Course Overview

Sign up to our Data Visualisation Masterclass with Vincent Mc Grady on Tuesday 27th September.

“Data really powers everything that we do.” — Jeff Weiner

This course will provide you with an overview of the entire data visualisation process in addition to introducing important development constructs and methodologies while using Power BI as a focal point. As this is an introductory course, no pre-requisite qualifications are necessary, but any experience in Computing and particularly in data visualisation is advantageous.
This course is designed to meet the needs of candidates who are currently working in related fields and may want to up-skill or for those who wish to pursue entry-level roles in the ICT sector. This course is also ideal for those who wish to pursue further study in Computer Science.

Why Data Visualisation?

Data has been called the new oil because it is a business asset and has a value, but in reality, data is the new sunlight. Unlike oil, data can be used over and over again, to keep driving insight and keep delivering value.

All organisations today are increasingly becoming data driven, the ability to work with data isn’t a bonus it’s essential. Data visualisation is about becoming a story teller presenting key insights in a form that is easy to understand. It is a unique mixture of art and science delivering impactful, insightful and digestible data visualisations. This requires balancing phycological design principles & colour theory, with engineering a suitable data set and analysing the data accurately.

Power BI was chosen as the tool to deliver this module. Microsoft Power BI is a fantastic tool that can handle all of the analytical pipeline from ingesting and modelling data to presenting it as a dashboard and distributing those dashboards to anyone who cares. Because of its potential as a complete data Pipeline, Microsoft Power BI is fast becoming one of the most used tools on the market.

  • Average Data Analyst salary in Dublin is €60k
  • Data analysts are in high demand and this field is still growing
  • It presents excellent opportunities for professional development

Course Modules

Theory and Concepts of Data Visualisation
• History of data visualisation.
• Understand the various categories used in the field e.g., Information/data/scientific visualisation, infographics, visual analytics.
• Investigate theorists and best practice in these fields, e.g., cognitive amplification, perceptual enhancement and ways to encourage inferential processes.

Introduction to Power BI
• How to download and install Power BI
• How to import data into Power BI
• How to transform Data within Power BI
• How to create data visualisations with Power BI

Data visualisation pre-processing techniques
• Learn data cleaning techniques relevant to data visualisation – data aggregation, data sampling, impute missing data, find inconsistencies.
• Learn transformation techniques – data normalisation, construct new variables, investigate how to use regular expressions and data manipulation techniques to pre-process data sets.

Data Analysis
• Learn how to interrogate data
• Learn creating calculations and formulae to drive insights

Data Visualisation traditional statistical approaches
• Histograms, boxplots, scatter plots
• Analysing correlations and patterns between variables
• Univariate, bivariate and multivariate ways of presenting data

Advanced visualisation techniques
• Investigate computer-based tools for visualisation and their features – interactivity, geospatial methods, hierarchical and networks solutions.

Visual Analytics
• Understand and critique the various visualisation methods used to solve data mining and data analytics problems, e.g., anomaly detection, pattern discovery.

Data Analytics Techniques
• Investigate the main pitfalls in data visualisation and data analytics in a realworld setting.
• Compare and contrast data analytics techniques investigating their theoretical principles, assumptions, strengths and weaknesses.


Learning Outcomes

  1. Describe the concepts, principles and methods of data visualisation.
  2. Select and apply a variety of data explorative and pre-processing techniques to a range of data visualisation problems.
  3. Design and implement appropriate data visualisation techniques to solve data analytical problems.
  4. Interpret, critique and communicate patterns and knowledge discovered as a result of applying data visualisation techniques to a variety of data sets and analytical problems.
  5. Research and appraise a variety of data analytics solutions to current challenges in the area.

Course Award

A City College’s Professional Diploma Course is a focused, practical programme designed and delivered by an industry practitioner, that consolidates, upskills or reskills learners in a professional area. They are stand-alone qualifications that indicate that you have been trained in a particular area or specific subject matter.

City College Diplomas are suitable for career minded learners wishing to advance their professional skills and prospects. They are widely accepted by employers and many students are sponsored to study here by their organisation.


A pass grade on the written assignment will be required for awarding the Diploma.
Similarly, the Project must be handed in on the due date unless a sound reason for lateness is provided. In such instances, an extension may be awarded at the discretion of the lecturer.


Vincent Mc Grady

Vincent currently works as the Head of Performance in Bank of Ireland were he leads a team of analysts to drive insights in the banks insurance arm. Vincent has a masters in Computer Science from University College Dublin were he graduated with first class and a bachelors from Trinity College Dublin in Information Systems were he also graduated with First Class honors. Vincent has worked as a lecturer for the last 4 years delivering a range of modules in various colleges and universities in Dublin.


Contact Us

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Live online training with real-time instructor-student interaction.
  • Live Fully online
  • Archived for review
January 2023
10 weeks, 1 evening per week from 6.30 to 9.30pm GMT
Study in a classroom environment one evening a week.
  • City centre location
  • Fully interactive
  • Limited class sizes
January 2023
10 weeks, 1 evening per week from 6.30 to 9.30pm GMT

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