TRON07034 2020 Data Analytics and Visualisation

General Details

Full Title
Data Analytics and Visualisation
Transcript Title
Data Analytics and Visualisati
N/A %
Subject Area
TRON - Electronics
COEL - Computing & Electronic Eng
07 - NFQ Level 7
05 - 05 Credits
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Saritha Unnikrishnan
Programme Membership
SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies

This module covers the data analysis and visualisation skills required for level 8 in Electronics. This topic will introduce the learner to the SOTA data analysis tools and techniques, which help to interpret and extract meaningful information from data. The learner will gain expertise in data preprocessing, exploratory data analysis and pattern recognition using sensor data including public datasets.

Learning Outcomes

On completion of this module the learner will/should be able to;


Apply data pre-processing techniques.  


Visualise data graphically, identify feature correlation, remove/retain features based on variable importance.


Identify patterns in the data using exploratory data analysis and clustering techniques


Work on a given use-case to apply the techniques learned


Appreciate the data ethics and constraints that apply to the use of data in real-world scenarios.

Teaching and Learning Strategies

The theory of the key topics will be delivered through lectures. 

The code demonstrations of the algorithms will be performed using Python Jupyter notebooks.

Module Assessment Strategies

Two individual assignments and a final project are given to assess the Learning outcomes.

20% data preprocessing assessment

20% data visualisation and analysis assessment

60% project to solve a use-case provided by the lecturer in the data visualisation area


Repeat Assessments

Repeat the failed elements and the project.

Module Dependencies

COMP07187 201900 Object Oriented Programming

Indicative Syllabus

Data preprocessing:

  • Feature scaling and standardisation.
  • Handling noise and missing data.
  • Handling categorical data.

Data visualisation:

  • Graphics fundamentals.
  • Mapping visualisation techniques to specific datasets.
  • 2D and 3D visualisation.
  • Python/R libraries will be used.
  • Potential data sources are public data sets, Kaggle and real-time/recorded sensor data.

Pattern recognition:

  • How to perform exploratory data analysis
  • How to identify interesting patterns in data
  • