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Data Analytics and Visualisation (HDip)

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Course Overview

This course is a conversion course to enable graduates 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.

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 or MSc Artificial Intelligence, providing a deeper and more specialised training in advanced Data Science or Artificial Intelligence topics such as Machine Learning. Transition to these programmes is contingent on spaces and achieving a minimum of a high 2:1 in the Higher 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.

This course is open for applications now, via Springboard website.

To check whether you are eligible for HCI funding, you can check last year's page. Scroll to the section "Am I eligible for the HCI Graduate Conversion Courses 2021".
 
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 Computer Science and Software Engineering postgraduate programmes. 

Applications and Selections

This course is now open for applications through the Springboard website. For more information, and to apply through Springboard please see: https://springboardcourses.ie/details/10074.
 
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

Dr Enda Barrett

 

researcher
Dr Owen Molloy
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researcher
Dr. Attracta Brennan
M.Eng.Sc., Ph.D
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researcher
Professor John Newell
BSc, MSc, PhD.
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researcher
Dr. James Duggan
B.E., M.Eng.Sc., Ph.D.
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researcher
DR CONOR HAYES
B.A., M.Sc., Ph.D.
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researcher
Ms. Karen Young
B.Ed., M.A.
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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

Duration

1 year, full-time

Next start date

September 2023

A Level Grades ()

Average intake

25

QQI/FET FETAC Entry Routes

Closing Date

Please view the review dates website for information.

NFQ level

Mode of study

ECTS weighting

60

Award

CAO

Course code

HDIP-DAV

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, Human Computer Interaction, Statistics for Data Science, Programming with Python.

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

A major aspect of the programme is the Industrial Data Analytics 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)

Required CT5160: Industrial Data Analytics 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. Understand a business-related problem, and design a data analytics-based solution in collaboration with industrial 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.
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module CT5160: "Industrial Data Analytics Project " and is valid from 2020 onwards.
Note: Module offerings and details may be subject to change.

Required CT5161: Introduction to Programming in Python


Semester 1 | Credits: 5

Introduction to Programming in Python (Algorithms and Information Processing, Control Structures, Modular Programming, Object-Oriented Programming, File Input/Output, Data Structures, Graphics and Graphical User Interfaces)
(Language of instruction: English)

Learning Outcomes
  1. Achieve fluency in the use of procedural statements — assignments, conditional statements, loops, function calls — and sequences. Be able to design, code, and test small Python programs that meet requirements expressed in English.
  2. Adequately use standard programming constructs: repetition, selection, functions, composition, modules, aggregated data (arrays, lists, etc.).
  3. Understand and use object based software concepts.
  4. Adapt and combine standard algorithms to solve a given problem.
  5. Identify and repair coding errors in a program.
  6. Use library software for (e.g.) building a graphical user interface, web application, or mathematical software.
  7. Understand and apply basic searching and sorting algorithms.
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
The above information outlines module CT5161: "Introduction to Programming in Python" and is valid from 2020 onwards.
Note: Module offerings and details may be subject to change.

Required CT511: Databases


Semester 1 | Credits: 5

This module will provide the student with the information and technical know-how to establish, manage and optimally use databases. This will be essential information for those interested in Clinical Research administration.
(Language of instruction: English)

Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module CT511: "Databases" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required CT870: Internet Programming


Semester 1 | Credits: 5


(Language of instruction: English)

Learning Outcomes
  1. Design and implement web pages
  2. Connect a website to a database
  3. Create dynamic web content
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (85%)
  • Continuous Assessment (15%)
Module Director
Lecturers / Tutors
The above information outlines module CT870: "Internet Programming" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required CT865: Human Computer Interaction


Semester 1 | Credits: 5

Postgraduate Introduction to HCI

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 interaction 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
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (80%)
  • Continuous Assessment (20%)
Module Director
Lecturers / Tutors
The above information outlines module CT865: "Human Computer Interaction" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Required ST2001: 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

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (75%)
  • Continuous Assessment (25%)
Module Director
Lecturers / Tutors
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 2019 onwards.
Note: Module offerings and details may be subject to change.

Required CT5162: Business Intelligence


Semester 2 | Credits: 5

Database and Data Warehouse Technologies and Architectures, Data Integration and ETL (Extract, Transform, Load) concepts and tools, Data Modelling, NoSQL, OLAP, KPIs (Key Performance Indicators), Dashboarding, Querying and Reporting, Vendor Tools
(Language of instruction: English)

Learning Outcomes
  1. Understand and describe the various architectures and main components of a data warehouse.
  2. Design a data warehouse, and be able to address issues that arise when implementing a data warehouse.
  3. Compare and contrast OLAP and data mining as techniques for extracting knowledge from a data warehouse.
  4. Understand and apply common techniques for ETL (Extract, Transform, Load) data preparation and cleaning.
  5. Describe and apply OLAP (online analytical processing) techniques and tools.
  6. Understand and apply basic data mining techniques.
  7. Apply data modelling and data warehouse design techniques.
  8. Create dashboards, querying and reporting tools using commercial software tools.
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
The above information outlines module CT5162: "Business Intelligence " and is valid from 2022 onwards.
Note: Module offerings and details may be subject to change.

Required CT5163: Applied Data Science with R


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 the R statistical programming language.
  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.
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module CT5163: "Applied Data Science with R" and is valid from 2021 onwards.
Note: Module offerings and details may be subject to change.

Required ST2002: 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

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Written Assessment (75%)
  • Continuous Assessment (25%)
Module Director
Lecturers / Tutors
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.

Required CT5100: Data Visualisation


Semester 2 | Credits: 5

Data Visualisation is concerned with techniques and technologies for the visual representation of data and the results and evaluation of data analytic processes. 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 analytics outcomes. The practical work in this module is done using the R programming language - and learners are expected to have completed an introductory module in R.
(Language of instruction: English)

Learning Outcomes
  1. describe the basic design principles underlying human perception, color theory and narrative
  2. select different types of visualisations for use in various exploratory and explanatory scenarios
  3. deploy the grammar of graphics to build interpretable static and interactive visualisations using R's visualisation packages and other processing libraries from the tidyverse set of R packages
  4. deploy visualisation techniques during exploratory analysis work flow
  5. build custom visualisations that represent specific data-driven narratives
  6. outline at least one research area in Data Visualisation based on your reading of at least two research papers
Assessments

This module's usual assessment procedures, outlined below, may be affected by COVID-19 countermeasures. Current students should check Blackboard for up-to-date assessment information.

  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
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 2022 onwards.
Note: Module offerings and details may be subject to change.

Why Choose This Course?

Career Opportunities

The Higher 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. Skills Ireland estimates a demand for Big Data/Analytics roles to the tune of up to 62,000 in Ireland by 2020. 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. 2023/24

Fees: Tuition

€

Fees: Student levy

€

Fees: Non EU

€20,750 p.a. 2023/24

 

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 

Downloads

  • Postgraduate Prospectus 2023

    Postgraduate Prospectus 2023 PDF (20.6MB)