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Data Analytics and Visualisation (PDip)

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Course Overview

This course is designed to reskill and upskill graduates to prepare them to take up the increasing opportunities to work as data analysts, who are in demand across multiple sectors, including Financial, Government, Manufacturing, Food, Health and Media.

Course detail is at: https://springboardcourses.ie/details/11023.

The course emphasises the development of strong theoretical and applied foundations, and builds on our existing strengths in Data Science and Analytics in the School of Computer Science and the Insight Institute, and our experience in running a successful Masters in Data Analytics.

The programme has a number of core elements:

  • Immersion in fundamental database and software development techniques.
  • A solid foundation in statistical and analysis methods.
  • Expertise in data analysis, visualisation and business intelligence using leading edge tools and programming languages.
  • Capstone project to deepen and demonstrate students’ acquired skills.
  • A significant placement/internship allowing participants to gain relevant experience and also provide Industry Partners with an opportunity to assess potential recruits.

On completion of the programme, graduates will be eligible to take our highly successful MSc Data Analytics, MSc Artificial Intelligence, and MSc Adaptive Cybersecurity, providing a deeper and more specialised training in advanced Data Science, Artificial Intelligence, and Cybersecurity topics such as Machine Learning. Transition to these programmes is contingent on spaces and achieving a minimum of a high 2:1 in the Postgraduate Diploma at the discretion of the programme director. 

This programme is funded by the Higher Education Authority Human Capital Initiative, Pillar 1*, Graduate Conversion initiative. For applications who are in employment the HEA will fund 90% of the course fee, with the balance to be provided by the application or her/his employer. Recent graduates will also pay 10% of the cost of the course. Apply now, for funded positions, via Springboard.

Applications can also be made (non-funded positions) through the University of Galway Postgraduate Admissions page. 

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You may also be interested in one of our other School of Computer Science postgraduate programmes.

Applications and Selections

Apply now via Springboard.
 
If you have questions regarding eligibility or how to apply for this course, please contact the course director—click on the "Find Out More" link on the left of this page.
 
A small number of places may be made available for fee-paying students who are not eligible for HCI funding. Please contact the course director if you wish discuss this, or you may apply via the "Apply Now" link on the left of this page.

Who Teaches this Course

Requirements and Assessment

A range of assessment methods are integrated and applied through the programme. These include continuous assessment, projects, reports presentations and case studies.

Key Facts

Entry Requirements

Applicants are normally required to hold a minimum of a Level 8 honours qualification (2.2 or higher) or equivalent in a cognate discipline. Graduates with a Level 7 degree and relevant practical industry experience in the area of computing and information technology will also be considered. Graduates from non-STEM (Science, Technology, Engineering, and Mathematics) disciplines such as languages will be welcomed, but will need to demonstrate an aptitude for logical thinking and problem solving. The application process will include interviews and/or aptitude tests, given that the placement is a key element of the programme.

The programme is in line with the University Policy for Recognition of Prior Learning in that it recognises prior academic qualifications. The aim of this initiative is to provide graduates with the opportunity to acquire qualifications for employment in the data analytics field. RPL applications are also welcome and can be completed by contacting the Programme Director. 

Additional Requirements

Recognition of Prior Learning (RPL)

The programme is in line with the University Policy for Recognition of Prior Learning in that it recognises prior academic qualifications. The aim of this initiative is to provide graduates with the opportunity to acquire qualifications for employment in the data analytics field. RPL applications are also welcome and can be completed by contacting the Programme Director.

Duration

1 year, full-time

Next start date

September 2025

A Level Grades ()

Average intake

20

QQI/FET FETAC Entry Routes

Closing Date

No set closing date. Offers made on a continuous basis.

NFQ level

Mode of study

ECTS weighting

60

Award

CAO

Course code

PGD-DDV

Course Outline

The programme is delivered over a 12-month period. The first two semesters consist primarily of taught modules, which have a high continuous assessment and practical aspect. The first semester focuses on creating a strong foundation in the Computer Science and Statistical techniques, including: Databases, Internet Programming, Interaction Design, Statistics for Data Science, and Python Programming.

The second semester focuses on deepening skills and applying them to real-life problems. The content includes Programming for Data Science, further Statistics for Data Science, Data Visualisation techniques, and Business Data Analytics theory and applications using widely used commercial tools.

A major aspect of the programme is the Data Analytics and Visualisation Project in which students work on a real-life data analytics and visualisation problem. This work will, where possible, be conducted in conjunction with the work placement company, resulting in the production of the final report and presentation of the project at the end of the work placement. The Work Placement will take place from the end of semester 2 until end August.

