Key Info
Bachelor of Science in Mathematical Sciences
Entry route(s):
If you like mathematics and statistics but you aren’t totally certain what career you want to pursue, this might be a good course choice for you. Mathematical and statistical skills are highly valued by employers and are easily transferable. Mathematical Sciences, with its three options, is the perfect way to study something you like, while having a chance to think about your eventual career choice.
Why Study Mathematical Sciences at UL?
The programme is suited to students with an aptitude for mathematics and statistics who are interested in applying their skills to problem solving in the real world. It is designed to provide a broad training that will allow you to work in any environment that requires strong analytical and problem solving skills. The programme involves an introductory two years, common to all students, when the fundamental mathematical and statistical tools are introduced. After two years, you will have the option of specialising in mathematics, statistics or computing. The programme also provides a theoretical grounding for students who wish to pursue postgraduate studies.
Entry route to Mathematical Sciences at UL is via LM124 Mathematics Common Entry.
Learn more about our courses and upcoming events
What you will study
The programme is full time, of four years in duration. It includes a period of Cooperative Education during the spring and summer of the third year of the course where the skills that you have acquired are applied in an appropriate workplace. The first two years of the course provide a foundation in a broad range of areas including calculus, statistics, linear algebra, mechanics, computer science and mathematical modelling.
There is also an elective pair of modules in the first year in either (a) Computer Science or (b) Economics or (c) Finance/Accounting or (d) Physics.
The third and fourth years of the programme give you the opportunity to specialise in one of the following options:
Mathematics
The mathematics stream is aimed at giving you a rounded appreciation of mathematics and the ability to approach problem solving with a mathematical mind. It develops the analytical skills acquired in the first two years using mathematical modelling of real world problems. Topics covered include linear algebra, fluid mechanics, dynamical systems, mathematical modelling and numerical solution of partial differential equations, perturbation methods, stochastic differential equations.
Statistical Data Science
The statistical data science stream aims to develop strong statistical, data modelling and programming skills to support the increasing need for graduates with these key skillsets. Application areas include marketing, product development and testing, finance, economics, sociology, medicine, and sports science. Topics covered range from the mathematical basis of statistics through to the use of specialised software in the analysis of large, complex sets of data. The modules include statistical inference, statistical modelling, experimental design, time series analysis, big data analytics and multivariate analysis.
You will undertake a project in your final year that reflects your area of specialisation and, if possible, your Cooperative Education experience.
In Years 2, 3 or 4, students can apply to spend a semester studying abroad at one of our partner institutes worldwide.
Semester 1 | Semester 2 | ||
MS4021 | Calculus 1 | MS4022 | Calculus 2 |
MS4131 | Linear Algebra 1 | MS4122 | Further Linear Algebra |
CE4701 | Computer Software 1 | MS4222 | Introduction to Probability & Statistics |
Choose 2: | CE4702 | Computer Software 2 | |
MS4101 | Mathematics Laboratory | Choose 1: | |
AC4213 | Financial Accounting | AC4214 | Accounting for Financial Decision Making |
EC4111 | Microeconomics | EC4112 | Macroeconomics |
PH4051 | Measure & Properties of Matter | CS4182 | Foundations of Computer Science 2 |
PH4131 | Mechanics/Heat/Electricity/Magnetism | PH4102 | Wave/Light/Modern Physics |
CS4221 | Foundations of Computer Science 1 |
Semester 3 | Semester 4 | ||
MS4043 | Methods of Linear Analysis | MS4014 | Introduction to Numerical Analysis |
MS4035 | Probability Models | MS4034 | Applied Data Analysis |
MS4403 | Ordinary Differential Equations | MS4303 | Operations Research 1 |
MS4613 | Vector Analysis | MS4404 | Partial Differential Equations |
Chose one: | MS4414 | Theoretical Mechanics | |
CS4013 | Object Oriented Development | ||
MB4005 | Analysis |
Semester 5 | Title | Semester 6 | Title |
---|---|---|---|
MA4617 | Introduction to Fluid Mechanics | Cooperative Education | |
MS4008 | Numerical Methods for PDEs | ||
MS4045 | Complex Analysis | ||
