Higher Diploma in Science in Data Analytics for Business (September 2021)

CCT College Dublin (CCT_0921_HCI_HDDAB)
Key Programme Details
Award

Higher Diploma in Science in Data Analytics for Business

NFQ Level

8 About NFQ

Delivery Method

Blended

Mode

Full Time

ECTS Credits

60

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 Higher Diploma in Science in Data Analytics for Business is a graduate conversion programme, industry informed by Microsoft Ireland and it's partner network of 1000+ companies, which allows learners to upskill/reskill for careers in the ICT sector and many other business sectors. It provides opportunities for Level 8 graduates from cognate or related disciplines and those with a relevant Level 7 award in an IT discipline to obtain a major award at Level 8 of the National Framework of Qualifications (NFQ).

This is a one year, full-time but flexible, programme with contact hours delivered on an evening/weekend and predominantly online/blended learning basis. The programme comprises six taught modules: Statistical Techniques for Data Analytics, Data Preparation, Machine Learning, Data Visualisation Techniques, Machine Learning for Business and a Strategic Thinking capstone module incorporating an industry informed Problem Based Learning (PBL) Project.

In addition to the core programme content, learners will develop a range of transversal development skills throughout the programme. These include critical analysis, problem solving, communication skills, advanced evaluation, team management and group work, professionalism, self-analysis and personal reflection.

A career in data analytics can be undertaken in a variety of sectors including private enterprise, public organisations, education, the arts, healthcare, government, and the public sector. Graduates of the programme, based on their learning, can also develop their careers in roles such as data analyst, business intelligence analyst, data engineer, operations analyst, marketing analyst, project manager, transportation logistics analyst, financial services analyst and IT systems analyst.

Successful learners will be awarded a Higher Diploma in Science in Data Analytics for Business with 60 ECTS at Level 8 of the NFQ, also enabling them with a further study/progression opportunity to a relevant level 9 programme like the MSc in Data Analytics at CCT College Dublin.

In addition to their academic studies, learners also have the option of obtaining Microsoft industry professional certification.

Entry Requirements

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:

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.

Long Description

The Higher Diploma in Science in Data Analytics for Business is a graduate conversion programme, industry informed by Microsoft Ireland and it's partner network of 1000+ companies, which allows learners to upskill/reskill for careers in the ICT sector and many other business sectors. It provides opportunities for Level 8 graduates from cognate or related disciplines and those with a relevant Level 7 award in an IT discipline to obtain a major award at Level 8 of the National Framework of Qualifications (NFQ). 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.

This is a one year, full-time but flexible, programme with contact hours delivered on an evening/weekend and predominantly online/blended learning basis. The programme comprises six taught modules: Statistical Techniques for Data Analytics, Data Preparation, Machine Learning, Data Visualisation Techniques, Machine Learning for Business and a Strategic Thinking capstone module incorporating an industry informed Problem Based Learning (PBL) Project.

In addition to the core programme content, learners will develop a range of transversal development skills throughout the programme. These include critical analysis, problem solving, communication skills, advanced evaluation, team management and group work, professionalism, self-analysis and personal reflection.


PROGRAMME CONTENT:

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. 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.


A career in data analytics can be undertaken in a variety of sectors including private enterprise, public organisations, education, the arts, healthcare, government, and the public sector. Graduates of the programme, based on their learning, can also develop their careers in roles such as data analyst, business intelligence analyst, data engineer, operations analyst, marketing analyst, project manager, transportation logistics analyst, financial services analyst and IT systems analyst.

Successful learners will be awarded a Higher Diploma in Science in Data Analytics for Business with 60 ECTS at Level 8 of the NFQ, also enabling them with a further study/progression opportunity to a relevant level 9 programme like the MSc in Data Analytics at CCT College Dublin.

In addition to their academic studies, learners also have the option of obtaining Microsoft industry professional certification.

Why Choose This Course

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, 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 Science in Data Analytics for Business is a graduate conversion programme which allows learners to upskill/reskill for careers in the ICT sector and many other business sectors. It provides opportunities for Level 8 graduates from cognate or related disciplines and those with a relevant Level 7 award in an IT discipline to obtain a major award at Level 8 of the National Framework of Qualifications (NFQ). 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.

This is a one year, full-time but flexible, programme with contact hours delivered on an evening/weekend and predominantly online/blended learning basis. The programme comprises six taught modules: Statistical Techniques for Data Analytics, Data Preparation, Machine Learning, Data Visualisation Techniques, Machine Learning for Business and a Strategic Thinking capstone module incorporating an industry informed Problem Based Learning (PBL) Project.

In addition to the core programme content, learners will develop a range of transversal development skills throughout the programme. These include critical analysis, problem solving, communication skills, advanced evaluation, team management and group work, professionalism, self-analysis and personal reflection.

Career Opportunities

A career in data analytics can be undertaken in a variety of sectors including private enterprise, public organisations, education, the arts, healthcare, government, and the public sector. Graduates of the programme, based on their learning, can also develop their careers in roles such as data analyst, business intelligence analyst, data engineer, operations analyst, marketing analyst, project manager, transportation logistics analyst, financial services analyst and IT systems analyst.

Successful learners will be awarded a Higher Diploma in Science in Data Analytics for Business with 60 ECTS at Level 8 of the NFQ, also enabling them with a further study/progression opportunity to a relevant level 9 programme like the MSc in Data Analytics at CCT College Dublin.

Timetable Info

Schedule is typically two evenings per week + Saturday morning. 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.

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 Higher Diploma in Science in Data Analytics for Business:

- 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
Higher Diploma in Data Analytics for Business

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

https://www.cct.ie/course/postgraduate-diploma-in-data-analytics-for-business/


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