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Course Details

Master of Science in Computing (Applied Data Science & Analytics)

Course code: BN529 Entry Route into programme:
  • 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. See detailed minimum entry requirements below
Duration: 2 years (4 Semesters)
NFQ level: 9
Schedule: Online Year 1: Tuesday and Wednesday 6pm-10pm. Online Year 2: Wednesday and Thursday 6pm-10pm. All classes are recorded
Award title: Master of Science
Credits for Full Award: 90 @ NFQ Level 9
Awarding Body:                     Technological University Dublin



Shortlisted in gradireland's Higher Education Awards 2019 postgraduate course of the year – Computer Science/Technology



Take a look at this short introductory video and get to know what an online classroom is like. Click Here

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This course is part-funded by Technology Ireland ICT Skillnet under the Training Networks Programme of Skillnets and by member companies. Skillnets is funded from the National Training Fund through the Department of Education and Skills. For further information see

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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.


Course Delivery

The online delivery of the MSc in Applied Data Science and Analytics has attracted students primarily from around Ireland, but also from across Europe and firther afield. Lectures are delivered live, using Adobe Connect online classroom environment which facilitates VoIP for lectures and students, screen sharing, web cam, file sharing, breakout rooms for group discussions, whiteboard, question and answer session and other facilities.

This, coupled with lecture recordings and other learning resources being made available through Moodle (our virtual learning environment) provides a truly flexible learning environment for all participants. Modules are assessed through continuous assessment work only. The course will focus on the the knowledge and skills to select, apply and evaluate data science and big data analytics techniques to discover knowledge that can add value to a company. Students will gain both an in-depth theoretical understanding and practical hands-on experience, including implementing novel and emerging techniques. Participants will be kept abreast of current research and state of the art in data science related topics. The two year programme is delivered over four semesters.

The courses are suitable for both entrants to a new discipline that require a broader range of taught modules to familiarize themselves with the skills and knowledge of the discipline and for specialist employees who want to up-skill in their specialist areas and require a narrow range of taught modules and more emphasis on the research skills project of the degree.

With our new degree structure, we seek to be flexible and responsive to industry needs while still maintaining a high standard of academic excellence.

Students will be required to take a minimum of 3 taught modules worth 10 credits each, giving a total of 30 credits.  Students then have the option of two different paths to complete the 90 credits required for this master’s degree.  One option is to take three more taught modules and a 30-credit research project.  This path would suit students who are new to the discipline and require more taught modules and a smaller research project.

The other path is to take a 60 credit research project module route.  This path would suit students who have a background in the discipline and who wish to dedicate more time to the research element of the masters and develop their research capabilities.



Who Should Apply for the MSc in Computing?

The MSc in Computing programme is 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.


Minimum Entry Requirements

The minimum entry requirement is a 2nd Class Honours Grade 2 (GPA 2.5 or equivalent), in a NFQ level 8 Degree in Computing, Science, Engineering, Business with IT, or equivalent.

Applicants not meeting this entry requirement may be admitted to the programme on the basis of extensive practical and/or professional experience which can be assessed by the Institute’s APL/APEL process.

Students progressing through the Higher Diploma in Science in Computing (BN509) who wish to enter the M.Sc. in Computing must attain a 2nd Class Honours Grade 2 (GPA 2.5) on the course.

In the event of a student not attaining this standard level , students must achieve an acceptable standard for progression by other means approved by QQI.

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.

Candidates may be interviewed to assess their suitability to undertake the level of work required and to assess their commitment to succeding on the MSc in Computing Programme.


Careers Opportunities

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 the MSc, 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: Ericsson, IBM, Microsoft, PayPal, Intel, O2, Vodafone, Aer Lingus, Ryanair, Dublin Airport Authority, GlaxoSmithKline, Mallon Technologies, Bank of Scotland, Arvato Finance Solutions, Samba Financial Group (India), Sky Ireland, VHI Healthcare, United Healthcare Group, Nathean Technologies, Compass Informatics and MTT.



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

Electives :

  • Text Mining and Web Content Mining
  • Geospatial Data Mining and Knowledge Discovery
  • Programming for Big Data
  • Statistics
  • Multimedia Mining

Two electives must be selected

Year 2 Semester 4
  • MSc Research Project


An award of ‘Postgraduate Diploma in Science in Computing’ may be granted if learners leave this programme having completed 6 taught modules and attained 60 credits with a minimum GPA of 2.00.


Data Pre-Processing and Exploration

Module Aims:

  • To investigate the properties of data.
  • How to visualise data.
  • How pre-processing can enhance the information content of data.

