Module: Data Analytics for Engineers

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analytics for Engineers
Module Code	MEC1029 (ITS: MM382)
Faculty	Mechanical & Manufacturing Eng	School	Engineering & Computing
NFQ level	8	Credit Rating	5

Description

To introduce the main concepts of data analytics and to give students a working knowledge of the practical application of these techniques in the field of engineering

Learning Outcomes

1. apply the basic techniques of data analysis
2. apply the fundamental laws of probability
3. demonstrate an awareness of the need for statistical techniques in engineering
4. collate, analyse, present and interpret basic engineering technology data sets
5. gather basic data from codes of practice, databases and other sources

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	two lectures per weel
Laboratory	12	1 hour weekly computer laboratory session
Online activity	24	online courses supplementing lectures and laboratory work
Independent Study	48	preparation for assessments and ungraded project assignments
Total Workload: 108

Section Breakdown
CRN	10231	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	John Geraghty	Module Teacher	Brian Corcoran, Jeremiah Murphy

Type	Description	% of total	Assessment Date
Assessment Breakdown
Loop Exam	MCQ test	20%	Week 4
Loop Exam	MCQ test	20%	Week 7
Loop Exam	MCQ test	27%	Week 10
Loop Quiz	Set of questions addressing all the learning outcomes of the semester	33%	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	None
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

Introduction
Introduction to data analytics, data visualisation and statistical programming languages.

The R programming language
The basics of the R programming language. Vectors, matrices and data frames. Functions. Working with packages within R. Loading data into R.

The shape of data
Univariate data. Frequency distributions. Central tendency and spread. Introduction to populations, samples and estimation. Probability distributions. Data visualisation.

Describing relationships
Multivariate data. Relationships between a categorical and continuous variable. Relationships between two categorical variables. Relationships between two continuous variables. Visualisation methods.

Predicting continuous variables
Linear models. Simple linear regression. Anscombe's quartet. Multiple regression. Regression with a non-binary predictor.

Probability
Definition. Bayes’ Theorem. Random variables. Binomial distribution. Normal distribution.

Inferential statistics
Estimating means. The Central Limit Theorem. Interval Estimation. The effect of small samples. Introduction to Quality Control Charts using R.

Testing hypotheses
The null hypothesis significance testing framework. Testing the mean of one sample. Testing two means. Testing more than two means.

Indicative Reading List

Books:

Fischetti: 0, Data analysis with R, 2nd,
Roger Peng: 0, Exploratory data analysis with R,

Articles:
None

Other Resources

Website: r, https://stackoverflow.com/
Website: r, https://www.r-project.org/