Higher Diploma in Science - Computing (Data Analytics)[2 year part-time]

Galway-Mayo Institute of Technology (TBC)
Key Programme Details

Higher Diploma in Science - Computing (Data Analytics)[1 year Full-time]

NFQ Level

8 About NFQ

Delivery Method



Full Time

ECTS Credits


Skills Area

Information and communication (including ICT)

General Information

Peter Butler




091 742328


Lifelong Learning Centre
Galway Mayo Institute of Technology
Dublin Road


Lifelong Learning Coordinator

Important Dates
Application Deadline


Start Date


End Date


About this Course

The Higher Diploma in Science in Computing (Data Analytics) is a conversion course for graduates of level 8 programmes in disciplines other than computing. The aim of the course is to provide students with a broad knowledge of computing, with a specialisation in data analytics. This will enable students to apply data analysis techniques to the topics in their
original degree, while also providing a foundation on which they can develop their skills in the more traditional areas of computing. The course covers such skills as automating manual spreadsheet-oriented data analysis processes, converting large data sets into actionable information, and creating web-based dashboards for visualising data. Level 8 graduates from disciplines such as business and finance are particularly suited to this course, as are those from life and physical sciences.


Various national research documents / skills reports over the past number of years clearly identify an (employment side) demand for individuals with Data Analytics /Applied Computing skills (e.g. Period Reports from the Expert Group on Future Skills Needs). A subsequent part of this application provides a sample of support letters and endorsements we have received, each of which clearly anticipates that continued demand. GMIT's recent experience (since 2012) in providing ICT skills conversion programmes is that a significant demand exists where programmes can be delivered in such a fashion as to provide time and space mobility for prospective candidates.

The following are some brief summary statistics in respect of student engagement and progress on the Higher Diploma in Data Analytics @ GMIT (commenced January 22nd, 2018):
• Recorded lectures are stored as video files and provisioned using the Microsoft Stream cloud service.
• MoodleTM is the learning management system (LMS) underpinning the online delivery of this programme.

Here are comments from students who commenced the course in January 2018:
- "The program delivery is excellent by both lecturers and is of a very high standard of presentation for both the subject matter and also the verbal conveyance of the lecture. I have every confidence that the lecturers are knowledge experts in their particular fields after sharing their biographies in week one. The GMIT Moodle pages are very easy to navigate and make submission of exercises\projects a much less arduous task than my last course in NUIG using the Blackboard system. Access to both lecturers is very accessible given the online nature of the course. In a short space of time, there is a sense of community within the group which is very positive and helpful given that the course is online with students all over the country."
— "Between family commitments and working full time with a NGO where funding for CPD is very limited, I simply would not have been in a position to upskill and begin pursuing a career in data science without the GMIT Springboard+ course. The online platform allows me to fit the coursework in when it suits me best and, being based in Waterford, is a lifeline to furthering my career in this sector as other similar courses necessitate evenings and/or weekends in either Cork or Dublin. The lack of direct contact with lecturers is not an issue as they're very responsive to emails, arrange regular Skype sessions and facilitate a discussion forum for students where any issues can be addressed. Overall, between the staff and fellow students, I feel I have the support I need to successfully complete the course."
— "Always wanted to do a postgrad in Computing. This course along with the Data Analytics emphasis will enable me to partake in projects in work which will enhance my career, or alternatively move my career in a new direction. Data Science and Data analytics have applications in every industry and is a hugely interesting subject area. The Higher Dip here in GMIT is delivered at an achievable pace and is made practical and interesting. Can't recommend it enough."
— "I came to this course with only two things, a below basic level of knowledge of data analytics and a desire to learn. The course is taught entirely online and although much of the content is challenging me the: leadership of the tutors, delivery of the course materials and student community have given me the confidence to keep moving, helping me to understand the material. Data Analytics is of growing importance to my industry and after only a couple of months I see how I can apply this knowledge to both my current and future roles."
— "I'm really enjoying the H. Dip in Data Analytics with GMIT. It's something completely new to me so I've started as an absolute beginner, both to Data Analytics and online learning. The course is really well laid out and the lecturers are extremely knowledgeable and responsive. The online learning experience to date has been so straight forward. Having the flexibility to manage my own learning has worked really well while working full time. I would definitely recommend the GMIT programme to anyone considering this course."
— "Excellent course. Everything is available online and the lecturers regularly update the online dashboard with the latest course slides and videos. The discussion forum adds an interactive element to the course and comes in handy when you are trying to solve difficult problems.
— I am currently studying for the Higher Diploma in Computer Science with a specialization in Data Analytics. So far, I have found the course and all of its content very interesting and relevant. The distance learning teaching method is excellent as it provides the flexibility to enable me to study around work commitments. I find the recorded video sessions are much better for content delivery than the classroom method as it allows me to play a session as many times as I need to embed the information. I thoroughly recommend this course and I'm excited and inspired by the opportunity to apply this new knowledge to my work projects."
— "I attended college back in 2004 when I was young and did not know what I wanted to do in life. I completed a Degree in an area which I had no interest in and always regretted making the wrong choice. A friend of mine is completing a Software Development course via Springboard and recommended that I look into it. We are 4 months on from that and here I am completing my 1st semester of the H. Dip in Data Analytics. I found the application process and acceptance process extremely user friendly and constant contact with updates from the admin of the course. I am loving the challenge and once completed, I will have a qualification in an area that I have a huge passion for. Without the funding for me to complete this course through Springboard, I would not have had this opportunity."
— "I was very anxious to return to college again and did not know what to expect of Data Analytics. I am surprised at how much I enjoy being back in college and cannot wait to clear my day’s workload and get into the college work. I thoroughly enjoy learning again and enjoy the online medium and being able to work through the problems at my own pace as time allows."

