Fundamentals of Data Science - Grangegorman

What is... Fundamentals of Data Science?
This is a postgraduate programme at level 9 in the National Framework of Qualifications. Applicants are required to have a strong numerate background. The course covers the key skills needed for an entry level position in data analytics, including modules in programming, databases, data wrangling and data analysis. It is very practically focussed with students developing skills in the main tools, methods and techniques used in the domain.

It is a part time course with evening delivery, over two evenings per week.

Core Modules (2 per semester)

Object Oriented Software Development
This module provides the learner with the fundamental skills of programming and object oriented programming. The aims of this module are:
To provide the learner with strong fundamental programming skills
To provide the learner with object-oriented programming skills
To ensure the learner has the necessary skills to design and develop an application using an object-oriented language

Information Systems
This module provides the learner with fundamental skills to design information systems, focussing on the design and implementation of database systems. The aims of this module are:

Enable the student to create new relational databases by devising a high-level conceptual data model
Transform that data model into a relational schema
Implement the relational schema correctly and robustly in SQL

Data Wrangling
The Data Wrangling module provides students with an opportunity to learn the skills and techniques most often associated with building and manipulating information sources to perform Data Analysis tasks. The module is intended to build a student’s skills and confidence with Data Programming tasks and provide a firm foundation for further Data Analytics study.

Data Analytics
Data analytics is an area of increasing importance and interest to organisations. Data analytics techniques offer huge potential in the creation of new knowledge products and services and the enhancement of existing products and services. Rather than focus on the details of specific data analytics techniques, this module addresses the application of data analytics techniques (from simple descriptive analytics techniques to more complex predictive analytics techniques) to real business problems.

It includes 30 ECTS credits of technical modules with 15 credits (2 modules) delivered each semester. Delivery is during weekday evenings, 2 evenings per week.

Minimum Entry Requirements
The minimum admission requirements for entry to the PgCert in Data Science Fundamentals are a 2.1 classification level 8 degree in a non-computing related degree or a 2.2 classification level 8 degree with at least 2 years of relevant industry experience.

All applicants require to have demonstrated strong numeracy and analytic skills which should be described in your supporting statement.

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

Applications for courses commencing in September 2024 will open in November 2023.

1 year

Mode of Study Part Time
Method of Delivery Blended

Schedule
TU257 is a part-time course with evening delivery, over two evenings per week in both semesters. Classes start at 6pm or 6.30pm depending on the module. The course starts in September, for two terms (finishing in June).

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.

Commencement Date: September 2024

Graduates of the PgCert in Fundamentals of Data Science can apply for the following courses;

TU256 Postgraduate Certificate in Data Science
TU059 MSc in Computer Science (Data Science)
TU060 MSc in Computer Science (Data Science)
Progression is subject to passing all modules, performing well on the programming (OOSD) module and achieving an overall average of 60% or higher.