BA (Arts with Data Science)

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Course Overview
Key Facts
Course Outline
Why Choose This Course?
Course Fees
Find Out More

Entry Points (2019)

434

Course Overview

Are you good at maths, interested in coding, and keen to study people as well as equations? Do you like the idea of using data to understand the human condition? If so, the new BA in Arts with Data Science may be for you.

Data science is the art of extracting useful knowledge from raw data, and then communicating that knowledge to others. Data scientists need strong quantitative and coding skills, but they are not narrow technical specialists. For example, to learn from psychological data, you need to have a good understanding of psychology.

In this unique four-year programme, you will build an outstanding portfolio of data science skills, while simultaneously studying a relevant Arts discipline (Psychology, Economics, Geography, History or Philosophy) to Joint Honours level. You will graduate with a proven track-record of applying data analytics techniques to real-world human problems. Few skills are more highly valued by employers.

Applications and Selections

Who Teaches this Course

Requirements and Assessment

Key Facts

Entry Requirements

Minimum Grade H5 in two subjects and passes in four other subjects at O6/H7 Grades in the Leaving Certificate, including Irish, English, another language and three other subjects recognised for entry purposes. A H5 or O1 Grade in Leaving Certificate Mathematics is also a requirement.

Additional Requirements

Duration

4 years

Next start date

Sept 2019

A Level Grades (2019)

nuigalway.ie/alevels

Average intake

Closing Date

NFQ level

Mode of study

ECTS weighting

Award

CAO

GY124

Course code

Course Outline

Students study Data Science and Mathematics for Data Science choose one of the following Arts major subjects: Psychology, Economics, Geography, Philosophy or History.

YEAR ONE
Arts major +
• Skills for Data Science 1
• Programming 1
• Programming 2
• Probability 1
• Statistics
• Analysis and Algebra 1
• Analysis and Algebra 2
• Skills for Mathematics

YEAR TWO
Arts major +
• Skills for Data Science 2
• Database Systems
• Probability 2
• Statistical Inference
• Linear Algebra

YEAR THREE
Data Science Accelerator in Semester 1:
• Skills for Data Science 3
•Philosophy of Statistics and Machine Learning
• Artificial Intelligence
• Calculus
• Formal Logic or Discrete Mathematics

Work placement in Semester 2.

YEAR FOUR
• Arts major +
•Machine Learning and Text Mining
• Applied Regression Models
• Stochastic Processes or Statistical Modelling
• Data Science project

Curriculum Information

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits: You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
Module: An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
Subject: Some courses allow you to choose subjects, where related modules are grouped together. Subjects have their own required number of credits, so you must take all that subject's required modules and may also need to obtain the remainder of the subject's total credits by choosing from its available optional modules.
Optional: A module you may choose to study.
Required: A module that you must study if you choose this course (or subject).
Required Core Subject: A subject you must study because it's integral to that course.
Semester: Most courses have 2 semesters (aka terms) per year, so a three-year course will have six semesters in total. For clarity, this page will refer to the first semester of year 2 as 'Semester 3'.

Year 1 (60 Credits)

Year 2 (60 Credits)

Why Choose This Course?

Career Opportunities

Data analytics talent is in very short supply worldwide. Graduates from this programme will have a highly attractive portfolio of skills, and will be well positioned for a wide range of graduate careers. Those who pursue further study (e.g. NUIGalway’s one-year MSc in Data Science) will be able to apply for data scientist roles.

Who’s Suited to This Course

Learning Outcomes

Work Placement

The Arts with Data Science degree includes an eight-month off-campus work placement in Year 3. (Should no external placement be available, you will be given a project on campus.)

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€6,300 p.a. 2019/20

Fees: Tuition

€3,076 p.a. 2019/20

Fees: Student Contribution

€3,000 p.a. 2019/20

Fees: Student levy

€224 p.a. 2019/20

Fees: Non EU

€16,650 p.a. 2020/21

EU Fees 2019/20:
- Tuition: may be paid by the Irish Government on your behalf see - free fee initiative.
- Student Contribution: €3,000 - payable by all students but may by paid by SUSI if you apply and are deemed eligible for a means tested SUSI grant.
- Student Levy: €224 - payable by all students and is not covered by SUSI.

Find out More

Dr Nick Tosh
t. +353 91 495929
e. nick.tosh@nuigalway.ie

What Our Students Say

Ciarán Rafter | First Year Student

The BA (Arts with Data Science) is a fantastic programme. I was interested in Arts subjects but I knew I wanted to work in the STEM sector. This degree was perfect for me as it gives me the opportunity to pursue both. There is a strong focus on the career opportunities available to us at the end of the four years so the entire class is very motivated with very clear-cut goals in mind. Overall, I’d recommend this course to anyone with an interest in mathematics and programming as well as an interest in pursuing any of the available Arts subjects at NUI Galway.

James Spillane | First Year Student

I attended the introduction to BA (Arts with Data Science) and instantly knew it was what I wanted to do. I had a keen interest in Maths and Economics and this course allowed me to combine both. I also knew that Data Scientists were extremely sought after in the world today therefore this course offered me the ability to work nationally or internationally. Once I began to study Data Science I haven’t looked back and with a small class size, we all got to know each other and the Data Science lecturer personally. Throughout the year the lecturer was in constant contact with us offering help with any problems. Before both Semester 1 and Semester 2 exams he offered us individual help with any questions we had regarding different subjects. I can genuinely say I enjoy each part of this course and am looking forward to starting second year

Eimear Walsh | First Year Student

I chose to study BA (Arts with Data Science) at NUI Galway because I liked the idea of combining of a very topical and emerging discipline like Data Science with an Arts subject. I really enjoyed pursuing my Arts subject of choice, Psychology, while learning very useful and relevant skills like programming and statistical analysis. The lecturers and tutors are always happy to answer questions and help out, as well as the tutors in the dedicated support centres. I have really enjoyed learning about how to analyse, interpret and visualise data correctly, and how valuable and useful these skills are in such a massive range of careers and sectors, now and even more so into the future. I am looking forward to incorporating my studies in psychology into future data science projects, as well as my work experience placement in 3rd year, where I’ll get to start applying my data science skills in the real world.

What Employers Say

Chris Chamberlain | Director of Data Analytics, Fidelity Investments

A career in Data Science means that you will always have a wide range of exciting opportunities to choose from. Your job may be to pick the next Facebook to invest in or to analyse the performance of an international sports team, anything is possible if you have the data. In an age when every small advantage matters, and marginal gains add up to a significant advantage, data is the what will make the difference.