Master of Science (MSc) in Data Analytics (September 2021)

CCT College Dublin (CCT_0921_SB_MSCDA)
Key Programme Details
Award

Master of Science in Data Analytics (Major Award)

NFQ Level

Level 9 About NFQ

Delivery Method

Blended

Mode

Part Time

ECTS Credits

90

Department

ICT Faculty

General Information
Contact

Admissions Office

Email

info@cct.ie

Phone

01 6333444

Address

Admissions Office
CCT College Dublin
30-34 Westmoreland Street
Dublin 2

Role

Admissions

Important Dates
Application Deadline

27/09/2021

Start Date

27/09/2021

End Date

31/08/2022

About this Course

The MSc in Data Analytics is a newly designed postgraduate masters degree aimed at IT graduates and professionals, and graduates from cognate/numeric disciplines. 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 sector. It was informed by consultation with members of the CCT College Dublin Industry Engagement Forum (IEF) and CCT's Strategic Partner Microsoft Ireland.

Modules include Statistics for Data Analysis, Data Preparation and Visualisation, Machine Learning, Research and Ethics Studies, Big Data Processing and Storage, Advanced Data Analysis, as well as the integration of multiple transversal skills throughout. Programming will be taught throughout many of the modules particularly across the first terms.

Taught modules 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.

Participants on this part-time programme will comprise IT professionals and graduates of cognate disciplines who are currently in employment and who require upskilling due to the accelerated pace of economic digital transformation. This along with a big data driven economy, and the resulting new ways in which business has become technology driven, across all sectors has heightened the need for programmes of this nature. The programme will be intensive with flexible modes of delivery and innovative and integrated assessment.

This one year, part time, MSc programme will be delivered on an evening/weekend predominantly blended/online basis. The programme leads to an award by QQI at Level 9 of the NFQ.

Register for our Information Webinar on these Funded Courses - in Partnership with Industry Partner Microsoft Ireland -
https://mktoevents.com/Microsoft+Event/288185/157-GQE-382

Objectives

This proposed 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.

Entry Requirements

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

or

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.

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).

Applicants whose first language is not English must demonstrate a minimum competency in the English Language of CEFR B2+.

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 policies and procedures set out in the RPEL criteria attached to this submission.

Long Description

The MSc in Data Analytics is a newly designed postgraduate masters degree aimed at IT graduates and professionals, and graduates from cognate/numeric disciplines. 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 sector.

It was informed by consultation with members of the CCT College Dublin Industry Engagement Forum (IEF) and CCT's Strategic Partner Microsoft Ireland.

Modules include Programming for Data Analytics, Statistics for Data Analysis, Data Preparation and Visualisation, Machine Learning, Research and Ethics Studies, Big Data Processing and Storage, Advanced Data Analysis, as well as the integration of multiple transversal skills throughout. Programming will be taught throughout many of the modules particularly across the first terms.

Taught modules 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.

Participants on this part-time programme will comprise IT professionals and graduates of cognate disciplines who are currently in employment and who require upskilling due to the accelerated pace of economic digital transformation. This along with a big data driven economy, and the resulting new ways in which business has become technology driven, across all sectors has heightened the need for programmes of this nature. The programme will be intensive with flexible modes of delivery and innovative and integrated assessment.

This one year, part time, MSc programme will be delivered on an evening/weekend predominantly blended/online basis. The programme leads to an award by QQI at Level 9 of the NFQ.

In the part-time programme there are 3 terms of taught modules during stage 1, and the fourth is a capstone project.

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. Learners typically attend online lectures 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.

In the part-time programme there are 3 terms of taught modules, and the fourth term 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.

Why Choose This Course

The MSc in Data Analytics is a newly designed postgraduate masters degree 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. It was informed by consultation with members of the CCT College Dublin Industry Engagement Forum (IEF) and CCT's Strategic Partner Microsoft Ireland.

Modules include Statistics for Data Analysis, Data Preparation and Visualisation, Machine Learning, Research and Ethics Studies, Big Data Processing and Storage, Advanced Data Analysis, as well as the integration of multiple transversal skills throughout. Programming will be taught throughout many of the modules particularly across Semester 1.

Career Opportunities

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.

Timetable Info

Classes are typically scheduled two evenings per week + 3/4 Saturdays per semester. Timetable will be published during the Summer.

Delivery Location

As this is a blended learning programme students will be required to engage in a combination of on campus (subject to public health advice) 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.

On campus classes (where applicable) will take place at CCT College Dublin.
CCT College Dublin
30-34 Westmoreland Street
Dublin 2

Delivery Notes

As this is a blended learning programme students will be required to engage in a combination of on campus (subject to public health advice) 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.

Admissions Contact Details
Contact Person

Admissions Office

Address

Admissions Office
CCT College Dublin
30-34 Westmoreland Street
Dublin 2

Phone

01 6333444

Email

info@cct.ie

RPL Information

Applicants are encouraged 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. Full information regarding the College RPL policy is available on the College website http://www.cct.ie/wp-content/uploads/CCTP602-RPL-V2.1.pdf

Application Procedures

On completion of the online application via springboardcourses.ie all applicants will receive an application acknowledgement directly from the College within 48 hours and will be asked to send by return email the following information, as applicable, to complete their application for the MSc in Data Analytics:

- Updated CV
- ID Verification and Residency Verification (photo ID required e.g. EU Passport or non EU passport + evidence of Stamp 4 for 3 out of last 5 years)
- Proof of employment (redacted payslip/letter from employer) or, if unemployed, evidence of receipt of social protection payment (redacted bank statement or payment slips)
- Attested original copies of degree qualification parchment
- Attested original copies of final degree transcript of results
- RPEL documentation as required by CCT (if applicable, as outlined in entry requirements above)
- Evidence of English Language proficiency scores if the applicant’s first language is not English (IELTS, TOEFL etc.)

Media
Master of Science (MSc) in Data Analytics

View this course on the CCT College Dublin website and get more information on college facilities and student supports.

https://www.cct.ie/course/masters-data-analytics-postgraduate-msc/


Information Webinar - Monday August 23rd

Register at the link to attend an Information Webinar for this and all Springboard+ and HCI funded courses at CCT College Dublin

https://mktoevents.com/Microsoft+Event/288185/157-GQE-382