Curriculum Information

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits
You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
Module
An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
Optional
A module you may choose to study.
Required
A module that you must study if you choose this course (or subject).
Semester
Most courses have 2 semesters (aka terms) per year.

Year 1 (60 Credits)

RequiredCT5196: Data Analytics and Visualisation Project


15 months long | Credits: 15

Applied Data Analytics and Visualisation project, in collaboration with industry and placement partner.
(Language of instruction: English)

Learning Outcomes
  1. Apply Data Analytics and Visualisation techniques to solve a real-world problem.
  2. Analyse a business-related or academic problem, and design a data analytics-based solution in collaboration with industrial or academic partners.
  3. Report on exploratory analysis of the problem domain.
  4. Produce a detailed report on the problem, diagnosis and solution design.
  5. Demonstrate the ability to research and apply state-of-the-art techniques in data analytics and visualisation.
  6. Gain an understanding of existing academic literature relevant to the project topic
Assessments
  • Continuous Assessment (100%)
Teachers
The above information outlines module CT5196: "Data Analytics and Visualisation Project" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT870: Internet Programming


Semester 1 | Credits: 5

The module covers the basics of web programming, including HTML, CSS and JavaScript.
(Language of instruction: English)

Learning Outcomes
  1. Design and implement web applications using HTML, CSS and JavaScript
  2. Deploy web applications to a Cloud Platform
  3. Gain experience using frontend design frameworks such as Bootstrap
  4. Build dynamic content and be able to save data to a database
  5. Build REST APIs using modern serverless platforms
  6. Make server side requests using AJAX calls to developed REST APIs
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
The above information outlines module CT870: "Internet Programming" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT5200: Fundamentals of Python Programming


Semester 1 | Credits: 5

This module will provide learners with an introduction to fundamental programming concepts using Python, which is one of today’s most widely used and fastest growing programming languages. Students will be taught how to program from first principles; therefore, no prior knowledge of computer science or programming is required. Students will require access to a computer where they can install Python and the required development tools. The module is taught using software and resources that are free and open source. Topics covered include: fundamental concepts such as variables and conditional statements, performing calculations, loops, data structures, input and output, algorithms for searching and sorting.
(Language of instruction: English)

Learning Outcomes
  1. Apply standard programming constructs when writing code, e.g., assignments, conditional statements, loops, functions, sequences (tuples, lists, etc.).
  2. Adapt and combine standard programming constructs to solve a given problem.
  3. Write programs using object-based software concepts.
  4. Use modules and packages in Python programs for tasks such as mathematical calculations, pseudorandom number generation, etc.
  5. Identify and repair coding errors in programs.
  6. Design, program and test short Python programs that meet requirements expressed in English
  7. Describe basic searching and sorting algorithms, and apply them to simple datasets.
  8. Write Python programs incorporating real-world computational tasks (e.g. working with data stored on a filesystem, processing command-line inputs, presenting the results of calculations in tabular format)
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Teachers
The above information outlines module CT5200: "Fundamentals of Python Programming" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT511: Databases


Semester 1 | Credits: 5

This module will provide the student with the information and technical know-how to establish, manage and optimally use relational databases.
(Language of instruction: English)

Learning Outcomes
  1. Analyse the limitations of file based systems
  2. Apply a Database Development Process
  3. Create an EERD showing how entities relate and interact
  4. Apply normalisation rules from 1NF to 4NF
  5. Apply denormalisation rules
  6. Use SQL
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
The above information outlines module CT511: "Databases" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT5197: Interaction Design


Semester 1 | Credits: 5

Interaction design and human computer interaction are concerned with the design of computing systems for usability and user experience. Human-centered design is a process for designing and developing such products, which includes an early focus on people and their practices, and an iterative cycle involving empirical measurement for understanding and evaluation. This module takes a practical approach to introducing learners to doing human-centered design and includes a substantial group project for this purpose. The module emphasises the importance of empathising with relevant stakeholders. It covers design principles, the strengths and weaknesses of human cognition, and tools and methods for capturing and analysing data and doing design work.
(Language of instruction: English)

Learning Outcomes
  1. Elaborate the importance of design in professional and social contexts and the critical role of users in the systems design process
  2. Distinguish between human cognition and emotion and assess their role in effective interactive system design
  3. Identify the roles of human agents and those of digital agents in any interaction
  4. Develop the knowledge and skills necessary to analyse, design and evaluate good quality interactive systems
  5. Competently differentiate between various Interaction Design processes or approaches
  6. Analyse technological developments and innovations in social, educational and leisure computing and their implications for user experience and interaction design
  7. Practice approaches to understanding and designing with users
Assessments
  • Written Assessment (80%)
  • Continuous Assessment (20%)
Teachers
The above information outlines module CT5197: "Interaction Design" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredST2001: Statistics for Data Science 1


