Higher Diploma in Science in Data Analytics for Business

Students will undertake learning in the subjects of programming, mathematical, logical and strategic thinking as well as machine learning, data gathering, analysis and visualisation and the subsequent business application of these skills. Industry-initiated real-world problems will be provided by our industry contacts and used as the context for planning and designing assessment solutions, as well as being an aid for problem-solving sessions.

In addition to the data analysis and associated technical skills, which will be fostered during the participants studies, transferable skills that will be developed throughout the programme via the varied teaching and assessment methods include: critical analysis, advanced evaluation, self-analysis and personal reflection, problem solving, communication skills, team management and group-work and professionalism.  The programme is underpinned by a Strategic Thinking Capstone module which spans all semesters and is assessed by a Problem Based Learning (PBL) project. The module explores current strategic thinking issues companies face today, such as data protection and privacy and the challenges and opportunities of emerging technology.

Strategic Thinking

This capstone module floats across all semesters of the full-time delivery and at least two of three semesters for the part-time delivery. Strategic Thinking concepts are introduced purposefully as the module and programme develops. As this module is the capstone and spans all semesters, the syllabus content usefully synchronises with the principal Problem Based Learning project and associated problem milestones, furthering the relevance of content to practice. The syllabus explores Problem Reduction Identification and Solution Mapping (PRISM), this then builds to project planning and team development specifically within this field.

Statistical Techniques for Data Analysis

This module forms the basis for Numerical methods, particularly those pertaining to statistics and probability which are central to the domain of data analytics. This module will equip the learner with statistical skills that are immediately applicable to basic data analytics tasks as well as serving as a foundation for more sophisticated techniques introduced in later modules.

Data Preparation

This module provides the learner with exposure to extensive exploratory data analysis and proper data management and preparation, which 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.

An understanding purpose of feature selection and dimensionality reduction in the context of the curse of dimensionality 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.

Machine Learning

This module provides the learner with Machine learning techniques that are an essential component of data analytics. This module builds on and draws from the Statistical techniques for Data Analysis and Data Preparation module to equip the learner with the ability to identify the fundamental nature of data analytical problem and practical experience of the use of commonplace classification and regression approaches.

Data Visualisation Techniques

This module is a key tool in the data analyst’s toolbox, allowing the efficient and effective communication of vast quantities of data, offering rapid insights that would otherwise be difficult or impossible with numerical presentation. This module will provide the learner with the skills needed to present a variety of different types and volumes in data in a manner that provides the maximum insight and understanding to the viewer. Allowing the learner to display directly the results of learning achieved in previous modules.

Machine Learning for Business

This module building on the knowledge acquired in Machine Learning Principles for Big Data, focusing on the available machine learning algorithms widely integrated into commercial machine learning modelling. This module is designed to equip the learner with the skills necessary to tackle a wide range of unsupervised learning problems, such as cluster analysis and text analytics. Both of these techniques are widely used in the analysis of business data as they allow the enterprise to develop a deeper understanding of their customers. The module will also provide the learner with the necessary understanding to be able to perform modelling of temporal data, a type of data that is commonplace in the business domain.

Evidence of ability in the application of mathematical concepts such as statistics, algebra, or spreadsheet analysis and formulas, for example, to a level 7 standard is required to evidence the numerate, technical and analytical ability required to ensure capacity for the extent of mathematical and technical content on the programme. This pre-requisite knowledge, skill and competence can be evidenced through a level 7 degree, or through a combination of qualifications with experience. Specifically:

a. Applicants will ideally possess a minimum of an ordinary degree in ICT, or a cognate discipline. For the purpose of the application process, cognate disciplines deemed to satisfy the requirement for numerate, technical and analytical content include those in the areas of:

• Mathematics
• Life sciences
• Engineering
• Technology
• Information science
• Economics
• Architecture
• Accounting

Applicants with non-cognate degrees will also be considered but must be able to demonstrate mathematical, technical and analytical ability up to a level 7 standard through qualifications or appropriate experiential learning.

or

b. Applications on the basis of experiential learning or informal / non-formal learning must evidence an applicant’s potential to succeed through demonstration of ability to pursue the programme at the applicable NFQ level and benefit from the programme of study in question. Specifically, RPL applications must evidence numerate, technical and analytical ability to a level 7 standard. In addition to numerate, technical and analytical capacity, all applicants will need to evidence learning to a level 7 standard including the ability to produce written summaries, discussions and projects on academic and applied matters.

RPL portfolio evidence may be provided through:

• Prior study and qualifications, including CPD, short courses and professional awards as well as NFQ awards
• Work experience and achievements
• Other experiential learning obtained through volunteering or non-employment experience
• Successful completion of an entry assessment set by the College
• A combination of the above

This programme is designed for graduates of level 7 degrees of a more numerate, technical and analytical nature or those individuals who can evidence equivalent through professional experience and/or educational qualifications. This programme is not suitable for individuals with only basic numeracy and or 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 at the time of writing is windows OS with a basic RAM Memory of 8GB DDR4 RAM and a basic processor Intel i3(7th Gen and above) and a dedicated graphics card.

While there is no compulsory access interview some applicants may be required to attend an interview. CCT reserves the right to request an applicant to attend a semi-structured interview in order to more fully establish the applicant’s suitability for the programme, their motivation and potential to succeed.

There is no specified minimum experiential requirement for standard applicants. RPL applications are considered on a case-by-case basis under the CCT RPL policy.

Applicants whose first language is not English, must present English Language proficiency level evidence. English language competency required for entry must be equal to or greater than B2+ in the CERFL. English language credentials endorsed by other systems (viz. IELTS, TOEFL, Cambridge etc.) will be assessed to ensure they meet this minimum standard.

All applications for admission onto this programme should include:

• Updated CV
• ID Verification (passport picture page copy)
• Attested original copies of degree qualification parchment
• Attested original copies of final degree transcript of results
• RPEL documentation as required by CCT
• Evidence of English Language proficiency scores if the applicant’s first language is not English (IELTS, TOEFL etc.)

Home Full-time –  €6,200

Full-time Non EU Offshore – €9,250