MSc in Data Analytics Course Overview
The MSc in Data Analytics is a postgraduate masters degree course aimed at IT graduates and professionals, and graduates from cognate/numeric disciplines.
The majority of companies today realise the value of data driven business strategy and are in need of talented individuals to provide insight into the constant stream of collected information. Leveraging the value of big data for strategic advantage has become an increasingly standard business practice globally, resulting in an exponential skills shortage of data analysts. This programme aims to produce graduates who will be able to apply for roles pertaining to Data Analytics across all sectors of the economy.
Taught modules on the Masters in Data Analytics are followed by a Data Analytics project allowing students to apply their knowledge to a specialised applied Data Analytics problem which will be industry-initiated and used as the context for planning, designing, building and testing potential analytical solutions. Modules include, programming, statistics, technology enabled data analysis using machine learning and artificial intelligence, data visualisation, research and ethical studies pertaining to the field. Multiple transversal skills are also embedded throughout the programme including time management, communication, critical thinking and analysis, research, presentation as well as management and leadership development, ethics, project management and evaluation, and professional judgement.
Learners on the full-time course will likely be IT graduates or graduates of cognate disciplines and learners on the part-time programme will typically comprise IT professionals and/or graduates of cognate disciplines who are currently in employment and who require upskilling due to the accelerated pace of economic digital transformation.
The Masters (MSc) in Data Analytics programme will be delivered on a blended learning basis. Contact hours for the programme are a combination of traditional face-to-face classroom learning and virtual classroom also incorporating face to face and virtual lab sessions / workshops. Full time learners are required to attend 15 hours per week. Part time learners attend 8 hours per week, spread over 2 evenings and some weekend attendance would be required for campus based / virtual practical labs/workshops.
The programme leads to an award by QQI at Level 9 of the NFQ and consists of 60 credits of taught module work + 30 credits of an applied project. Learners who decide to leave the programme, after completing the taught elements only, may be entitled to receive the embedded exit award of a Post Graduate Diploma in Science in Data Analytics.
Read more about the MSc in Data Analytics below:
This programme aims to provide an opportunity for successful applicants to specialise in Data Analytics at MSc level (level 9). The programme provides graduates with the skills and competencies to facilitate the ever-increasing demand for the upgrading of legacy data solutions in line with the emerging necessity for digital transformation of multiple sectors to incorporate the vast amount of data available into a more advanced and insightful model.
The programme aims to incorporate the emerging technologies of machine learning and artificial intelligence to allow graduates to have a marketable skill set for today’s technical employment environment.
The overall objectives of this programme are:
- To provide a progression pathway to further specialise in the area of Data Analytics for
graduates of level 8 major awards in ICT or cognate discipline.
- To provide graduates with an award at level 9 on the National Framework of Qualifications.
- To provide graduates with the ability to advance their career by attaining a qualification which enables them to secure or advance in employment in a range of intermediate and advanced industry positions specific to Data Analytics.
- To provide the IT sector with graduates who possess the requisite attributes to make a positive contribution to industry.
- To provide graduates with the foundation upon which they can further their studies at level 10 (PhD) in Computing or one of many Computing-related disciplines (in Ireland or abroad) such as Computer Science, Computational Science, Information Systems, IT Management, Technology and Innovation Management, Information Security & Digital Forensics, Information Systems Processes, and others.
Contact hours for the programme are a combination of traditional face-to-face classroom learning and virtual classroom also incorporating face to face and virtual lab sessions / workshops. Full time learners are typically required to attend three days per week. Part time learners typically attend two evenings per week plus some weekend attendance for campus based / virtual practical labs/workshops. Students will also be required to undertake independent study to complete some out of class activities and assessment tasks each week.
The first two semesters comprise of the taught modules, and the third is a capstone project.
Stage 1 (Taught Stage)
Programming for Data Analytics
The aim of this module is to provide the learner with knowledge of fundamental analytical programming concepts, problem solving techniques applied in real world domains and complex data manipulation operations. Students will learn about optimisation and improvement of concurrency in existing programs as well as testing, quality control and maintenance. Students will be made aware of different programming languages, but Python will be the language used for the facilitation of the programme. Module content is assessed by 100% continuous assessment (CA), which facilitates the formal assessment of learners and the student level and cohort level monitoring of knowledge, skill and competence in respect of programming during and on completion of the module.
Statistics for Data Analysis
Numerical methods, particularly those pertaining to statistics and probability, are central to the domain of data analytics. This module will equip the learner with the statistical skills that are immediately applicable to data analysis tasks as well as serving as a foundation for more sophisticated techniques introduced in adjoining modules. This module also includes what is essentially an embedded ‘bootcamp’ of basic statistics to ensure a level playing field for all learners. Assessment for this module is 100% continuous assessment (CA) and will comprise of three assignments in total, to be completed throughout and at the end of the module.
