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 MSc in Data Analytics are followed by a Data Analytics solution development group 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 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.
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
Statistics for Data Analysis
Data Preparation and Visualisation
Machine Learning for Data Analysis
Research and Professional Ethics
Big Data Storage and Processing
Advanced Data Analysis
Stage 2 (Project)
Data Analytics Project
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.
Application for Spring 2022 intake should be made via the Springboard+ website at the 'Application Weblink' below.
Provisional Schedule for Part-time Course
2 evenings per week 5.45-9.45pm plus 3-4 Saturday classes per term (Saturday schedule tbc)
Post Course Info
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.