Courses in

Science

Higher Diploma in Science in Computing (Data Analytics) - ICT SKILLS/SPRINGBOARD+

Discipline: Science

Programme Code: 

GA_KDATA_L08

Location: Online

NFQ Level: 8

Mode of Study: Online delivery

Application Route: Apply through www.springboardcourses.ie

Entry Requirements: Level 8 Honours Degree (or equivalent if earned outside the EU - see www.qqi.ie).

Credits: 75

Places: 50

Fee: 

675 Euros if employed, no fee if unemployed/ returner.  For full eligibility criteria go to www.springboardcourses.ie/eligility

Why Study?

  • You are a Level 8 Graduate from a non-computing background who wishes to enter into a career in computing.
  • The aim of the course is to provide you with a broad knowledge of computing, with a specialisation in data analytics.
  • This will enable you to apply data analysis techniques to the topics in your original degree.
  • It will also provide you with a foundation on which you can develop your skills in the more traditional areas of computing.
  • This course is fully online.

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.

 

NOTE: This course is now closed for applications.

If approved under Springboard+, it will open for applications on www.springboardcourses.ie in May/ June 2021.

The start date if approved would be January 2022.

Please monitor the www.springboardcourses.ie and www.hea.ie website for updates

Eligibility criteria for Springboard courses can be viewed here (PDF)

Programme Modules

The subjects undertaken are as follows:

  • Data Representation and Querying - module details can be downloaded here  (PDF)
    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.
  • Programming and Scripting - module details can be downloaded here  (PDF)
    An in-depth introduction to computer programming and scripting.
  • Fundamentals of Data Analysis - module details can be downloaded here  (PDF)
    In this module, students learn about the basics of data analysis and its underlying mathematical concepts.
  • Computer Architecture and Technology Convergence - module details can be downloaded here  (PDF)
    This module covers the basic principle of traditional computer design and highlights current trends in mobile and pervasive computing architectures.
  • Computational Thinking with Algorithms - module details can be downloaded here  (PDF)
    This module provides detail of algorithm design and the computational problem-solving process using programming libraries and application programming interfaces (APIs).
  • Programming for Data Analysis - module details can be downloaded here  (PDF)
    In this module, students develop their programming skills towards the effective use of data analysis libraries and software.
  • Object-Oriented Software Development - module details can be downloaded here  (PDF)
    This module provides an introduction to programming (using an Object-Oriented approach) and assumes little or no previous experience in programming.
  • Machine Learning and Statistics - module details can be downloaded here  (PDF)
    A practical look at the most popular algorithms used in machine learning and the analysis of stochastic processes.
  • Web Applications Development  - module details can be downloaded here  (PDF)
    This module is focused on the development of practical skills in the area of web applications.          
  • Advanced Databases  - module details can be downloaded here  (PDF)
    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.
  • Work Placement/ Project  - module details can be downloaded here  (PDF)
    Work placement is undertaken only by Unemployed applicants, otherwise a Project is undertaken. Such candidates will be assigned a dedicated academic supervisor for the duration of the project.

Learning Outcomes: 

On successful completion of this programme the learner will/should be able to:

1. The learner will have knowledge and understanding of advanced concepts in the following areas:

Data analysis: collecting, cleaning, processing, exploring and modelling.

Programming: iteration, conditions, abstraction, procedures. Mathematical foundations: numerical software, regression, hypothesis testing.

Professional issues: summarisation of results, presentation, decision-making.

2. Identify real-world problems that are well suited to data analysis.

3. Recognise, understand and appreciate techniques in computational data analytics.

4. Describe the limitations of current techniques and technologies in computing and data analytics.

5. Model real world problems from a data analytics perspective.

6. Design and construct a data analytics workflow to solve a data-intensive computational problem.

7. Identify, analyse and plan.

8. Identify and select appropriate data analysis techniques in a range of real-world contexts.

9. Apply quality concepts to computer programming and data analytics workflows.

10. Manage a computer-based project throughout all stages of its lifecycle.

11. Apply best practice in the fields of computing and data analytics.

12. Apply diagnostic skills in a range of data-focused contexts.

13. Discuss, plan and implement fundamental techniques in computing, including programming.

14. Work autonomously in solving problems using a computer.

15. Plan and track the development of software by a group of people.

16. Recognise the different roles involved in organising a project in data analysis.

17. Locate and evaluate documentation and information through online research.

18. Assimilate new skills and techniques in computing through online learning.

19. Criticise computational work in a constructive manner.

20. Critique the ways in which data analysis affects the world.

21. Summarise how academic and industrial research leads to new knowledge, solutions and techniques in data analysis.

22. Recommend an appropriate course of action based on results from data analysis.

Career Opportunities

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).

Follow-on Studies: 

A sufficient result on this course would qualify students to undertake Level 9 (Master) programmes.

Essential Information: 

Course Start Date:

January 2022 if approved under Springboard+2021- check www.springboardcourses.ie for actual start date

 

Delivery Method:

Fully online with recorded lectures, followed by online discussion groups with the lecturers

 

Application Process:

Applications must be made through www.springboardcourses.ie

 

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 European Structural and Investment Funds Programme 2021-2027.

Contact Us

School:                           School of Science

Department:                   Department of Computing & Applied Physics

Head of Department      Dr. Sean Duignan

 

Contact person for the course

Peter Butler
Graduate Studies & Professional Development
Galway Mayo Institute of Technology
Dublin Road
Galway 

Phone
085 805 3691 from 09:00 to 17:00, Monday to Friday

Email: springboard@gmit.ie

Springboard+ is co-funded by the Government of Ireland and the European Social Fund as part of the ESF programme for employability, inclusion and learning 2021-2027