Data Preparation and Visualisation
Extensive exploratory data analysis and proper data preparation are a crucial first step in any data analysis process. The aim of this module is to provide the learner with an in-depth understanding of the rationale for data exploration and the methods used to explore data programmatically with a high level, low entry barrier language (e.g. python), The student also learns the importance of feature selection and dimensionality reduction and the bias-variance trade-off, the importance of the correct encoding of data and the usefulness of feature engineering as a means of representing complex functional relationships to machine learning models. The module also deals with the theory and application of data visualisation methods and transmission media, tailored for diverse audiences. By incorporating basic programming skills in a hands-on practical integrated manner enables the learner’s ability to program but also reinforces the inseparable nature of programming within the field of Data analytics. This module also includes what is essentially an embedded ‘bootcamp’ of basic programming concepts to ensure a level playing field for all learners (facilitated through the use of a low entry barrier language: Python). This integrated approach to practical programming skills is continued in accompanying modules. Assessment for this module is 100% continuous assessment (CA) and will comprise of three assignments in total, to be completed throughout and at the end of the module.
Machine Learning for Data Analysis
Machine learning is the method that automates data analysis through analytical model building. This module will equip the learner with a wide range of machine learning skills and techniques necessary to understand and analyse large data sets. This module will also serve as the basis for more advanced data analytics introduced in adjoining modules. Assessment for this module is 100% continuous assessment (CA) and will comprise of three assignments in total, to be completed throughout and at the end of the module.
Research and Professional Ethics
This module provides learners with knowledge, skills and competencies within research, professionalism, ethics and governance and allowing them to practically connect learning to modules throughout the programme, and particularly, the applied data project in the final semester. The module deals with the ethical dilemmas commonly faced by industry, i.e. storage and the commercialisation of customer data. Assessment for this module is 100% continuous assessment (CA), to be completed throughout the module.
Big Data Storage and Processing
This module deals with the core enabler for Data Analytics, i.e., data. Companies generate large amounts of data that need to be gathered and stored for eventual analysis to turn them into value. This module will equip students with the analytical and technical skills to manage large and diverse amounts of data to allow their analysis at the right scale and within the desired time frame. Assessment for this module is 100% continuous assessment (CA) and will comprise of three assignments in total, to be completed throughout and at the end of the module.
Advanced Data Analysis
This module deals with a cornerstone of modern Data Analytics by building upon the statistical modelling knowledge already gained in adjoining modules. The ability of students to develop a learning system for use as a Data Analysis solution to real world problems ties directly to and builds on the Machine learning module as well as the Statistics for Data Analysis module. As an emerging technology, A.I. is increasingly vital in both academic and commercial decision-making processes and is one of the vital skills a modern Data Analyst requires to deal with the increasing use of temporal data in order to remain market relevant. Assessment for this module is 100% continuous assessment (CA) and will comprise of three assignments in total, to be completed throughout and at the end of the module.
Stage 2 (Project)
Data Analytics Project
This module deals with the application of knowledge gained in the taught modules of the course in a structured environment, while allowing the learners the freedom to engage with a specialist area of particular interest. The module also deals with Project management tools (e.g. CRISP DM) and theory as well as the practical implementation of these tools to formulate, plan and deliver on a chosen area of research and application.
As this is a blended learning programme students will be required to engage in a combination of on campus and online activities. All students will be introduced to the CCT online learning environment as part of the induction to the programme and will have access to further support as required.
Online activities can include live or pre-recorded lectures, independent learning and assessment activities such as research tasks, discussion forums, simulations, quizzes and e-portfolio work along with online group activities such as live classes, group project work, virtual labs and tutorials. Completing the online elements of the programme each week is essential to successfully complete the programme. On campus activities can include small group tutorials, labs, project supervision, problem solving case studies, library research and seminars.
Assessment for all taught modules is 100% continuous assessment and will comprise of three assignments for each module to be completed throughout and at the end of each semester. Industry initiated real-world problems are used as the context for planning and designing assessment solutions, as well as being an aid for problem solving sessions. Summative assessment is a blend of integrated assessment and module specific assessment utilising both group and individual work, while formative assessment is pipelined into module delivery and feedback, so as not to add to the assessment burden of students.
The project stage culminates in a peer presentation and solution demonstration. There will be an opportunity for students to present a poster presentation of their work to industry representatives to informally evaluate and discuss solutions with learners, further enhancing the professionalism of the learner and engaging industry in the programme. This module incorporates learning from all modules in the taught components and aims to ready learners for industry and/or academic Data Analytics / Science work.
CCT College Dublin has identified entry criteria and processes that will enable it to determine an applicant’s potential to succeed on the proposed programme.
