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Module Specifications

Archived Version 2019 - 2020

Module Title	Data Analytics for Engineers
Module Code	MM382
School	School of Mechanical and Manufacturing Engineering
Online Module Resources
Module Co-ordinator	Dr Jeremiah Murphy	Office Number	S365
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

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.

Assessment Breakdown
Continuous Assessment	70%	Examination Weight	30%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
Unavailable

Indicative Reading List

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

Other Resources

0, Website, 0, r, https://stackoverflow.com/, 0, Website, 0, r, https://www.r-project.org/,

Programme or List of Programmes

BMED	B.Eng. in Biomedical Engineering
BMEDI	B.Eng. in Biomedical Engineering
CAM	B.Eng. Mechanical & Manufacturing Eng
CAMI	B.Eng. in Mechanical & Manufacturing Eng
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)
ME	B.Eng. in Mechatronic Engineering

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