, and non-governmental organizations. Embark on this dynamic learning journey with us and unleash the potential of data science for your future professional endeavors.
Full-time students in Year 1 must complete the following mandatory modules:
1. Databases and Data Infrastructure (10 credits)
2. Ethics and Governance (10 credits)
3. Group Project (30 credits)
4. Individual Project (30 credits)
5. Machine Learning and its Applications (15 credits)
6. Principles of Data Science (15 credits)
7. Programming for Data Science (15 credits)
8. Research Project Management (10 credits)
9. Mathematics and Statistics for Data Science (15 credits)
Additionally, students are required to select 30 credits from the following options:
1. Advanced Programming for Data Science (15 credits)
2. Data Science for Medical Applications (15 credits)
3. Data Visualisation and its Applications (15 credits)
4. Graph Theory and its Applications (15 credits)
5. Spatial Data Science (15 credits)
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