Graduate Certificate in Data Analytics
Classroom - Evening
School of Computing
Information and communication (including ICT)
Renaat VerbruggenEmail Phone
School of Computing
Dublin City University
The objectives of the Graduate Certificate in Data (Business) Analytics course are to provide students with specialised knowledge in the fields of Business and Data Analytics. Data analytics is the practice of iterative, methodical exploration of an organisation's data, with an emphasis on statistical analysis. Data analytics is used by companies committed to data-driven decision-making. The course will aid the understanding of data analysis techniques, technologies in a modern enterprise setting. The delivery mode is by attending lectures on the DCU campus two (and some weeks three) evenings per week.
The objectives of the Post Graduate Certificate in Data Analytics course are to provide students with specialised knowledge in the field of Business and Data Analytics. Data analytics is the practice of iterative, methodical exploration of an organisation's data, with an emphasis on statistical analysis and is used by companies committed to data-driven decision-making. The course will aid the understanding of data analysis techniques, technologies in a modern business setting.
Candidates must hold, at a minimum, a Second Class Honours degree in Computer Science, Computing, Computer Applications or a related discipline at Level 8 on the NFQ.
Candidates with an NFQ Level 7 qualification, with a minimum of three years relevant experience in a relevant discipline will also be considered.
Candidates with significant experience in the software development sectors, in addition to an Honours primary degree in some other discipline, may also be considered for entry.
All three routes of entry are subject to an interview if deemed necessary by the Chair of Programme or Head of School.
Students who meet the minimum entry requirements are not guaranteed a place on the programme and DCU reserves the right to interview candidates to confirm their suitability for the programme. Non-Native English speakers must submit evidence of competency in the English language as per DCU entry requirements, which can be found at https://www.dcu.ie/registry/english.shtml
Technologies such as the internet, sensor nets, social media and cloud computing are generating quintillion bytes of data per day, from which gems of knowledge can be extracted to improve processes and generate value. This programme, delivered in conjunction with leading industry players, builds on the School’s long-term expertise in Data Analytics (ModSci Group), as well as its recent participation in the SFI-funded centre (INSIGHT), and aims to provide students with a deep understanding of the issues, techniques and tools, required to examine large amounts of raw data and extract meaningful information. Students are introduced to the challenges of dealing with large heterogeneous data sources and with the scientific methods to extract actionable knowledge from these.
The modules that students will study on the Graduate Certificate in Data Analytics are:
CA669 Web Design and Implementation (7.5 credits, part time on campus delivery; 50% CA; 50% Exam)
This module will introduce students to the architecture and workings of the world wide web, and to the principles of designing a usable website. It will address the technical structure of the WWW and practical aspects of web authoring as well as the design of web sites. It will also introduce the tools and techniques for constructing, optimising and maintaining websites. It will look at enhancing web pages by giving an overview of advanced web technologies such as XML, CSS, SEO, frameworks and Web 2.0. It will address design issues including Human-Computer Interaction, design principles, usability guidelines, coping with user diversity and covers methodologies to ensure a designed website is effective, easy to use and attractive. Website evaluation techniques are also introduced.
CA660 Statistical Data Analysis (7.5 credits, part time on campus delivery; 25% CA, 75% exam)
This module is a foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. Reasonable proficiency in algebra and the ability to grasp concepts of probability and its importance are predominantly required. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems, rather than mathematical sophistication.
CA683 Data Analytics and Data Mining (7.5 credits, part time on campus delivery; 25% CA; 75% Exam)
This module aims to review and complement foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems, rather than mathematical sophistication
CA652 Information Access (7.5 credits, part time on campus delivery; 30% CA, 70% exam)
The aim of this module is to familiarise the student with aspects of information management, which impact the e-commerce area. This includes conventional databases, access to text documents and to multimedia information as well as the emerging important topics of semantic web, blogging and microblogging, and social networks.
OI501 Technology and ICT Career Success (7.5 credits, part time online delivery; 100% CA)
In this careers focused module the learner will build on their skills and knowledge in line with the development of their study of ICT. They will be given the opportunity to develop the tools to allow them to market themselves and their skills to assist them in developing their employability. Topics covered in the module include development of personal development plan, development of a professional profile and portfolio to share with prospective employers, which can be online, understanding the ICT labour market sector and targeted CV development.
Given the ever-increasing volume of data that enterprises have access to today, there is a demand for employees who have computational and analytical skills and who can inform business decisions to increase efficiency.The programme as designed has been developed with just that in mind and our aim is to teach students how to apply descriptive, predictive, and prescriptive analytics of big data concepts and techniques to generate valuable insight that can assist with decision making.
In addition, it has been reported in the EGFSN, 2017 report that there could be an estimated 29,880 job openings in the area of Big Data and Analytics Skills up to 2020. Given the skills shortages forecast globally, there is a need for organisations, such as DCU, to upskill some of the people already working in this area as well as providing new opportunities for others who wish to develop a career in this growing sector of Data Analytics, which is why this special purpose award in Data Analytics directly addresses this need.
Graduates who successfully complete this programme will have improved proficiency across a range of key disciplines in the field of data analytics as well as updating their skills beyond the narrow remit of training courses. It also supports recent graduates of computing and cognate disciplines to gain specialised knowledge and skills for higher level industry entry at an early stage in their careers.
In addition to the technical modules which enable graduates to upskill, DCU provides an integrated job readiness component (OI501, Technology and ICT Career Success at 7.5 credits), which will allow participants develop the necessary employability skills alongside their academic studies. The course will help participants to identify clear, realistic and practical steps to enhance their careers and to upskill to meet the identified skills shortage in the area of Data Analytics. Finally, while this is a bespoke award, it does provide a foundation for an M.Sc. qualification, and self-funded routes to completing an M.Sc. are available. Please contact the programme chair, Renaat Verbruggen for further details.
The course runs over two semesters, Sep to Jan and Feb to May. Lectures are campus based two evenings (4pm to 7pm) per week, with three afternoons being required on occasion. A full timetable of lectures will be provided at the start of semester. In the event that participants do not complete all requirements of the programme at the end of semester 2, it is possible to resit exams in August.
Dublin City University
School of Computing
Dublin City University
Applications are submitted through the Springboard Application Website: www.springboardcourses.ie
All application should include
1) A scanned copy of all relevant academic transcripts
2) Telephone contact details for two referees
3) Personal Statement detailing your prior knowledge, if any, of Data Analytics as well as any industrial experience you might have.
4) A current telephone contact number
5) Applicants full current address details
Decisions of the applications committee are final.