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.
This Special Purpose Award in Applied Data Science and Text Analytics is a constituent of our MSc in Applied Data Science and Analytics. Developed with industry for industry, it has gained a significant reputation as a high quality, challenging and very rewarding programme with a practical focus. Focusing on the knowledge and skills to select, apply and evaluate data science and text analytics techniques, this award emphasises discovering knowledge that can add value to a company. Students will gain both an in-depth theoretical understanding and practical hands-on experience, including keeping abreast of current research and state of the art in data science related topics.
The programme is designed for students in the workplace that need to upskill or reskill in data science and analytics. The award comprises two modules. "Algorithms for Data Science" focuses on applying, tuning a variety of types of data models, and critically evaluating results with respect to data type and quality, and the overarching business objective. "Text Analytics and Web Content Mining" specialises on techniques for prepressing and modelling unstructured text. All assessments are adaptable to individual business contexts.
The course is 100% continuous assessment comprising of literature reviews, self- and peer- evalations, practical reports and online presentations.
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; 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.
Graduates will also be able to discuss and evaluate methods for extracting key concepts, sentiments and relationships from semi-structured and unstructured data; structural representations of text data; and appropriate visualisation techniques. In addition to theoretical knowledge, graduates will be able to advise on methods that are appropriate to a specific business context and analysis of sparse and dense datasets, 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-evaluations to promote communications, responsibility, problem ownership, and appreciate for the need to update knowledge and skills.
The graduate should be able to demonstrate:
- An ability to evaluate and critically appraise data science techniques with respect to a challenging business objective for structured and text data; 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.
- 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.
Year 1 Semester 1
The programme will run two evenings a week over one semester, from September 2024 to January 2024. Contact hours are delivered synchronously, online, from 6pm to 9pm, and are recorded. 1 to 1 support is available outside of scheduled class times.
6 contact hours per week and additional self directed learning, time commitment varies depending on prior experience.
Timetabled days: Tuesday and Wednesday
Applicants can express their interest for this course by completing the form at the link below:
On completion, students can progress onto a Postgraduate Certificate in Applied Data Science and Analytics (Level 9), Postgraduate Diploma in Applied Data Science and Analytics (Level 9), or our MSc in Applied Data Science and Analytics (Level 9).
