This course is eligible for 80% funding through the HCI Micro-Credential Course Learner Subsidy. Please refer to the fees and how to apply sections for specific details and eligibility criteria.
Applicants should hold:
- Second Class Honours Grade 2 (GPA 2.5 or equivalent), in a NFQ Level 8 Degree in Computing, Science, Engineering, Business with IT, or equivalent qualification. The acceptance of candidates with Third Class Honours degrees and appropriate work experience will be allowed provided there is evidence that the candidate can cope with the learning objectives of the course.
On completing this award, graduates will be able to discuss the workings of several of the most popular machine learning algorithms, data cleaning methods, and feature engineering techniques; strong focus is put on understanding the strengths and weaknesses of each, and critically evaluating alternatives suggested in literature that aim to address some limitation. In addition to theoretical knowledge, graduates will be able to advise on methods that are appropriate to a specific business context and dataset, ethically apply those methods as part of a data science methodology, and critically evaluate the results.
Soft skills are also developed through report writing, oral presentations, and self- and peer- evaluations to promote communications, responsibility, problem ownership, and appreciate for the need to update knowledge and skills.
Graduates who opt for “Applications in Data Science” will have more detailed knowledge on the application of data science methods, following a data science life cycle on a dataset of their choosing, and reporting on the results in both technical and non-technical language under direct lecturer supervision. Graduates who opt for “Text Analytics and Web Content Mining” will be able to discuss methods for extracting, cleaning, preparing, visualising and analysing text data, and apply those techniques to a data source of their choosing.
The graduate should be able to demonstrate:
- an ability to evaluate and critically appraise data science techniques with respect to a challenging business objective, dataset; and apply a range of data science techniques to address specific problems.
- an understanding and appreciation of the need for quality and integrity and an awareness of ethical concerns arising from data analysis.
- an ability to design and implement a data analytics solution that requires preliminary research for novel and unfamiliar situations; critically evaluate design and implementation issues in data science.
- advanced theoretical and practical knowledge and skills relevant to data science including recent developments; and the key stages of relevant development methodologies.
- an ability to reflect on their strengths and weaknesses; recognition of the need to constantly update knowledge and skills; and an attitude based on initiative, responsibility, and problem ownership
- interpersonal and communication skills to discuss current challenges and research and report on analysis results with respect to a business objective.
The programme runs over two semesters, from September to May. Each module is delivered synchronously, online, one evening a week for 3 hours, and is also recorded. 1 to 1 support is available outside of scheduled class times.
The course will run Mon - Wed, depending on electives chosen
Each module is 3 contact hours per week and additional self directed learning, time commitment varies depending on prior experience. There is one mandatory module per semester and one additional elective module either in semester 1 or semester 2.
Information on application opening dates will be published in February 2025.