MS4105 | Linear Algebra 2 | ||
Elective - Choose 1 | |||
CS4416 | Database Systems | ||
MS4215 | Advanced Data Analysis | ||
MS4217 | Stochastic Processes | ||
MB4017 | Geometry |
Semester 7 | Title | Semester 8 | Title |
---|---|---|---|
MS4407 | Perturbation Techniques & Asymptotics | MS4018 | Dynamic Systems |
MS4627 | Mathematics of Natural Phenomena | MS4408 | Mathematical Modelling |
Elective - choose 1 | Elective - Choose 1 | ||
MS4417 | Project 1 | MS4418 | Project 2 |
MS4037 | Statistical Data Science Project 1 | MS4038 | Statistical Data Science Project 2 |
Elective - Choose 2 | Elective - Choose 2 | ||
CS4178 | Software Requirements and Modelling | CS4115 | Data Structures and Algorithms |
MS4027 | Fundamentals of Financial Mathematics | MS4028 | Stochastic Differential Equations for Finance |
MS4117 | Discrete Mathematics 2 | MS4218 | Time Series Analysis |
MS4315 | Operations Research 2 | MS4327 | Optimisation |
MS4528 | Mathematical and Statistical Models of Investments |
Semester 5 | Title | Semester 6 | Title |
---|---|---|---|
MS4105 | Linear Algebra 2 | Cooperative Education | |
MS4214 | Statistical Inference | ||
MS4215 | Advanced Data Analysis | ||
MS4217 | Stochastic processes | ||
Elective - Choose 1 | |||
MS4617 | Introduction to Fluid Mechanics | ||
MS4008 | Numerical Partial Differential Equations | ||
MB4017 | Geometry | ||
CS4416 | Database Systems |
Semester 7 | Title | Semester 8 | Title |
---|---|---|---|
MS4037 | Statistical Data Science Project 1 | MS4038 | Statistical Data Science Project 2 |
MA4007 | Experimental Design | MA4128 | Advanced Data Modelling |
MS4218 | Time Series Analysis | ||
Elective - choose 3 | Elective - Choose 2 | ||
MS4315 | Operations Research 2 | MA4708 | Quality Control |
MS4045 | Complex Analysis | MS4018 | Dynamical Systems |
MS4117 | Discrete Mathematics 2 | MS4028 | Stochastic Differential Equations for Finance |
MA4617 | introduction to Fluid Mechanics (if not chosen in year 3) | MS4327 | Optimisation |
MS4027 | Fundamentals of Financial Mathematics | MS4528 | Mathematical and Statistical Models of Investments |
CS4337 | Big Data Management & Security | CS4168 | Data Mining |
Semester 5 | Title | Semester 6 | Title |
---|---|---|---|
CS4178 | Software Requirements and Modelling | Cooperative Education | |
CS4416 | Database Systems | ||
Elective - choose 2 | |||
MA4617 | Introduction to Fluid Mechanics | ||
MS4008 | Numerical Partial Differential Equations | ||
MS4045 | Complex Analysis | ||
MS4105 | Linear Algebra 2 | ||
MS4017 | Geometry | ||
Elective - choose 1 | |||
MS4214 | Statistical Inference | ||
MS4215 | Advanced Data Analysis | ||
MS4217 | Stochastic processes |
Semester 7 | Title | Semester 8 | Title |
---|---|---|---|
MS4117 | Discrete Mathematics 2 | MS4327 | Optimisation |
Elective - choose 1 | Elective - choose 1 | ||
MS4417 | Project 1 | MS4418 | Project 2 |
MS4037 | Statistical Data Science Project 1 | MS4038 | Statistical Data Science Project 2 |
Elective - choose 3 modules total | Elective - choose 3 modules total | ||
Choose 1 or 2 from | Choose 1 or 2 from | ||
CS4125 | Systems Analysis and Design | CS4006 | Intelligent Systems |
CS4337 | Big Data Management & Security | CS4115 | Data Structures and Algorithms |
CS4815 | Computer Graphics | ||
CS4084 | Mobile Application Development | ||
CS4158 | Programming Language Technology | ||
CS4168 | Data Mining | ||
And choose 1 or 2 from | And choose 1 or 2 from | ||
MS4007 | Experimental Design | MA4128 | Advanced Data Modelling |
MS4027 | Fundamentals of Financial Mathematics | MS4018 | Dynamical Systems |
MS4315 | Operations Research 2 | MS4028 | Stochastic Differential Equations for Finance |
MS4407 | Perturbation Techniques and Asymptotics | MS4218 | Time Series Analysis |
MS4627 | Mathematics of Natural Phenomena | MS4408 | Mathematical Modelling |
MA6011 | Cryptographic Mathematics | MS4528 | Mathematical and Statistical Models of Investments |
Entry requirements
Additional considerations |
Please refer to the entry requirements for |
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Non-EU Entry Requirements |
|
How to apply
Where are you applying from? | How to Apply |
---|---|
Ireland | Irish students must apply to UL via the CAO. More information can be found here. |
The UK | Students who have completed their A-Levels can apply to UL via the CAO. More information can be found on the Academic Registry website. |
The EU | EU Students can apply to UL via the CAO. More information can be found on the Academic Registry website. |
Non-EU country | If you are outside of the EU, |
Fees and funding
Student course fees are broken into three components - Student contribution, Student Levy and Tuition Fees.
