UCC Undergraduate courses

Data Science and Analytics

About This Course

Fact File

  • Title

    Data Science and Analytics

  • Code

    CK411

  • College

    Science, Engineering and Food Science

  • Duration

    4 years

  • Teaching Mode

    Full-time

  • Qualifications

    BSc (Hons)

  • Fees

    Student Contribution + Capitation: €3,250 See Fees and Costs for full details.

  • Entry Requirements

    1 x H3, 1 x H5, 4 x O6/H7; H3 in Maths. See Requirements for full details.

  • CAO Points

    2019: 509

Course Outline

Data Science & Analytics focuses on new ways to capture and understand data from the world around us.  It helps us make better decisions for people, communities and industry. The BSc in Data Science & Analytics at UCC provides an education in data storage, manipulation and interpretation using mathematical sciences and computational methods which assists us solve complex real-world problems. In the first and second year, you will study the mathematical and computational foundations of data science and analytics. During the third year, you begin applying the fundamentals of data science and analytics to real life problems and data. In Spring of third year, you undertake a six-month work placement (paid in most cases) providing an opportunity to apply the knowledge you have learnt in a work-place environment. In the fourth year, you choose specialised modules and undertake an independent project, which enables you to investigate more applied elements of the discipline. Firms specialising in analytics, financial services and consulting, and governmental agencies to name but a few are currently seeking graduates with data analytics skills to fill a range of positions.

Year 1

CORE:

  • CS1106 Introduction to Relational Databases (5 credits) – Dr Kieran Herley, Semester 1
  • CS1112 Foundations of Computer Science I (5 credits) – Prof Barry O'Sullivan, Semester 1
  • CS1113 Foundations of Computer Science II (5 credits) – Prof Barry O'Sullivan, Semester 2
  • CS1117 Introduction to Programming (15 credits) – Dr Jason Quinlan, Semesters 1 & 2
  • AM1054 Mathematical Software (5 credits) – Dr Andreas Amann, Semester 1
  • MA1058 Introduction to Linear Algebra (5 credits) – Dr Andrei Mustata, Semester 1
  • MA1059 Calculus (5 credits) – Dr Martin Kilian, Semester 1
  • ST1050 Statistical Programming in R (5 credits) – Ms Fengyun Gu, Semester 1
  • ST1051 Introduction to Probability and Statistics (5 credits) – Dr Shirin Moghaddam, Semester 2

ELECTIVES:

AM1053 Introduction to Mathematical Modelling; ST1401 Introduction to Operations Research (5 credits each)

Year 2

CORE:

  • CS2208 Information Storage and Management I (5 credits) – Dr Alejandro Arbelaez, Semester 1
  • CS2209 Information Storage and Management II (5 credits) – Dr Alejandro Arbelaez, Semester 2
  • CS2513 Intermediate Programming (5 credits) – Dr Laura Climent, Semester 1
  • CS2514 Introduction to Java (5 credits) – Dr Marc van Dongen, Semester 2
  • CS2515 Algorithms and Data Structures I (5 credits) – Prof Ken Brown, Semester 1
  • CS2516 Algorithms and Data Structures II (5 credits) – Prof Ken Brown, Semester 2
  • MA2055 Linear Algebra (5 credits) – Dr Andrei Mustata, Semester 1
  • MA2071 Multivariable Calculus (5 credits) – Dr Eduardo Mota, Semester 1
  • ST2053 Introduction to Regression Analysis (5 credits) – Dr Michael Cronin, Semester 1
  • ST2054 Probability and Mathematical Statistics (10 credits) – Dr Liang Chen, Semesters 1 & 2

ELECTIVES:

AM2052 Mathematical Modelling; ST2402 Modelling and Systems for Decision Making (5 credits each)

Year 3

CORE:

  • CS3204 Cloud Infrastructure and Services (5 credits) – TBC
  • CS3205 Data Visualization for Analytics Applications (5 credits) – TBC
  • CS3220 Work Placement DSA (10 credits) – TBC
  • CS3306 Workplace Technology and Skills (10 credits) – Dr Marc van Dongen, Semester 2
  • CS3318 Advanced Programming with Java (5 credits) – Dr John O'Mullane, Semester 1
  • CS3509 Theory of Computation (5 credits) – Prof Michel Schellekens, Semester 1
  • ST3053 Stochastic Modelling I (5 credits) – Dr Kevin Hayes, Semester 1
  • ST3061 Statistical Theory of Estimation (5 credits) – Dr Supratik Roy, Semester 1
  • ST3069 Generalised Linear Models (5 credits) – Dr Michael Cronin
  • ST3070 Statistical Theory of Hypothesis Testing (5 credits) – Dr Supratik Roy

Work placement: 6 months (March to September) or 12 months (from March)

Year 4

CORE:

  • CS4701 Analytics Project for Computer Science (15 credits) or
  • ST4092 Data Analytics Project (15 credits)

and

  • CS4704 Algorithms and Data Structures for Analytics (5 credits) – TBC
  • CS4705 Computational Machine Learning (5 credits) – TBC
  • ST4060 Statistical Methods for Machine Learning I (5 credits) – Dr Eric Wolsztynski, Semester 1
  • ST4061 Statistical Methods for Machine Learning II (5 credits) – Dr Eric Wolsztynski, Semester 1
  • ST4069 Multivariate Methods for Data Analysis (10 credits) – TBC

Course Practicalities

Expected lecture/lab hours: This is a full-time course expecting a full-time commitment. The annual 60-credits workload typically equates to 12 hours of lectures per week and a comparable amount for laboratory work and tutorials.

