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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Analytics for Engineers
Module Code MM382
School School of Mechanical and Manufacturing Engineering
Module Co-ordinatorSemester 1: Jeremiah Murphy
Semester 2: Jeremiah Murphy
Autumn: Jeremiah Murphy
Module TeachersBrian Corcoran
Jeremiah Murphy
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Coursework Only
A similar set of problems to work on

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

Workload Full-time hours per semester
Type Hours Description
Lecture24two lectures per weel
Laboratory121 hour weekly computer laboratory session
Online activity24online courses supplementing lectures and laboratory work
Independent Study48preparation 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 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.

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 Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Loop ExamMCQ test20%Week 4
Loop ExamMCQ test20%Week 7
Loop ExamMCQ test27%Week 10
Loop QuizSet of questions addressing all the learning outcomes of the semester33%Week 12
Reassessment Requirement Type
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
This module is category 1
Indicative Reading List

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

40555, Website, 0, r, https://stackoverflow.com/, 40556, Website, 0, r, https://www.r-project.org/,
Programme or List of Programmes
BMEDB.Eng. in Biomedical Engineering
BMEDIB.Eng. in Biomedical Engineering
CAMB.Eng. Mechanical & Manufacturing Eng
CAMIB.Eng. in Mechanical & Manufacturing Eng
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)
HDEATH.Dip Engineering Analysis & Technology
MEB.Eng. in Mechatronic Engineering
MEIB.Eng in Mechatronic Engineering
SSEBEng in Mechanical & Sustainability Eng
Date of Last Revision09-JAN-06

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