Applied Data Science and Text Analytics

Program Overview

Applied Data Science and Text Analytics

Overview

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.

Programme Details

Course Code

TU5095

ECTS

20

Level

Level 9

Award

Certificate

Duration

15 Weeks

Course Type

Micro-credentials

Mode of Study

Part Time

Method of Delivery

Online

Commencement Date

TBC

Location

Blanchardstown

Programme Structure

Year 1 Semester 1

• Algorithms for Data Science
• Text Mining and Web Content Mining

Schedule

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

Admission Criteria

Minimum Entry Requirements

• 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.

Fees

• Full Course Fee (Before any HCI award applied) is €2,000
• If eligible for the HCI fee subsidy of 80%, the fee is €400

Learning Outcomes

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.
• 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.
• 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.
• 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.

Progression

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).