Data Analytics and Visualisation (PDip)

Applications and Selections

Who Teaches this Course

Requirements and Assessment

Glossary of Terms

Year 1 (60 Credits)

RequiredCT5196: Data Analytics and Visualisation Project

CT5196: 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Adrian Clear 🖂
  

RequiredCT870: Internet Programming

CT870: 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  ENDA BARRETT 🖂
  

RequiredCT5200: Fundamentals of Python Programming

CT5200: 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  PATRICK MANNION 🖂
  

RequiredCT511: Databases

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 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

•  DEIRDRE KING 🖂
  
•  ATTRACTA BRENNAN 🖂
  

RequiredCT5197: Interaction Design

CT5197: 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Adrian Clear 🖂
  

RequiredST2001: Statistics for Data Science 1

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

• Continuous Assessment (30%)
• Computer-based Assessment (70%)

Teachers

•  COLLETTE MCLOUGHLIN 🖂
  
•  ANDREW SIMPKIN 🖂
  
•  CARL SCARROTT 🖂
  

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

RequiredCT5100: Data Visualisation

CT5100: 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

•  DEIRDRE KING 🖂
  
•  CONOR HAYES 🖂
  
•  GERALDINE HEALY 🖂
  

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

RequiredCT5198: Programming for Data Science

CT5198: 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

•  DEIRDRE KING 🖂
  
•  GERALDINE HEALY 🖂
  
•  Karl Mason 🖂
  

RequiredST2002: Statistics for Data Science 2

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

• Written Assessment (75%)
• Continuous Assessment (25%)

Teachers

•  COLLETTE MCLOUGHLIN 🖂
  
•  JOHN NEWELL 🖂
  
•  EMMA HOLIAN 🖂
  
•  ANDREW SIMPKIN 🖂
  

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

RequiredCT5199: Business Data Analytics

CT5199: 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

•  DEIRDRE KING 🖂
  
•  OWEN MOLLOY 🖂
  
•  GERALDINE HEALY 🖂