Expected reading/practical hours: The course also demands a significant amount of additional time for study, reading, completion of project and assignment work.

Why Choose This Course

The BSc DSA at UCC is one of two such programmes being offered in Ireland and amongst a dozen offered worldwide. 

This programme will suit you if have an aptitude for mathematics, logic and computational thinking, an enquiring mind and a willingness to adapt.

The degree is jointly offered by the School of Computer Science and Information Technology and the Department of Statistics, UCC and is closely associated with Insight – the Centre for Data Analytics, which is a national SFI centre. UCC has expertise in both statistics and computer science that is second to none.

Graduates of the UCC BSc in Data Science and Analytics have vast opportunities in a wide variety of industries as there is a high global demand for graduates with data science expertise. Almost all sectors of the economy and community need to understand the enormous data sets available.

Placement or Study Abroad Information

The BSc Data Science & Analytics aims to ensure that you are work-ready. Work placement is a core module undertaken from Spring until August in Year 3.

To find a placement organisation, you will work with the Careers Service; they will help you with interviews and keep in contact with you during placement. Working in a company setting provides you with additional skills that cannot be taught through lectures or in the laboratory. During placement, you will work as part of a team to solve real problems. Your placement is jointly monitored by a UCC academic staff member and an employee of the company.

Skills and Careers Information

Job prospects are buoyant:

Gradates of Data Scientist are the most highly recruited degree according to InfoWorld, 2017.

  • Top in salary
  • Top in job satisfaction
  • Demand will only increase

IBM Predicts Demand for Data Scientists Will Soar 28% By 2020 (Forbes, May 2017)

Practically all sectors of the economy employ Data Scientists.  The following list provides some insight into sectors/companies that use such skills.

Banking e.g. AIB, BoI, Central Bank, Citi

Energy e.g. Bord Gas, Electric Ireland

Financial Services e.g. Elavon, Pramerica

Food & Agriculture e.g. Kerry, Glanbia

Gaming e.g. Paddy Power, Xanadu, Betbright

Government e.g. CSO, Revenue, HSE

Health e.g. HSE, HIQA, Optum

Insurance e.g. Allianz, Aviva, Aon

Management Consultancy e.g. EY, PWC, FTI Consulting, Deloitte, Accenture, Clarion, KPMG

Marketing, Media & Communication e.g. Core Media, Vodafone, Eir, 3

Pharmaceutical e.g. Abbott, Novartis, Regeneron, Johnson and Johnson, Pfizer

Research e.g. ESRI, Universities, Insight

Retail e.g. Dunnes Stores, Tesco, Super Valu, Amazon, Wayfair

Software e.g. SAS, SPSS

Sport e.g. Munster Rugby, GAA, RTE, Sky Sports

Technology e.g. IBM, EMC, HP, Apple, Microsoft, Google, Dell, Facebook, Intel, Version 1

 

 

Requirements

Leaving Certificate entry requirements

At Least six subjects must be presented. Minimum grade H3 in one subject, minimum grade H5 in one subject and minimum grade O6/H7 in four other subjects.  English and Irish are requirements for all programmes unless the applicant is exempt from Irish. 
Applicants will need to meet the following minimum entry requirements:

English

Irish

Maths

O6/H7

O6/H7

H3

Non-EU Candidates

Non-EU candidates are expected to have educational qualifications of a standard equivalent to the Irish Leaving Certificate. In addition, where such candidates are non-native speakers of the English language they must satisfy the university of their competency in the English language.

To verify if you meet the minimum academic and language requirements for this programme please visit our qualification comparison pages.

For more detailed entry requirement information please refer to the International website.

Mature Students Requirements

Please refer to the mature student entry requirements for details. 

Fees and Costs

Course fees include a tuition fee, student contribution fee and capitation fee. The state will pay the tuition fees for EU students who are eligible under the Free Fees Scheme. The annual student Contribution and Capitation Fees are payable by the student. In 2019/20 the Student Contribution Fee will be €3,000 and the Capitation Fee will be €250.

Please see Fees Office for more information.

Non-EU Fees

The Undergraduate Fees Schedule is available here.

How Do I Apply

Non-EU Applications

Applicants who are interested in applying for the programme can apply online.

For full details of the non-EU application procedure visit our how to apply pages for international students.

 

**All Applicants please note: modules listed in the course outline above are indicative of the current set of modules for this course, but these are subject to change from year to year. Please check the college calendar for the full academic content of any given course for the current year. 

In UCC, we use the term programme and course interchangeably to describe what a person has registered to study in UCC and its constituent colleges, schools and departments. 

Apply Now

For queries regarding course content or timetables please contact

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