Applied Data Science and Analytics

Program Overview

Applied Data Science and Analytics

Overview

This award in Applied Data Science and Analytics is a constituent of the 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. Focused on the knowledge and skills to select, apply, and evaluate data science techniques, the programme emphasises discovering knowledge that can add value to a company. Learners 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.

Programme Structure

The programme comprises two modules that are designed to work together to cover all stages of a data science methodology from defining business objectives, exploration of and understanding your data, assessing data quality and bias, understanding the implications of data preparation choices, tuning and interrogating data models, and critically evaluating results with respect to a business objective. Assessments are adaptable to individual business contexts and interests.

Modules

• Algorithms for Data Science
• Data Pre-processing and Exploration

Schedule

• Average of 3 contact hours per week and additional self-directed learning.
• Time commitment varies depending on prior experience.
• Data Science Algorithms runs one evening a week from September 2024 to December 2024, final assessments to due in early January.
• Data Exploration and Pre-Processing runs two evenings a week from January to March with assessments completed by early May.
• Contact hours are synchronous, online, from 6pm to 9pm, and are also recorded.
• One-to-one support is also available throughout the week.
• Timetabled Days: Tuesday and Wednesday.

Admission 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.

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; 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.
• 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-evaluations to promote communications, responsibility, problem ownership, and appreciate for the need to update knowledge and skills.

Programme Details

• Course Code: TU5094
• ECTS: 20
• Level: Level 9
• Award: 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 €2,000
• If eligible for the HCI fee subsidy of 80%, the fee is €400