Semester 1 | Credits: 5

The course provides an introduction to probabilistic and statistical methods needed to make reasonable and useful conclusions from data. Topics include probabilistic reasoning, data generation mechanisms, modern techniques for data visualisation, inferential reasoning and prediction using real data and the principles of reproducible research. The course will rely heavily on R (a free open source language) and will include examples of datasets collected in a variety of domains.
(Language of instruction: English)

Learning Outcomes
  1. Calculate conditional probabilities and probabilities for random variables from standard distributions (Binomial, Poisson, Normal).
  2. Summarise data numerically (centre and spread) and graphically (e.g. bar charts, line, area, boxplots, histograms, density plots, scatterplots) with an emphasis on best practice for communication.
  3. Summarise the importance of probabilistic based sampling schemes (e.g. simple random sampling, stratified sampling, cluster sampling).
  4. Summarise the difference between observational and experimental studies and the principles of experimental design.
  5. Perform probability calculations about the sample mean and use them to make inferential statements using the Central Limit Theorem.
  6. Calculate interval estimates for parameter estimation in one sample problems using classical and computational (i.e. bootstrap) approaches.
  7. Perform hypothesis testing (null and alternative hypotheses, type I and II errors and p-values) in a variety of scenarios.
  8. Fit and interpret a simple linear regression model.
  9. Compile a statistical report, i.e. prepare a typed document which introduces the statistical research question being explored, describes the data collection mechanism, provides subjective impressions on relevant numerical and graphical summaries, and outlines conclusions from all formal statistical analyses undertaken.
Assessments
  • Continuous Assessment (30%)
  • Computer-based Assessment (70%)
Teachers
Reading List
  1. "Open Intro Stats" by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel
    Publisher: Open Intro
  2. "R for Data Science" by Garrett Grolemund, Hadley Wickham
    Publisher: O’Reilly
  3. "Hitchhikers Guide to GGplot2" by Mauricio Vargas Sepúlveda and Jodie Burchell
    Publisher: Leanpub
  4. "An Introduction to Statistical and Data Sciences via R" by Chester Ismay and Albert Y. Kim
The above information outlines module ST2001: "Statistics for Data Science 1" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT5100: Data Visualisation


Semester 2 | Credits: 5

Visualisation is a fundamental technique in presenting the properties of data and the results and evaluation of data analytical processes. For a data visualisation to be successful the analyst needs to have considered the properties of the data, the information to be communicated, the mode of visualisation delivery and the expectations of the audience. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytical outcomes. The module emphasises the need to communicate clearly, while adhering to the ethical requirement to present data-derived information truthfully and without bias. The examples covered during the module have been generated using the R language. However, students can use other languages and visualisation applications in their assignment work.
(Language of instruction: English)

Learning Outcomes
  1. Describe the basic design principles underlying human perception, color theory and narrative
  2. Analyse the effectiveness of different visual elements in communicating analytical information
  3. Select the best visualisation strategy to use for different exploratory and explanatory scenarios
  4. Execute different types of data visualisations for use in various exploratory and explanatory scenarios
  5. Carry out basic data preprocessing and wrangling necessary to produce effective visualisations
  6. Discuss the ethical issues of representing data and information truthfully when creating a visualisation
  7. Critically evaluate data visualisations produced by other people
Assessments
  • Written Assessment (65%)
  • Continuous Assessment (35%)
Teachers
Reading List
  1. "R Graphics Cookbook" by Winston Chang
    Publisher: O'Reilly
  2. "ggplot2" by Hadley Wickham
    ISBN: 9783319242750.
    Publisher: Springer
  3. "Information Visualization" by Colin Ware
    ISBN: 9780123814647.
    Publisher: Elsevier
  4. "Now You See it" by Stephen Few
    ISBN: 9780970601988.
    Publisher: Analytical Press
  5. "The Visual Display of Quantitative Information" by Edward R. Tufte
    ISBN: 9781930824133.
    Publisher: Graphics Press
The above information outlines module CT5100: "Data Visualisation" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT5198: Programming for Data Science


Semester 2 | Credits: 5

Using the R programming language and tidyverse libraries for exploratory data analysis, data visualisation, data modelling and data transformation.
(Language of instruction: English)

Learning Outcomes
  1. Evaluate the functionality of R and Python for data science
  2. Perform data cleaning, manipulation and wrangling techniques to specified data problems.
  3. Implement appropriate data visualisation techniques to examine real world datasets.
  4. Investigate statistical modelling techniques.
  5. Develop best practice in terms of reproducible documentation and version control.
  6. Gain an introductory level understanding of machine learning for data science
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
The above information outlines module CT5198: "Programming for Data Science" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

RequiredST2002: Statistics for Data Science 2


Semester 2 | Credits: 5

This course will provide an introduction to commonly used techniques in statistics when analysing data from experiments and observational studies. Topics include classical and modern methods in interval estimation, regression models for prediction problems, modern approaches for visualising multivariate data and the principles of reproducible research.

