Computing - Data Analytics - ICT Skills - Conversion Course - Galway

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.

Data Analytics / Data Science is a growing area of employment, with significant future growth also anticipated. This is well established in various national skills bulletins (e.g. Expert Group on Future Skills Needs).

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.

Entry requirements

Eligibility criteria:

Department of Education & Skills:

You must have a full NFQ Level 8 Honours Degree (or equivalent if earned outside the EU, see

RPL applications will be considered form those holding a Level 7 who have significant working experience which would match the learning outcomes of a Level 8.

The Springboard+ Programme is operated by the Higher Education Authority on behalf of the Department of Education and SKills and is co-funded by the Irish Government and the European Union under the Euroepan Structural and Investment Funds Programme 2014-2020.


2 years online

Students can complete the course over 18 or 24 Months.

The course is fully ONLINE, with optional programming workshops once per month on a Friday evening.

To apply go to

Part-time Mode: (January 2019 – May 2020): (18 months, can be extended to 24 months)

Delivery Mode:

Fully Online. It is anticipated that a number of optional on campus workshops at GMIT, Galway will take place each term; approximately three per term on Friday afternoons.

Careers or further progression

Data Analytics / Data Science is a growing area of employment, with significant future growth also anticipated.

This is well established in various national skills bulletins (e.g. Expert Group on Future Skills Needs).

Further enquiries

Peter Butler
Lifelong Learning Coordinator
Dublin Road
Tel: +353 (0)91 742328

Subjects taught

The modules undertaken are as follows

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 [5 credits at Level 8]
The work-placement / internship 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.

Enrolment and start dates

Part-time Mode: (January 2019 – May 2020):

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