Entry Requirements

A Level 8 qualification under the National Framework of Qualifications or equivalent (see www.qqi.ie) in any discipline. Applicants will be ranked in terms of academic merit each week and offers of places made to those with minimum academic requirements in order of academic merit. If all available places are not filled by applicants holding a Level 8 qualification will be evaluated.

Long Description

[Data Representation and Querying - 5 credits at Level 8]
In this module students will investigate and operate the protocols, standards and architectures used in representing and querying the data that exists across the internet. Students will also gain practical experience in developing applications that interact with
such data. On completion of this module the learner will be able to: explain the basic mechanisms by which data is represented and transmitted; compare the different data models and architectures used in modern web (and offline) applications; design and utilise
application programming interfaces in the context of the web and other hosting platforms; write data-centric software applications that adhere to defacto standards and protocols.

[Programming and Scripting - 10 credits at Level 8]
An in-depth introduction to computer programming and scripting. In this module, an emphasis is placed on automating manual computer activities. While students receive a firm grounding in basic data structures, conditionals and iteration, they also receive training in high-level computer automation concepts such as shell scripting and interacting with the operating system.

[Fundamentals of Data Analysis - 5 credits at Level 8]
In this module, students learn about the basics of data analysis and its underlying mathematical concepts. Topics include data exploration and visualisation, data cleansing, basic regression and classification, and big data concepts. The emphasis is on the
practical implementation of established techniques.

[Computer Architecture and Technology Convergence - 5 credits at Level 8]
This module covers the basic principle of traditional computer design and highlights current trends in mobile and pervasive computing architectures. On completion of this module the learner will be able to: explain the role of the information processing paradigm in ICT; demonstrate an understanding of the layers of a computer systems and the necessity for functional abstraction; distinguish between computing as a tool and computing as a discipline; describe the computer problem-solving process; Demonstrate an understanding of the function and operation of the components of a von Nuemann machine and its modern
equivalent; appreciate the increasingly convergent nature of systems, data, media and functionality.

[Computational Thinking with Algorithms - 5 credits at Level 8]
This module provides detail of algorithm design and the computational problem solving process using programming libraries and application programming interfaces (APIs). On completion of this module the learner will be able to: apply a structured methodology
in their approaches to problem solving with systems and software; design and apply algorithms to computational problems efficiently and correctly; critically evaluate and assess the performance of algorithms; apply advanced knowledge and experience of the use of core Java class libraries in real-world problem solving in a variety of data analytics-centric contexts.

[Programming for Data Analysis - 10 credits at Level 8]
In this module, students develop their programming skills towards the effective use of data analysis libraries and software. Students learn how to select efficient data structures for numerical programming, and to use these data structures to transform data into
useful and actionable information.

