Data Analytics

Course Description
This Master of Science in Data Analytics is a one-year full-time or two-year part-time Level 9 programme primarily aimed at students with strong numeracy skills who wish to extend their skill-set to a more advanced level of theoretical knowledge and practice and to further develop their research capabilities and innovation skills in the area of Data Analytics. The programme takes an integrative approach, focusing on the synthesis of knowledge and practice from specialist areas within the data analytics and computing domains.

The aim of this programme is to produce highly-skilled graduates with expertise that cuts across the core disciplines of Mathematics, Statistics and Computer Science. It emphasises the critical connection from data to information, from information to knowledge, and from knowledge to decision making, encompassed in the Data-Lifecycle.

The educational aim is to develop students' analytical, critical thinking, problem-solving and communication skills and to foster their research capabilities and innovation skills in the area of Data Analytics.

The primary aim is to develop expert knowledge in aspects of data required to become highly-skilled Data Analysts: with modules in Statistics, Data Visualisation, Time Series, Data Architecture, Machine Learning, Statistical Programming and Ethics in Data.

Who Should Apply?
- Individuals who are numerate, and have good programming skill-set;

- Individuals who like to be a part of cutting-edge technology that works to improve human-machine and machine-machine interactions;

- Individuals who wish to extend their skill-set to a more advanced level in the area of Data Analytics to improve their career prospects or make a career change.

Subjects taught

Data Architecture (part 1 of 2)
Statistics
Programming for Data Analytics
Research Process for Data Analytics
Data Architecture (part 2 of 2)
Data Visualization and Insight
Time Series Analysis
Machine Learning
Ethics in Data Analytics
Research Project

Entry requirements

Minimum Entry Requirements
The standard minimum entry requirement is a 2.2 Honours Degree (NFQ Level 8) or equivalent in the area of Mathematics, Statistics, Computing, Engineering, Science or equivalent qualification in a cognate discipline, which includes a minimum of 15 credits in Mathematics and Statistics and 10 credits of programming centric modules.

The Higher Diploma in Science in Data Analytics Level 8 award is seen as an entry route to this programme.

Application dates

How to Apply
You should apply using the DkIT Postgraduate Application (see "Applocation Weblink" below).

Please include an up-to-date CV with your application form and send your completed application to Admissions Office, Dundalk Institute of Technology, Dublin Road, Dundalk, Co. Louth.

Applying as an International Student? Please see applying as an International Student section of Provider's website.

Duration

1-2 Years (Full time or Part-time).

Course Delivery
This course is delivered using a mix of lectures, workshops, labs, tutorials and independent reading, continuous assessment projects etc.

It is delivered full-time over 3 semesters in 1 year (including summer) while the part-time delivery mode takes place over 2 years.)

Course delivery may be affected by the ongoing Covid-19 situation and it may be delivered remotely. All changes related to the course delivery and examinations will be communicated in advance. Important regular updates on Covid-19 can be found at dkit.ie/coronavirus.

Post Course Info

Career Opportunities
These career paths can take place in many different sectors and industries including areas such as research; academia; business; finance; banking; healthcare; pharmaceuticals; commercialism; and technology.

Graduates from MSc in Data Analytics can find Industry roles such as Data Analyst, Data Scientist, Data Engineer, Data Architect, Business Data Analyst, Data Visualisation developer and Statistician.

More details
  • Qualification letters

    MSc

  • Qualifications

    Degree - Masters (Level 9 NFQ)

  • Attendance type

    Full time,Part time,Daytime

  • Apply to

    Course provider