The direct entry route to this programme requires applicants to evidence numerate, technical and analytical ability to a minimum of NFQ level 8 standard.
The following are accepted as appropriate evidence for direct entry:
a. An NFQ Level 8 major award, or higher, in the discipline areas of ICT/Computing, Business, Science or Engineering or cognate discipline
b. An NFQ Level 8 major award, along with relevant experience in the area of Data Analytics and/or professional certification, may also be considered
In both scenarios presented above, applicants will also be required to evidence ability in the application of mathematical concepts such as algebra, or spreadsheet analysis and formulas, database knowledge, for example, to a level 8 standard. This is essential to demonstrate applicants numerate, technical and analytical ability required to ensure capacity for the extent of mathematical and technical content related to the programme.
This programme is designed for individuals who have previous knowledge in computing, analytics or similar through professional experience and/or educational qualifications. This programme is not suitable for individuals with only basic computer literacy.
Prior programming experience is not essential for admission. All learners will be required to
complete the CCT Programming Induction Bootcamp. A learner who can present evidence of currency in programming using Python can apply to the Programme Leader for exemption from this element of the induction programme. Such applications should normally be made not less than 2 weeks prior to programme start.
English Language Entry Requirement: Applicants whose first language isn’t English must demonstrate a minimum competency in the English Language of CEFR B2+.
To fully engage in this programme applicants will be required to have access to the internet, a laptop or desktop PC with webcam, microphone and speakers or headset. The minimum recommended specification is Windows Operating System with a basic RAM Memory of 8GB DDR4 RAM with a basic processor Intel i5 (7th Gen and above) with a dedicated graphics card (or equivalent graphics option).
Applications are also welcome from individuals who do not meet the standard entry requirements but wish to apply for entry based on prior learning (RPL) or prior experiential learning (RPEL). The College will thoroughly assess applications received through RPL and RPEL to ensure that candidates are able to evidence learning to an appropriate standard – normally the framework level equivalent to the direct entry qualifications requirement and demonstrate potential to succeed and benefit from the programme. Applications submitted on this basis will be assessed in line with the College RPL policy.
The programme has been designed to produce graduates with the attributes required of data specialists and analysts today and the ability to continue to develop knowledge, skill and competence to remain competitive and employable in an ever-advancing discipline. On successful completion of the MSc in Data Analytics learners may progress to further study or research opportunities.
Graduates of the MSc in Data Analytics should be able to secure professional roles at intermediate and advanced positions in data analysis across all sectors of the economy and progress to leadership or research roles using skills related to those learned in the programme curriculum. Potential roles include but are not limited to: Business Intelligence Analyst, Data Analyst, Data Scientist, Data Engineer, Quantitative Analyst, Data Analytics Consultant, Operations Analyst, Marketing Analyst, Data Project Manager, IT Systems Analyst, Transportation Logistics Analyst, Financial Data Analyst, Healthcare Data Analyst.
According to Grad Ireland, who issue regular surveys to Industry on employability trends, the graduate recruitment trends for Ireland and Northern Ireland specifically list data analytics as an area where recruiters saw one of the biggest skills shortfalls at 46%.
This programme aims to produce graduates who are technically skilled, problem solvers, professional, good communicators and effective team players as well as leaders. The graduate will also be well-placed to pursue further academic or professional study.
Application for the Springboard+ funded programme should be made via the Springboard courses website here.
Application for full-time (day) study can be made by emailing firstname.lastname@example.org
Those who are in employment/working :
For eligible applicants who are currently in employment/working 90% of the tuition fees will be covered by the HEA through Springboard+ and the remaining 10% is payable by the student or their employer.
The full EU fee for this course is €8,400 so for successful applicants who are in employment €840 will be payable once you have been offered a place and before you enrol on your Springboard+ course.
Those who are unemployed, formerly self-employed or ‘Returners’:
This course is free for eligible applicants who are unemployed, formerly self-employed or who are classified by Springboard+ as ‘Returners’ or ‘Homemakers’.
Please see this link on the Springboard Courses website for more detail on funding eligibility and also this link on the Springboard Courses website detailing documents that are acceptable as evidence of eligibility.
We are hosting a number of events in the lead up to the next academic year to give prospective students the opportunity to find out more about their course and the College. At the moment these events are virtual and you can pre-register here.
You can also book a one to one appointment with an Admissions Advisor in person or online via Zoom by email.
All QQI accredited programmes of education and training of 3 months or longer duration are covered by arrangements under section 65 (4) of the Qualifications and Quality Assurance (Education and Training) Act 2012 whereby, in the event of the provider ceasing to provide the programme for any reason, enrolled learners may transfer to a similar programme at another provider, or, in the event that this is not practicable, the fees most recently paid will be refunded.