Program Overview

Applied Data Science and Analytics

Overview

This Postgraduate Certificate in Applied Data Science and Analytics is a constituent of our MSc in Applied Data Science and Analytics, running fully online. Developed with industry for industry, it has gained a significant reputation as a high quality, challenging and very rewarding programme with a practical focus. The programme is designed for students in the workplace that need to upskill or reskill in data science and analytics.

What is Applied Data Science and Analytics?

This programme focuses on the knowledge and skills to select, apply and evaluate data science and big data analytics techniques, with an emphasis on discovering and using knowledge to add value to a company. Students will gain both an in-depth theoretical understanding and practical hands-on experience, including implementing novel and emerging techniques.

Modules

• Algorithms for Data Science (mandatory)
• Data Exploration and Pre-processing (mandatory)
• Electives:
  • Data Science Applications
  • Text Analytics and Web Content Mining
  • Programming for Big Data

Schedule

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.

Minimum Entry Requirements

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.

Learning Outcomes

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.
• 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.
• 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.
• Show an understanding and appreciation of the need for quality and integrity and an awareness of ethical concerns arising from data analysis.
• 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.
• Demonstrate advanced theoretical and practical knowledge and skills relevant to data science including recent developments; and the key stages of relevant development methodologies.
• 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.

Course Content

Year 1 Semester 1

• Algorithms for Data Science (mandatory)
• Text Mining and Web Content Mining (elective)

Year 1 Semester 2

• Data Pre-processing and Exploration (mandatory)
• Data Science Applications (elective)
• Programming for Big Data (elective)

Course Details

• Course Code: TU5096
• ECTS: 30
• Level: Level 9
• Award: Postgraduate Certificate
• Duration: 30 Weeks
• Course Type: Micro-credentials
• Mode of Study: Part Time
• Method of Delivery: Online
• Commencement Date: TBC
• Location: Blanchardstown
• Fees: Full Course Fee (Before any HCI award applied) is €2,995. If eligible for the HCI fee subsidy of 80%, the fee is €599.