A number of illustrative examples of fees for this course based on the current fee levels have been set out in the tables below.
An explanation of the components, how to determine status and the criteria involved is provided below the examples as is a list of possible scholarships and funding available.
EU Students with Free fees status in receipt of a SUSI grant
HEA pays | Tuition Fees | €4,262 |
SUSI pays | Student contribution | €3,000 |
Student pays | Student Levy | €100 |
€7,362 |
EU Students with Free fees status not in receipt of a grant
HEA pays | Tuition Fees | €4,262 |
Student pays | Student contribution | €3,000 |
Student pays | Student Levy | €100 |
€7,362 |
Students with EU fee status not in receipt of a grant
Student pays | Tuition Fees | €4,262 |
Student pays | Student contribution | €3,000 |
Student pays | Student Levy | €100 |
€7,362 |
Non-EU Students
Student pays | Tuition Fees | €20,900 |
Student pays | Student Levy | €100 |
€21,000 |
Student course fees are comprised of the following components:
Student Contribution
Annual charge set by the government for all full-time third level students. All students are liable unless they have been approved for a grant by (SUSI). Please refer to https://www.studentfinance.ie to determine your eligibility for a grant and for instructions on how to apply. The current student contribution is set at €3000.
Student Levy
All students are liable to pay the Student Levy of €100. Please note the Student Levy is not covered by the SUSI Grant.
Tuition Fees
These are based on Residency, Citizenship, Course requirements.
Review the three groups of criteria to determine your fee status as follows
-
Residency
- You must have been living in an EU/EEA member state or Switzerland for at least 3 of the 5 years before starting your course
-
Citizenship
- You must be a citizen of an EU/EEA member state or Switzerland or have official refugee status
-
Course Requirements
(all must be met)
- You must be a first time full-time undergraduate (Exceptions are provided for students who hold a Level 6 or Level 7 qualification and are progressing to a Level 8 course in the same general area of study).
- You must be undertaking a full-time undergraduate course of at least 2 year’s duration
- You cannot be undertaking a repeat year of study at the same level unless evidence of exceptional circumstances eg serious illness is provided (in which case this condition may be waived)
Depending on how you meet these criteria your status will be one of the following -
- Free Fee Status: You satisfy all three categories (1, 2 and 3) and therefore are eligible for the .
- EU Fee Status: You satisfy both the citizenship and residency criteria but fail to satisfy the course requirements and are liable to EU fees.
- Non EU Fee Status: You do not meet either the citizenship or residency criteria and are therefore liable to Non EU fees.
More information about fees can be found on the Finance website
These scholarships are available for this course
Title | Award | Scholarships Available |
---|---|---|
BD Science and Engineering Undergraduate Scholarship | €2,000 for one year | 6 |
Johnson and Johnson WiSTEM2D Programme | ||
Royal Irish Academy Hamilton Awards – Mathematics | €250 | 1 |
The Critchley Prize | 1 |
These scholarships are available for all courses
Title | Award | Scholarships Available |
---|---|---|
All Ireland Scholarships - sponsored by J.P. McManus | €6,750 | 125 |
Brad Duffy Access Scholarship | €5,000 for one year | 1 |
Bursary for my Future Scholarship | €2,750 one off payment | 4 |
Cooperative Education Award | 1 medal per faculty | |
Elaine Fagan Scholarship | €1,500 | |
Financial Aid Fund | ||
Hegarty Family Access Scholarships | €5,000 for one year | 2 |
Higher Education Grants & VEC Grants | ||
Paddy Dooley Rowing Scholarship | €2,500 | |
Plassey Campus Centre Scholarship Programme | ||
Provincial GAA Bursaries Scheme | €750 | |
Stuart Mangan Scholarship | ||
The Michael Hillery and Jacinta O’Brien Athletics Scholarship | Various benefits equating to over €7,000 in value | |
UL Sports Scholarships | Varies depending on level of Scholarship | Multiple |
Your future career
Employability skills from this degree
- Designing and conducting observational and experimental studies
- Analysing and interpreting data, finding patterns and drawing conclusions
- Approaching problems in an analytical and rigorous way, formulating theories and applying them to solve problems
- Dealing with abstract concepts
- IT skills
- Advanced numeracy and analysing large quantities of data
- Logical thinking
Further Study Options
- PhD in Mathematics and Statistics
- PhD in Statistics
- MSc in Mathematical Modelling
Job titles for graduates with this degree
Graduates progressing directly into employment take up a wide variety of roles. The following provides a sample of initial roles listed on the Graduate Outcomes Survey by graduates approximately one year after graduation:
- Actuary
- Analytics Consultant