[Object Oriented Software Development - 5 credits at Level 8]
This module provides an introduction to programming (using an Object-Oriented approach) and assumes little or no previous experience in programming. On completion of this module the learner will be able to: demonstrate an understanding of the core concepts of object-oriented programming; implement a software application using an object-oriented programming language utilising core object-oriented programming concepts, and develop problem solving skills as part of this process; design an object-oriented software application; test and debug an object-oriented software application; demonstrate an understanding of the
universality of the Object-Oriented paradigm and its applicability to programming for data analytics-centric contexts.

[Machine Learning and Statistics - 5 credits at Level 8]
A practical look at the most popular algorithms used in machine learning and the analysis of stochastic processes. Students cover topics such as incorporating neural networks, support vector machines and large-scale machine learning in their own data analytics workflows.

[Web Applications Development - 5 credits at Level 8]
This module is focused on the development of practical skills in the area of web applications. On completion of this module the learner will be able to: design, prototype, and evaluate a user interface based on good UI design principles; describe the architecture of the World Wide Web and its applications; design, develop and deploy data centric web applications using HTML 5.0, CSS, and other “open” web technologies.

[Advanced Databases - 5 credits at Level 8]
This module presents the theory and practice relating to advanced database applications in areas such as Enterprise Data Management, and in the management and storage of non-relational data. It builds on the concepts as well as on the skills and knowledge acquired in the (earlier) Data Representation & Querying module. On completion of this module the learner will/should be able to: distinguish between operational databases, and data warehouses; demonstrate an understanding of a data warehouse design method and its application; discuss how Data Mining and other advanced data analysis tools are used to give corporate decision makers access to all of an organisation's data, both historical and current; recognise the benefits and challenges associated with distributed DBMSs and have awareness of the protocols associated with distributed transaction management, concurrency control; demonstrate an appreciation of the various approaches by which web and database technologies are currently being integrated, and the appropriateness of the web as a database application platform.

[Work Placement/ Project - 15 credits at Level 8]
The work-placement / internship (for unemployed people) / project (for employed people) component is an integral part of the academic programme of this Higher Diploma in Computing. The aims of the component are to offer the student the opportunity to apply the knowledge and skills gained throughout the course in a relevant work-place setting; facilitate the student in developing the practical competencies and communication skills necessary to function as an effective team member in the work environment. On completion of this module the learner will be able to participate in a team in a professional IT environment as an effective and efficient team member; contribute as an individual contributor and as a full team member; demonstrate an understanding of the main business strategy of the employer and show an understanding of the role of the team and its work in the overall business strategy of the company; take on (minimally) entry-level development and / or analysis roles relating to data analytics / data science. Candidates already in employment will undertake a work-based project centered on data analytics / data science that serves to apply their new acquired skills and competencies to a realistic work-based scenario / problem / data-set. Such candidates will be assigned a dedicated academic supervisor for the duration of the project.

On completion of this programme the learner will be able to:
1. Design and construct a well-informed data analytics workflow to solve a data-intensive computational problem.
2. Recognise, understand and appreciate advanced techniques in computational data analytics.
3. Discuss, plan and implement fundamental techniques in computing, including programming.
4. Identify, analyse and plan strategies for solving general computational problems.
5. Describe the limitations of current techniques and technologies in computing and data analytics.
6. Apply quality concepts to computer programming and data analytics workflows.
7. Locate and evaluate documentation and information through online research.
8. Work effectively as an autonomous individual in solving problems using a computer.
9. Manage a computer-based project throughout all stages of its lifecycle.
10. Plan and track the development of software.
11. Apply best practice in the fields of computing and data analytics.
12. Explain how academic and industrial research leads to new computing solutions, knowledge, technologies and techniques.

Timetable Info

Fully online, all lectures are recorded

Delivery Notes


Admissions Contact Details

Peter Butler


Lifelong Learning Centre
Galway Mayo Institute of Technology
Dublin Road


091 742328



Application Procedures

1. Apply for the course on www.springboardcourses.ie
2. Email a copy of your highest qualification to date (showing 1.1/2.2/2.2 result if a Level 8 qualification on the parchment or on a transcript of results), a copy of your Passport/ National ID Card, a copy of a document showing your PPSN and copy of a document showing your name and address to peter.butler@gmit.ie.
3. Applications received each week will be evaluated and places offered until the course is full.