Data Analytics for Business - Conversion Course
The Higher Diploma in Science in Data Analytics for Business postgraduate course aims to provide an opportunity for learners with a degree outside the computing arena as well as those currently involved within the IT sphere to refocus and reskill for careers that require Data Analytics knowledge and skills. This programme is specifically designed for individuals with evidenced numerate, technical and analytical ability who aspire to work, or are working, in roles that involve data analysis or the interpretation of data to inform business management and decision-making. They will have the opportunity to continue to develop knowledge, skill and competence to remain competitive and employable in an ever-advancing sector.
Data Analytics is among a set of emerging and rapidly developing technologies termed Innovation Accelerators, which have been identified as being critical to the next wave of digitalisation. According to Gartner's Hype Cycle 2019, over the next decade, data analytics and AI will augment workers' efficiency, as companies rely on leading tech to beat out competitors.
This Higher Diploma in Data Analytics for Business is a rigorous and highly skills focused conversion course, and as such applicants will need to be highly motivated and fully committed to the programme in order to be successful. The design and development of modules within this programme was informed by significant industry consultation, particularly from Microsoft and its partner network. The course deals with current business trends in the use of big data and the tools and technologies used in implementing data analytics across a wide selection of business types undergoing digital transformation. It also deals with the different types of statistical analysis and its underlying implementation.
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.
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.
Statistical Techniques for Data Analysis
Data Visualisation Techniques
Machine Learning for Business
Learners submitting an application to the proposed programme should provide supporting documentation for application consideration, in line with any one of the below Access arrangements or minimum entry requirements:
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:
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.
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:
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.)
Blended Learning - Evenings and Weekends
2 evenings per week 5.45-9.45pm plus 3-4 Saturday classes per term