Our Master of Science in Computing in Big Data Analytics is a one year, full-time or two year, part-time programme. It focuses on the processes involved in examining and interpreting large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information.
From banking and financial services to retail and healthcare, as well as life sciences, the opportunities in big data analytics are expanding all the time, and this course provides you with excellent qualifications to make the most of the ever increasing opportunities.
After all, your skills can provide competitive advantage for businesses including more effective marketing and increased revenue which is why more and more companies have moved into the field, harnessing talents such as yours to exploit the huge volumes of data now available.
The opportunities for successful graduates exist in companies running large database systems, as well as the payment card industry and financial services. Roles typically include becoming a data storage manager, data analyst or data scientist.
| College | Atlantic Technological University (ATU Donegal) |
| Course ID | 193030 |
| Course Location | Letterkenny, Donegal |
| Course Type | Classroom Based |
| Course Qualification | Masters Degree |
| Course Start Date | 1st January 1970 |
| Course Duration | 1/2 years |
| Course Time | Part-time/Full-time |
| Course Fee | 6300 |
| Entry Requirements | Level 8 Honours Degree in Computing, or equivalent, second class honours (2.2), or Higher Diploma in Computing (Conversion Course into Computing). Non computing applicants must have a minimum of 30 ECT credits in Computing or Computing related modules, or computer industry experience. If you do not have an honours degree but have relevant experience you may also be eligible to apply via Recognition of Prior Learning (RPL). Applicants may also attend a one week bridging course where necessary. |
| Career Path | This programme is suitable for individuals who have a strong interest in data analysis, programming, and statistical modelling, and who want to develop advanced skills in these areas to pursue a career in data science or related fields. |
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