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Data Analytics

This computing course aims to produce high-quality, technically competent, innovative graduates that will become leading practitioners in the field of data analytics.

Upon completion of this course, graduates will be able to:
• Conduct independent research and analysis in the field of data analytics
• Implement a research idea using the latest industry practices
• Demonstrate expert knowledge of data analysis and statistics
• Critically assess and evaluate business and technical strategies for data analytics
• Develop and implement business and technical solutions for data analytics

About this Course
The course structure accommodates a wide audience of learners whose specific interests in data analytics may be either technically focused or business focused. All students will also gain exposure to pertinent legal issues and product commercialisation considerations associated with the data analytics field.

The course will be delivered using academic research, industry-defined practical problems, and case studies. This approach will naturally foster a deeper knowledge of the subject area and create transferable skills for work such as critical thinking, problem-solving, creative thinking, communication, teamwork and research skills. The course is completely delivered by faculty and industry practitioners with proven expertise in data analytics.

Who is the course for?
This course is ideal for graduates that are looking to progress into the emerging data analytics market to increase their employment potential. The course is suitable for graduates who have technical or mathematical problem solving skills. Graduates from disciplines that have not developed these skills will need to be able to demonstrate an aptitude for technical or mathematical problem solving.

Entry requirements

A minimum of a level 8 (honours degree) qualification(2.2 or higher) on the National Qualifications Framework. Applicants may be from a cognate/STEM background and standard applicants for the programme are those holders of computing or numerate degrees.

For candidates who do not have a level 8 qualification, the college operates a Recognition of Prior Experiential Learning (RPEL) scheme - meaning applicants who do not meet the normal academic entry requirements, may be considered based on relevant work or other experience. Non-English speaking applicants must demonstrate fluency in the English language as demonstrated by an IELTS academic score of at least 6.5 or equivalent.

This programme has a BYOD (Bring Your Own Device) policy. Specifically, students are expected to successfully participate in lectures, laboratories and projects using a portable computer (laptop/notebook) with a substantial hardware configuration. The minimal suitable configuration is 8GB of RAM (16GB are recommended); a modern 64-bit x86 multi-core processor (Intel i5 or superior); 250+ GB of available space in hard disk; WiFi card; and a recent version of Ubuntu, macOS or Windows.

It is the responsibility of each student to ensure their computer is functioning correctly and that they have full administrator rights. NCI IT cannot provide support for these personal devices.

Duration

Part-time
This course runs over 2 years; 4 semesters.

Indicative Schedule
This course takes place two evenings per week from 6pm-10pm and every second Saturday. This schedule is for indicative purposes

Full-time
This course runs over 1 year; 2 semesters with a final research project.

Indicative Schedule
Students need to be available 09.00-18.00 Mon – Fri (class days and times vary).

Please note that exams can be scheduled during the morning, afternoon or evening Monday to Saturday.

Careers or further progression

Progression
Students who successfully complete this course may progress to a major award at level 10 on the NFQ. Students may also elect to exit early with the Postgraduate Diploma in Science in Data Analytics at level 9 on the NFQ.

According to a recent Higher Education Outcomes Report released by the CSO, ICT graduates receive the highest weekly earnings five years after graduating compared to other sectors based on the analysis of the destinations of students who graduated between 2010 and 2016.

Further enquiries

For Further Information Contact:
The Admissions Team
Telephone: 1850 221 721
Email: admissions@ncirl.ie
Web: www.ncirl.ie

Subjects taught

Year 1-2
Core Modules
• Statistics for Data Analytics
• Data Warehousing and Business Intelligence
• Strategic ICT and e-Business Implementation
• Advanced Data Mining
• Data Visualisation
• Research in Computing
• Research Project or Industry Research Project

In addition, there is a choice of two electives (one in Year 1 Semester 2 and one in Year 2 Semester 1)

To find out more about the subjects taught on this course view the module descriptor.

Students on the course also have free access to DataCamp. Datacamp allows students to revisit and reinforce the knowledge acquired during lectures when and where they like.

Comment

Location: IFSC Campus
Award: The Master of Science in Data Analytics is awarded by QQI at level 9 on the National Framework of Qualifications.

Assessment method

Assessment
The course will be assessed with a blend of project work and exams. This varies between modules but typically assessment is 50% continuous assessment and 50% exam.

Please note that in some instances exams may take place in the daytime, evenings and at weekends.

Course fee

Part-time
Fees
€4,475 per annum €8,950 total fee (Fees revised annually)

Full-time
Fees
€6,500 total fee (EU/Ireland applicants)

You can spread the cost of this course with a direct debit plan.

International Students' Fees
The fee for this course for international students is €15,000.

Find out more about studying here as an international student.

Enrolment and start dates

Start Date: 14/09/2020

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