Module: Programming for Data Analysis

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Programming for Data Analysis
Module Code	CA274 (ITS) / CSC1033 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Stephen Blott
Module Teachers	-
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

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

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.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work 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: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

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

Other Resources

None