The minimum entry requirement is a Second Class Honours Grade 2 (GPA 2.5 or equivalent), in a NFQ Level 8 Degree in Computing, Science, Engineering, Business with IT, or equivalent qualification.
The acceptance of candidates with Third Class Honours degrees and appropriate work experience and industrial certification on this course will be allowed provided there is evidence that the candidate can cope with the learning objectives of the course.
Applicants will be requested to submit the following information to enable us score and rank applicants in order of merit
- A Curriculum Vitae
- Copy of your level 8 award, transcripts for each year showing modules studied and classification of the award. Where transcripts are not in English, you must provide a copy of the original language documents and certified English translations.
- You will also be asked to answer five questions relating to your experience in working with data, statistics, interest in the course etc. We will email the template for completion on receipt of your online application.
- If English is not your first language you will need to provide evidence of your English language proficiency as detailed on our website. Applicants for this programme should have a minimum IELTS (Academic Version) English Proficiency of 6.5 overall (or equivalent)
Data analytics, the analysis of both large and small data sets, has become a fundamental source of valuable information derived from ever increasing volumes of structured and unstructured data. Data analytics applications cover a variety of organizations and industries, and remains mission critical for businesses as it turns information into an asset for deriving insight and making decisions. This reflects the need for companies to do business more smartly, enabled by business intelligence.
Increased user activity has resulted in significant growth in data, both structured and unstructured. The value of this data is dependent on appropriate analysis of the data, and the subsequent application of analysis results. Consequently, data analytics has become a fundamental element for both private sector and public sector organisations that wish to compete through ever-evolving technology, productivity advancement, and innovation in research and development.
Globally, there is a reported shortage of data analytics talent particularly individuals with the required ‘deep analytical’ skills. In Ireland, government policy in recent years has consistently identified data analytics as a key growth area with a medium-term goal to become a leading country in Europe for big data and analytics.
This award winning programme has run for over 10 years in an online format and is open to both domestic and overseas students. It will be of particular value to holders of a primary degree in computing, IT, or equivalent, working as IT professionals. It is also of value to individuals with a computing degree background who wish to develop their career towards working within a research-oriented environment at a postgraduate level.
Graduates from this programme are equipped for employment in sectors where data analysis is a critical component, such as the insurance, retail, pharmaceutical, biotechnology, business, travel, telecommunication, government, and intelligence sectors.
Following successful completion of this course, graduates have taken up data analytics jobs with Accenture, SAP, FBD Insurance, Deutsche Bank, IBM, Eircom, Emirate airlines and PayPal, while one graduate has started his own analytics consultancy firm. Many students registered on this stream were already working in data analytics, or aim to start a data analytics function with their current employer.
Former students come from a variety of industry sectors and companies including:
- Aer Lingus
- Dublin Airport Authority
- Mallon Technologies
- Bank of Scotland
- Arvato Finance Solutions
- Samba Financial Group (India)
- Sky Ireland
- VHI Healthcare
- United Healthcare Group
- Nathean Technologies
- Compass Informatics
Year 1 Semester 1
- Business Intelligence
- Data Mining Algorithms
Year 1 Semester 2
- Data Pre-processing and Exploration
- Data Science Applications
Year 2 Semester 3
- Text Mining and Web Content Mining
- Geospatial Data Mining and Knowledge Discovery
- Programming for Big Data
- Multimedia Mining
* Two electives must be selected
Year 2 Semester 4
- MSc Research Project
- Online Year 1: Tuesday and Wednesday 6pm-10pm.
- Online Year 2: Wednesday and Thursday 6pm-10pm.
All classes are recorded
2020 Applications Now Open
Before making your application and paying the non-refundable €50 application fee please read the following guidelines. https://tudublin.ie/study/part-time/how-to-apply/
The closing date to apply and submit supporting documentation is Friday 12th June 2020
My job involves a lot of complex analysis, and because of the practical nature of the course I have learned how to make complex analytics more consumable to a wider audience. As a student, I was really busy, but on projects that were interesting to me. I use the analytics and documentation skills I learned on the course every day.
The course teaches students how to clearly present a problem, and using analytics better understand the problem. This is a key skill in industry as it helps shape better solutions and better products. I have now a broader knowledge base and am better prepared when asking questions around analytics and the desire for more understanding of how a product is used.
The key differentiating factor is this course is that it's delivered remotely, so this made it ideal for me as a working mum. I was able to get an education and all the support I needed without having to sacrifice family time. When I was studying, my husband had to travel, and we have a young family. The remote nature of the course really worked for both my family needs and my educational needs. Even though the course was remote, we still had a great connection online with everyone in the class. When I couldn’t make the lecture, the recordings were uploaded straight away, and I had access to the material the following morning. I would certainly recommend this course
Graduate of Master of Science in Computing (Applied Data Science & Analytics)