Learning Outcomes:

  1. Discuss in depth a variety of data preparation techniques, and their applicability to various problem domains.
  2. Research current trends in data visualisation, and select the appropriate graphical representation for data and results.
  3. Understand the links between data and necessary pre-processing algorithms to improve as well as prepare the data for modelling purposes.
  4. Evaluate appropriate techniques to improve data quality, and be aware of the limitations of such techniques.
  5. Analyse a data set to assess what data preparation is required to both clean the data set and expose its information content.
  6. Highlight information content using data visualisation techniques.
  7. Independently research current trends and developments in data preparation techniques.


Business Intelligence

Module aims:

  • Investigate state of the art industry and research trends in business intelligence.
  • Conducting level 9 research and how to communicate results.

Learning outcomes:

  1. Evaluate the role and benefits of effective business intelligence in the organisation.
  2. Demonstrate awareness and critical understanding of developments in data warehouse design and implementation.
  3. Demonstrate awareness and critical understanding of developments in business intelligence front end tools and techniques.
  4. Independently research current trends and developments in business intelligence related technologies.
  5. Apply research methods to their work and differentiate between exploratory, constructive and empirical research.
  6. Demonstrate awareness and critical understanding of applications in the areas of ETL, Databases, and BI.
  7. Evaluate and critique current legislation on data privacy and relevant ethical issues.


Data Mining Algorithms

Module aims:

  • To study advanced concepts relating to data science. Using both lectures and independent research, the module will address a number of issues relating to understanding and optimising the performance of data mining algorithms.

Learning outcomes:

  1. Discuss in depth a variety of data mining techniques, and their applicability to various problem domains.
  2. Evaluate a business objective and related dataset to assess the appropriateness of a number data mining algorithms in achieving that objective.
  3. Work through the mining and evaluation stages of a data mining methodology, selecting the most appropriate mining technique, and optimising algorithm parameters to maximise performance
  4. Independently research current trends and developments in knowledge discovery related technologies.
  5. Critically analyse relevant publications to assess the relative merits of methodologies used and conclusions made.
  6. Self-evaulate work done.


Text Mining and Web Content Mining

Module aims:

  • Investigate state of the art and research trends in text mining and web content mining.
  • Critique and evaluate the performance of algorithms for both text mining and web content mining.

Learning outcomes:

  1. Demonstrate an awareness and critical understanding of ways to extract key concepts and relationships from semi-structured and unstructured text, and structure them for data mining.
  2. Discuss current research activities relating to text mining and web content mining.
  3. Understand limitations of current information extraction techniques and the vision for the future.
  4. Extract key concepts and relationships from semi-structured and unstructured data.
  5. Apply prediction and clustering techniques to the prepared data, and critically evaluate the results.
  6. Independently research current trends and developments relating to the processing of semi-structured unstructured data.


Data Science Applications

Module aims:

  • Apply state of the art business intelligence, data preparation and data mining techniques to a specific case study and dataset. Starting with a business objective and data, work through all stages of an appropriate methodology to extract knowledge from the data in accordance with the business objectives, and present the results to stakeholders in the appropriate language, highlighting how the knowledge learned can be used to add value to the business.

Learning outcomes:

  1. Research appropriate business intelligence or data mining techniques for a specific problem domain.
  2. Select from, and apply, a range of advanced, state of the art, data analysis, data visualisation and data mining techniques to a practical case study.
  3. Understand and interpret a business objective, and translate the business objective to business intelligence and data mining objectives.
  4. Identify possible risks and limitations of a data set in achieving business objectives.
  5. Apply the appropriate business intelligence and data mining techniques to match a business objective.
  6. Present results to stakeholders in terms of the business objectives set, and how the information learned can be used to add value to the business.


Multimedia Mining

Module Aims:

  • Traditional data mining has proved to be a successful approach to extracting new knowledge from collections of structured digital data usually stored in databases. Whereas data mining was done in the early days primarily on numerical data, the tools needed today are tools for discovering relationships between objects or segments within multimedia document components, such as classifying images based on their content, extracting patterns in sound, categorising speech and music, and recognising and tracking objects in video streams.
  • This module will introduce the fundamental concepts of multimedia data mining and will demonstrate how to apply proven mining techniques to large multimedia datasets.

Learning Outcomes:

  1. Describe techniques for feature extraction, selection and combination on multimedia data.
  2. Compare and contrast models and algorithms for mining multimedia datasets.
  3. Pre-process or clean multimedia data.
  4. Reduce the dimensionality of multimedia data whilst conserving the relevant information.
  5. Apply proven mining techniques for finding implicit patterns in large multimedia datasets.
  6. Characterise the performance of various mining algorithms on multimedia data.


Geospatial Data Mining and Knowledge Discovery

Module Aims:

  • To introduce the concepts and utility of geographically referenced data and geographic data mining for knowledge discovery in data.
  • To explore and critique data mining techniques and algorithms for mining data with a geographical component.

