Module:

Latest Module Specifications

Current Academic Year 2025 - 2026

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Module Title
Module Code	(ITS: CA274)
Faculty		School
NFQ level		Credit Rating

Description

This module aims to give the student a background in using a programming language such as R to deliver a competent analysis of both structured and unstructured data.

Learning Outcomes

1. R Basics: The student should be able to manipulate and read data into various R dataframes and R Tables.
2. R Objects: Creating a library in R and using class objects.
3. Parallel Programming: Developing Parallel code to handle computationally intensive analysis.
4. Visualisation: Basic plots, Geographic Maps, Multi-Dimensional reduction, 3D plotting, Dynamic Graphics
5. Big Data in R: The student should be able to handle large data-sets and demonstrate the various techniques and libraries that can be used in R to analyze BIG data-sets.
6. Differential Equations and linear Algebra in R: Understanding of the packages used in linear Algebra and differential calculus.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	12	Lectures will cover the material required for the course.
Laboratory	24	Laboratory will be used to demonstrate the techniques and R packages taught in class.
Assignment Completion	89	The will be 4 Assignments throughout the term. Assignments will vary in marks from 10 to 40 % of the final mark. See Module Content and Assessment.
Total Workload: 125

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Data manipulation with a specified data-set. One should demonstrate how to pivot a table and provide a detailed univariate statistics with supplementary graphics.	10%	Week 3
Assignment	Create a R library that will have a numer of class objects that can be used to create new features for a chosen dataset. These features should include transformations such as moving averages, exponential averages and other potential smoothing utilities.	20%	Week 6
Assignment	Demonstrate how simple linear regression can be implemented using parallel programming in R	30%	Week 8
Assignment	Complete a data analysis on a Big dataset that combines both text and numeric data.	40%	Week 12

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	1,
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

R Basics
Introduction to R. Variable function declaration, Creating data-frames & Matrices, Reading data from outside sources such as Databases/CSV /XML files

R objects
Library development in R. How to get the best out of CRAN. Using R objects and classes to handle data.

Parallel programming in R
Developing Parallel code to handle simple computationally intensive basic analysis. The following packages will be examined:

pbdMPI/openMP, pbDSLAP, snowfall,foreach, future, rborist, randomForestSRC

R visualisation
Basic plots, Geographic Maps, Multi-Dimensional reduction, 3D plotting, Dynamic Graphics

R Big Data
Package, rJava, RCCP, pqR,pddR

Maths in R
Cover some Linear algebra and Differential Calculus techniques in R.

Indicative Reading List

Books:

Jane M. Horgan: 2009, Probability with R, Wiley, Hoboken, N.J., 9780470280737
Robert Kabacoff: 0, R in Action, Manning Publications, 375, 9781935182399
Tony Fischetti: 2015, Data Analysis with R, Packt Publishing, 9781785288142

Articles:
None

Other Resources

None