Learning Outcomes
  1. Conduct and interpret a two-sample and paired t-test using classical hypothesis testing and modern computational approaches.
  2. Conduct and interpret a chi-square test using classical and computational approaches.
  3. Use Simple Linear Regression (SLR) to make inferences about relationships between a response variable and an explanatory variable.
  4. Check the assumptions underlying a SLR model.
  5. Apply methods to visualise multivariate data (e.g. radar plots, case profile plots, heatmaps).
  6. Apply hierarchical clustering techniques (e.g. nearest neighbours) in multivariate data.
  7. Compile a statistical report, i.e. prepare a typed document which introduces the statistical research question being explored, describes the data collection mechanism, provides subjective impressions on relevant numerical and graphical summaries, and outlines conclusions from all formal statistical analyses undertaken.
Assessments
  • Written Assessment (75%)
  • Continuous Assessment (25%)
Teachers
Reading List
  1. "Open intro Stats" by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel
    Publisher: OpenIntro
  2. "Statistical inference for Data Science" by Brian Caffo
    Publisher: Leanpub
  3. "Hitchhikers Guide to GGplot2" by Mauricio Vargas Sepúlveda and Jodie Burchell
    Publisher: Leanpub
  4. "R for Data Science" by Garrett Grolemund, Hadley Wickham
    Publisher: O'Reilly
The above information outlines module ST2002: "Statistics for Data Science 2" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

RequiredCT5199: Business Data Analytics


Semester 2 | Credits: 5

Modern Business Data Analytics process, tools and techniques. From exploratory data analysis to development of descriptive, diagnostic, predictive and prescriptive data analytics solutions using modern tools including Power BI and Python.
(Language of instruction: English)

Learning Outcomes
  1. Demonstrate competence in the application of Business Data Analytics in settings such as business operations and performance improvement
  2. Extract, manipulate and mine data from various sources using modern techniques and tools
  3. Perform exploratory analysis using statistical techniques and tools such as Excel, Power BI, Tableau and Python
  4. Design and create descriptive analytics solutions to business problems using insightful reports and data visualisations and dashboards
  5. Demonstrate competence in the choice and development of appropriate descriptive, diagnostic, predictive and prescriptive analytics techniques
  6. Apply predictive analytics techniques to business questions, including techniques such as classification, regression, clustering and forecasting
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Teachers
The above information outlines module CT5199: "Business Data Analytics" and is valid from 2024 onwards.
Note: Module offerings and details may be subject to change.

Why Choose This Course?

Career Opportunities

The Postgraduate Diploma in Data Analytics responds to a strong and growing demand for graduates with skills in data analysis across all industry sectors. Every industry has seen a huge growth in the amount of data which they generate and collect, which represents a very valuable resource for companies. Demand for workers with specialist data skills like data scientists and data engineers has increased dramatically over the past five years according to recent surveys. Roles that will be suitable for graduates of this programme include: 

  • Data Analysts
  • Data Visualisation Specialists
  • Data Engineers
  • Data Scientists
  • Business Analytics Specialist
  • Business Intelligence Developer

Who’s Suited to This Course

Learning Outcomes

Transferable Skills Employers Value

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€6,500 p.a. (including levy) 2025/26

Fees: Tuition

€6,360 p.a. 2025/26

Fees: Student levy

€140 p.a. 2025/26

Fees: Non EU

€23,500 p.a. (€23,640 including levy) 2025/26

 

For Students Eligible for HCI Pilar Funding: There are no tuition fees for DEASP customers or returners but any subsequent costs such as travel, and course materials must be borne by the participant

*A 10% course fee contribution (€650) for graduate conversion courses is applicable for employed participants and recent graduates.

The formerly self-employed not in receipt of a DEASP payment must also pay 10%. This is payable directly to the provider.

For further details see https://springboardcourses.ie/faq. 

Find out More

Dr Adrian Clear
E: adrian.clear@ universityofgalway.ie 
https://springboardcourses.ie/details/11023

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