Master of Science (MSc) 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
  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.

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:
 

• An NFQ Level 8 major award, or higher, in the discipline areas of ICT/Computing, Business, Science or Engineering or cognate discipline
  
  or
   
• 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 fees for Master of Science (MSc) in Data Analytics may vary from €10,000 to €25,000 annually for tuition fees. The fee's prices may get higher for International Students.

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.