Data Analytics and Data Mining

This microcredential aims to review and complement basic statistical knowledge and to set the framework for a range of methods, used in the analysis of simple and complex systems. The main focus is on grasping the principles intuitively and being able to use them practically with examples from diverse systems, rather than mathematical sophistication.

1. To demonstrate an understanding of the different levels of measurement and data types.

2. To demonstrate an understanding and ability to apply the underlying probability principles and distribution examples.

3. To demonstrate the ability to distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics.

4. To demonstrate an appreciation of the scope and robustness of common analytical methods for one to many samples.

5. The ability to use a range of analytical statistical techniques and interpret outcomes.

6. The ability to select appropriate statistical software, having been exposed to several examples.

7. To demonstrate the ability to apply techniques to a range of illustrative examples and case studies in big data and other real-world systems.

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis. 

And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport
• Please upload a CV
• If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.