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BSc (Hons) in Mathematics and Data Science

Course Points
310 Points
Course Duration
4 Years
Course Level
Level 8
Course Start Date
September 2024

Two great reasons to consider this course

  • Data Analytics is identified, both nationally and internationally, as a key future skill and the demand for Data Scientists, Data Analysts and highly numerate graduates continues to grow alongside the massive amounts of available data.
  • This course will prepare students to work in the computing industry by helping to develop both their personal and professional skills in a supportive, positive, and student-centred environment .

Course Summary

This 4-year level 8 course is aimed at students who enjoyed Mathematics and Statistics at second level and are looking to develop the skills needed to enter the fast growing ICT sector, in particular in the area of Data Science and Data Analytics.

Data Analytics is identified, both nationally and internationally, as a key future skill and the demand for Data Scientists, Data Analysts and highly numerate graduates continue to grow alongside the massive amounts of available data. The course is well suited to numerate students who are looking to develop a more applied, industry-focussed skillset than offered by traditional mathematics courses.

The course takes an integrative approach, focusing on first building strong foundational knowledge in the areas of Mathematics, Statistics and Programming, and later facilitating the synthesis of knowledge and practice from high-tech areas within the data and computing domains. The primary aim is to develop specialist knowledge in aspects of data required to become highly-skilled sought-after Data Analysts:

  • Fundamental and Advanced Statistics techniques
  • Applied Probability
  • Data Visualisation techniques that bring the data to life
  • Time Series Analysis to investigate copious amounts of time data and trends
  • Machine Learning, the fast-paced and popular technique that is big data-driven.

These skills will be built alongside essential programming capability and Systems knowledge: Programming in R & Python, Data/Big Data and Distributed Data Systems, while developing strong mathematical reasoning and in-depth understanding in areas of Computational Mathematics and Mathematical Analysis: Logic, Number Theory, Linear Algebra, Calculus and Mathematical Modelling.


Industry Expert Interview

Listen to PJ O'Kane, VP for Technical Marketing and Innovation at First Derivatives, discuss the opportunities within the industry for graduates of this course.

YEAR 1

Semester 1

  • Data Science Fundamentals
  • Introduction to Programming for Data Analytics (year-long)
  • Probability & Statistics (year-long)
  • Pre-Calculus
  • Logic
  • Communication Skills

Semester 2

  • Bias in Computing
  • Tools for Software Development
  • Linear Algebra
  • Discrete Mathematics
  • Introduction to Programming for Data Analytics (year-long)
  • Probability & Statistics (year-long)

YEAR 2

Semester 1

  • Study Design
  • Database Systems
  • Object-Oriented Programming for Data Analytics (year-long)
  • Statistics (year-long)
  • Analysis 1 Geometry

Semester 2

  • Project Management
  • Big Data Systems
  • Number Theory
  • Analysis 2
  • Object-Oriented Programming for Data Analytics (year-
  • Statistics (year-long)

YEAR 3

Semester 1

  • Data Visualisation and Insight
  • Distributed Data Systems
  • Artificial Intelligence
  • Applied Probability Cryptography
  • Analysis 3

Semester 2

  • Work Placement
  • OR Approved Semester Abroad

YEAR 4

Semester 1

  • Research Methodology
  • Machine Learning (year-long)
  • Data Mining
  • Advanced Statistics 1
  • Project (year-long)
  • Differential Equations

Semester 2

  • Ethics & Professional Practice in Data Science
  • Time Series Analysis
  • Mathematical Modelling
  • Advanced Statistics 2
  • Machine Learning (year-long)
  • Project (year-long)

 

* All module titles are subject to change and are for indicative purposes only. The provision of electives each year is subject to numbers enrolling on each elective and available resources.

This course is delivered in a combination of lectures, lab classes and tutorials.

There are numerous support services available within the department to enable and encourage all students to realise their personal and professional potential.

This course builds advanced skills in programming, mathematics and statistics, databases and data mining, combined with highly developed problem-solving skills and will allow graduates to take on different roles in a variety of sectors. This course provides a stepping-stone to many others careers that value those who can think on their feet such as teaching, research and finance.

The typical roles that a graduate could take on include:

  • Data Analyst
  • Data Scientist
  • Data Wrangler
  • Data Curator
  • Data Architect
  • Business Analytics Specialist
  • Data Visualisation Developer
  • Junior Intelligence designer or Statistician

In additional to its industry focus, this programme also aims to provide a path for students wishing to pursue a 2nd level teaching career in Mathematics and Computer Science, dependent on Teaching Council requirements – graduates would be required to complete a Post-graduate Diploma in Education.

Graduates will have gained knowledge and developed advanced skills and competencies necessary to continue to an NFQ level 9 – Masters – or NFQ 10 doctoral programme – either taught, structured or by research.

