Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Coursework Only |
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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. | |||||||||||||||||||||||||||||||||||||||||||
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 |
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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. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||