Learning Outcomes:

  1. Understand fundamental geographic concepts and principles underlying geograhpic data for GIS.
  2. Demonstrate awareness and critical understanding of challenges in mining geographically-referenced data in spatial database system.
  3. Apply appropriate (geographic) visualisation tools to the data for mining.
  4. Select and apply appropriate exploratory spatial data analysis, data preparation techniques and modelling algorithms to a practical case study.
  5. Independently research applications, trends and developments in Geographical Data Mining.


Programming for Big Data

Module Aims:

  • Students taking this module will acquire the computer programming skills necessary to analyse and manipulate big data. Big data in this context refers to datasets that are too large to be handled by the software tools commonly used to analyse and manipulate data within a tolerable elapsed time.
  • The algorithms and challenges for processing large datasets form a core part of this course, such that the student will be able to select the appropriate algorithms, tools or methods for big data problems in addition to being able to implement and evaluate solutions using a variety of programming techniques and tools. Students are not expected to have advanced programming skills in order to take the module, but will need to have fundamental knowledge and skills in computer programming.

Learning Outcomes:

  1. Clearly describe the characteristics of big data, and contrast the requirements for processing big data with conventional data.
  2. Identify and illustrate the challenges of programming for big data, and evaluate contrasting methods for addressing these challenges.
  3. Demonstrate a detailed understanding of the state of the art in Big Data algorithms and techniques.
  4. Select and evaluate the appropriate development tools for various big data programming problems.
  5. Demonstrate a detailed understanding of state of the art distributed programming paradigms for both data storage and data analysis, and select the appropriate method for a given context.
  6. Implement solutions to various big data programming problems using a range of state of the art tools and techniques, and evaluate the effectiveness of these solutions.
  7. Present an informed view of the changing big data landscape and how programming for big data may change in the future, based on current literature and standards.



Module Aims:

  • The purpose of this module is to provide the postgraduate student with the concepts, tools and techniques needed to undertake standard statistical analysis and to use these concepts to underpin their adoption of data mining techniques.

Learning Outcomes:

  1. Summarize large sets of data, including grouped data, using the standard measures of central tendency and dispersion and their definitions and properties, and represent it graphically, by following an agreed set of conventions.
  2. Apply the laws of probability to questions involving random variables and events, and move on to the concept of a random variable and its distribution, the meaning of expected values, and the properties of common distributions such as the normal, binomial, Poisson and exponential distributions.
  3. Interpret the concept of a statistic as a random variable arising from sample data, with the central limit theorem determining the behaviour of such statistics and thereby underpinning many statistical tests.
  4. Frame and use an appropriate test for a statistical problem, based on their knowledge of hypothesis testing, the central limit theorem and those distributions used in a range of common statistical tests. This will include multivariate analyses – Manova, Mancova.
  5. Design or explain the chosen structure of an experiment and the meaning of any data analysis produced for that experiment, based on the students understanding of the properties of Analysis of Variance and Analysis of Covariance and other statistical tests.
  6. Apply their knowledge of techniques derived from linear algebra to the matrix formulation of the general linear model, including eigenvector decompositions of the covariance matrix and their application to Principal Component Analysis.


MSc Research Project :


30 Credits:

  • Independent research project to give learners the experience of developing an individual computing project at postgraduate level. Learners will demonstrate their responsibility for substantial independent working and a full project from problem specification through to implementation and evaluation.

Learning Outcomes:

  1. Investigate various approaches to research enquiry and develop a research proposal.
  2. Write a literature review for selected research questions by reporting on relevant existing research demonstrating appropriate academic citation and referencing.
  3. Demonstrate the purposes and procedures involved in data gathering techniques, data analysis and discussion of results.
  4. Dissemination of results through presentations and research thesis documentation.

60 Credits:

  • This research module builds on your existing postgraduate experience to enable you to complete your training as a researcher. You’ll develop a research proposal by identifying and explaining a research problem relevant to your MSc. Your research will involve a literature review, data collection, data analysis, results and conclusions.
    You will then communicate the outcome of your research through presentations and dissertation report.

Learning Outcomes:

  1. Demonstrate self-direction and originality in planning tasks and solving problems during a research project.
  2. Prepare a comprehensive review or critical evaluation of existing research literature and/or professional guidance on a specific topic.
  3. Evaluate the research findings in relation to applicable techniques, theoretical limitations and experimental or design considerations.
  4. Analyse data showing originality in its interpretation in relation to scientific literature.
  5. Synthesise appropriate conclusions and findings through knowledge and systematic understanding of the research process and any limitations of the work.
  6. Communicate the outcomes of research to professional standards through a dissertation, poster and oral presentation.

Note: The closing date to apply for this course is 7th June 2019


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What our students say...

"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"                                                          Niambh Scullion                 Graduate of Master of Science in Computing (Applied Data Science & Analytics)


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