- Business Analyst
- Commodity Analyst
- Marketing Analyst
- Master Data/SRM Analyst
- Risk Analyst
- Software Engineer
Student Profiles
Sarah Murphy
I chose UL for this course, but also because I’d never met a UL student who didn’t seem to love their time here. Maths and applied maths were my favourite subjects in school and this course seemed liked the perfect way to pursue those interests. For the first two years, you will establish a strong base in mathematics and statistics before specialising in your area of choice. In third year, you start to focus on your chosen specialty and then everyone goes on co-op placement. In your final year, you have the chance to pick a final year project in a topic that interests you.
This course has allowed me to develop essential skills needed to be a mathematician while also giving me the chance to apply them in a working environment. I completed my co-op placement in Analog Devices in 51±¾É«, one of the leading semi-conductor companies in the world. I worked as a part of the New Product Engineering team that specialised in data analytics. My job involved the statistical analysis of data from different stages of testing and gave me the opportunity to apply the skills I had learned in college to the real world. My communication skills, presentation skills and my ability to work effectively as part of a team were vastly improved during my co-op experience.
I think one of the great advantages of studying Mathematical Sciences at UL is that it opens up a broad range of career paths. The course doesn’t tie you down to one profession but instead gives you the essential mathematical skills that are in demand in every sector.
Colm Howlin
I really enjoyed Maths in school, so I decided to continue with it at University. I visited several campuses before my making my decision on which University to choose. UL had by far the most impressive campus which made the decision easy.
As Principal Researcher at Realizeit, I lead the analytics and research efforts. Realizeit is an adaptive learning company that has created a platform to deliver personalised learning online to students. The platform uses data to figure out what works best for individual students and uses that to personalise and adapt the delivery of learning material.
I work on the development and deployment of the algorithms that are used by the system to personalise the learning experience. This ranges from algorithms that estimate the difficulty of a question to algorithms that automatically detect when a student is bored. I also work with several Universities to help them understand the impact of adaptive learning on how their students learn. My role, as with most in the tech sector, predominately involves problem-solving. The course not only provided me with the foundations in the tools that I would rely on in my career but more importantly, helped me develop my problem-solving skills.
Any advice for school leavers?
Study what you think you will enjoy, and you’ll set yourself up to have a far more successful and happier career than forcing yourself to study something that is supposed to lead to a good job or career.
Kevin Brosnan
Throughout second level education numeracy based courses, maths and accounting, were my strongest - and not knowing what I could do with that my intention was always to become a second level maths teacher. After being encouraged by my maths teacher I looked at maths courses across Irish universities, and chose UL after speaking to a number of students on the course and after visiting the amazing campus.
The first two years formed a basis of maths, statistics and computing, but it was the third year which changed my career trajectory. My co-op placement was with Accenture Analytics in Dublin working with large enterprises to identify and prevent fraud using statistics and data, a field we now commonly call Data Science. After my co-op placement I was gripped by the thought of using statistics and computing to prevent fraud and risk. I finished my final year and then enrolled for a PhD at UL - during which time I investigated cheating in elite athletics and worked with a company developing fraud detection tooling in the Motor Insurance Space.
After finishing my PhD, I moved into the financial services space, and have worked with two global payment processors designing statistical and machine learning systems which detect, prevent and manage payment fraud in real-time - every transaction you do is scanned through a statistical model to evaluate the likelihood of the transaction being fraudulent in milliseconds!
Today I work on strategic risk management and product development, where I use my knowledge of fraud and risk, as well as mathematics and statistics, to protect companies and consumers from sophisticated fraud attacks. While I don’t write algorithms, or solve difficult equations, my background in logical thinking and understanding of data is essential to risk, design and strategic decisions I make every day.