Please find information on fees and funding here: www.dkit.ie/fees

Leaving Certificate Entry Requirements:

The standard Minimum entry requirements for this Level 8 ab-initio degree are:

Six grades at O6 or H7 in Leaving Certificate

INCLUDING At least two H5 Grades

AND Mathematics Grade O2 or H6

AND English Grade O6 or H7 OR Irish Grade O6 or H7


Northern Ireland/UK Entry Requirements:

DkIT recognises Standard A Levels, Applied A Levels, BTECs and OCR Cambridge Technical

qualifications, or a combination of all four. To be eligible for a NFQ Level 8 programme,

applicants must meet the matriculation requirement of 6 different subjects which must

include:

• Mathematics at:

o GCSE: Grade B

o Or AS Level: Grade C

o Or A Level Grade E

• English (or Irish) at GCSE (Grade A* - C) or better

• Two subjects at either:

• ‘A Level’ (Grade A* - C)

• AND/OR Applied A-Level (Grade A* - C)

• AND/OR BTEC National Level 3 (National Award, Subsidiary Diploma, Extended

Certificate, 90-Credit Diploma, Foundation Diploma) (Grade Merit or

Distinction).

• AND/OR OCR Cambridge Technical Level 3 (Introductory Diploma, Extended

Certificate, Subsidiary Diploma, Foundation Diploma, 90-Credit Diploma) (Grade

Merit or Distinction)

• OR BTEC National Level 3 Diploma (Grade min: MM)

• OR OCR Cambridge Technical Level 3 Diploma (Grade min: MM)

• OR BTEC National Level 3 Extended Diploma (Grade min: MMP)

• The remaining subjects must be different from that presented above and may be

drawn from recognised subjects at:

• GCSE (Grade A* - C)

• AND/OR AS Level’ (Grades A - E)

Bachelor of Science (Honours) in Mathematics and Data Science

Department of Computing Science & Mathematics 8

• AND/OR A-level’ (Grades A* - E)

• AND/OR Applied ‘A-level’ (Grade A* - E)

• AND/OR BTEC National Level 3 (National Award, Subsidiary Diploma, Extended

Certificate, 90-Credit Diploma, Foundation Diploma, Diploma or Extended

Diploma*) (Pass, Merit or Distinction).

• AND/OR OCR Cambridge Technical Level 3 (Introductory Diploma, Extended

Certificate, Subsidiary Diploma, Foundation Diploma, 90-Credit Diploma,

Diploma or Extended Diploma*)(Pass, Merit or Distinction).

 

Students on the Bachelor of Science (Honours) in Mathematics and Data Science will undertake a 15 week work placement in year 3, semester 2 (semester 5). Students also have the option of undertaking a year of study abroad.

This course is for you if:

  • you are a logical thinker, with a passion for mathematics and wish to develop strong mathematical reasoning and problem-solving skills
  • you wish to develop the skills to allow you to extract meaningful ethical information from data, building analytical solutions that can provide invaluable insights and trends in varied contexts and industries
  • you wish to develop the skills and expertise to enter the fast growing data industry and to carve your own career-path in this and other sectors.

Dr Siobhan Connolly Kernan (Lecturer )
Email: Siobhan.ConnollyKernan@dkit.ie

Course ID DK823
CAO Round 1 Entry Points (2023) 310 Points
Course Type Undergraduate
Study Mode Full-Time
Level 8
Duration 4 Years
Starting Date September 2024
School School of Informatics & Creative Arts
Department Computing Science and Mathematics
Awarding Body Dundalk Institute of Technology
Delivery Method On Campus

How To Apply

Apply on CAO

All standard entry first-year applicants must apply for entry through the CAO. See Important application dates for CAO and information for specific applicant types below:

CAO Code: DK823
Apply on CAO

Advanced Entry & Transfer Applications

Advanced Entry is for applicants who have previous educational achievements and/or work experience and want to be considered for direct entry into year  2, 3, or 4 of a course. This includes students looking to transfer to DkIT from another Higher Education provider.

Closing Date: 6th June
Apply Now

International Application (non-EU)

International Applicants (not from or living in the EU) can apply through an agent or directly to DkIT to study this course.

Disclaimer: All module titles are subject to change and for indicative purposes only. All courses are delivered subject to demand and timetables are subject to change. Elective Module options will only run subject to student numbers. The relevant Department will determine the viability of each elective module option proceeding depending on the number of students who choose that option. Students will be offered alternative elective modules on their programme should their preferred elective option not be proceeding. Award Options for Common Entry Programmes: The relevant Department will determine the viability of each award option proceeding depending on the number of students who choose either option. If the numbers for one of the Award options exceed available places, students for this option will be selected based on Academic Merit (highest grades).