Skip to main content

Course Search

Course Search

Course Search

Data Visualisation (Certificate, L9, 10 ECTS)

  • Status: Register Your Interest

  • Campus: Online

  • weeks: 10

  • Fees: €1,200* See FEES Section below for Skillnet Funding


Course Overview

This module is aimed at manufacturing and process engineers, and other relevant industry professionals who work with large amounts of data on a regular basis. The module aims are:

  • To improve participants visual and design awareness by harnessing their natural ability to recognise visual patterns within data retrieved from in-house or work related data sources
  • To strengthen design approaches and methodologies for interpreting data in visually accessible and stimulating ways for industry professionals
  • To help industry professionals understand and present their data more effectively therefore allowing them to react decisively and quickly to correct challenges in their work environment

Contact Details

General Queries

Flexible Learning Office

Email: flexible.midwest@tus.ie

Telephone: (061) 293802

Academic Queries

Eva Shortt

Email: Eva.Shortt@tus.ie

What are the entry requirements?

Applicants must possess a primary honours degree or equivalent.

Recognition of Prior Experiential Learning (RPL) will be granted based on relevant experience and training in accordance with RPL policy.

This is a level 9 Special Purpose Award. It is expected that candidates would have prior experience working with technology and have a working knowledge of relevant data software.

Recognised Prior Learning (RPL) – Assessment
Please review the academic entry requirements for this programme.
If you do not hold these qualifications but would like your application to be assessed under RPL please select YES on the online application form.
Once you have submitted your online application you will then receive an email acknowledgement with further instructions on RPL.

ENGLISH LANGUAGE: Applicants who do not have English as their first language must ensure they satisfy English Language requirements. For entry to undergraduate courses, a score of 5.5 in an IELTS or equivalent exam is required. For postgraduate courses, a score of 6.0 in an IELTS or equivalent exam is required. It is the responsibility of the applicant to ensure their English proficiency meets these requirements.

Course Content

  • INTRODUCTION TO DATA VISUALISATION

    Definitions / historical perspective / data visualisation / information design / visual storytelling / key elements of effective data visualisation / case studies

  • VISUAL STORYTELLING

    Audience / intention / meaning and significance / visual storytelling / case studies

  • APPROACHES TO DATA VISUALISATION

    Design convention / using colour / typography / space hierarchy / key examples

  • SELF-INITIATED PROJECT

    Each week for the duration of the project, there will be a specific time set aside to avail of tutorial advice online. Tutorial times will have to be arranged in advance.

More Information

  • The proposed delivery schedule is subject to change.

    Modules are to be delivered over 10 weeks in an online learning format.

    2 hours per week x 10 weeks

  • Each 5 credits will normally equate to approximately 100 Total Learning Hours. Total Learning Hours includes the time you spend in class (lectures, tutorials, practical elements) and the time you spend completing work outside of college. The balance between these two varies by discipline, and by level of study. You should bear in mind that the workload will increase at particular times e.g. when assignments are due.

  • Assessment is based on the results self-initiated project presentation.

  • Certificate in Data Visualisation (Special Purpose Award – Level 9, 10 ECTS)

  • €1,200*

    For ICBE Business Excellence Skillnet funding options, please contact Grainne Walsh, grainne@icbe.ie

  • 18 August 2024.

    Places are allocated on a first come first served basis. Course will be closed once the maximum number of applicants is reached. Courses run subject to viable numbers.