Data Science Eolaíocht Sonraí

The Postgraduate Certificate in Data Science is a one-year part time course which aims to provide science, engineering and computing graduates an opportunity to upskill in the developing area of data science and machine learning.

The course covers the key skills needed for an entry level position in data science, including modules in data wrangling, data mining, data visualisation, probability and statistical inference and machine learning. It is very practically focussed with students developing skills in the main tools, methods and techniques used in the domain.

The minimum admission requirements for entry to the PgCert in Data Science are 2.1 classification level 8 degree in a numerate discipline (including engineering, computing, science) or a 2.2 classification level 8 degree with at least 2 years of relevant industry experience.

All applicants require to have demonstrated strong programming skills.

Applicants should provide a full CV and a personal statement to support their application.

Individuals with significant experience in software development who wish to extend their skills into the fast developing area of data science and data analytics should apply.

Data science has been highlighted in a range of recent reports as an area of strategic importance both nationally and internationally. Areas in which opportunities for data science practitioners exist include retail, financial services, telecommunications, health, and government organisations. Specific roles include but are not limited to:

• Data Analytics Consultant
• Data Scientist
• Data Analyst
• Data Architect
• Database Administrator
• Data Warehouse Analyst
• Business Intelligence Developer
• Business Intelligence Implementation Consultant
• Business Analyst
• Reporting Analyst

TU256 is a part time course with evening delivery, over three evenings in the first semester and two evenings in the second semester.  Classes start at 6pm or 6.30pm depending on the module.

This course will be delivered in a blended mode with majority of learning activities delivered online with a number of onsite face-to-face touch points in each semester. These touch points include the induction event at the beginning of the academic year and face-to-face lectures and lab in weeks 1, 7 and 13 of each semester.  In order to facilitate students who cannot attend, each face-to-face activity will be accompanied by an online version of the event – lectures and labs will be livestreamed from the classroom.