GYE07 MSc in Computer Science (Data Analytics), Part-time - Industry Stream

The Part-time Industry Stream option of the MSc in Computer Science (Data Analytics) is aimed at students who wish to keep working while pursuing an MSc in Data Analytics over the course of two academic years.

Per year, part-time students will take about 40% of the taught course work of a full time student. They will undertake a larger industry-focused project and thesis (than full-time students), which must be completed over two years.

Duration: Part-time over 24 months

ECTS:90 ECTS (project, thesis and taught courses)1

Admission requirements: BSc in computer science or cognate degree, 5+ years of industry experience in senior technical role in industry and available to attend lectures in NUIG.

Visit our How to Apply? Section

Programme organisation:

  • 40 ECTS: industry-focused project and thesis to be completed over 2 years
  • 50 ECTS: of taught modules comprising a minimum 20 ECTS of core modules and 30 ECTS selected from the advanced modules on offer. To be completed over 4 semesters

Industry focused project

Students are advised to propose an industry-focused analytics project (ideally based on analytics research problems within the employer organization).

To apply:Contact the course director: Dr Conor Hayes conor.hayes@nuigalway.ie

1 an ECTS is a standard academic ‘credit’ defined by the European Credit Transfer and Accumulation System

Example Scenario

Year 1:

Semester1: 15 ECTs

  • Programming for Data Analytics (Core) (5 ECTS)
  • Data Mining and Machine Learning (Core) (5 ECTS)
  • Statistics for Engineers (Core) (5 ECTS)

Semester 2: 10 ECTS

  • Web Mining (5 ECTS)
  • Natural Language Processing (5 ECTS)
  • Independent Learning: Project requirements and initial draft report defined in Semester II.

Trimester 1

  • Independent Learning: Project (phase 1)

Year 2:

Semester1: 15 ECTs

  • Tools & Techniques for Large Scale Data Analytics (Core) (5 ECTS)
  • Probability (Core) (5 ECTS)
  • Modern information Management (5 ECTS)
  • Independent Learning: Project phase II.

Semester 2: 10 ECTS

  • Data Visualisation (5 ECTS)
  • Advances in Machine Learning and Information Retrieval (5 ECTS)
  • Independent Learning: Project phase III

Trimester 2

  • Independent Learning: Project (final phase and thesis write-up)
  • You may choose to front load semesters or years to suit your schedule.
  • E.g. You may take more modules in semester 1 or year 1 to free up time in semester 2 or year 2.
  • The minimum constraint is that you achieve 50 taught ECTs (20 ECTs core and 30 ECTS advanced) over 4 semesters (2 years)
  • Independent learning assumes the student can accomplish the learning task with occasional contact from a supervising academic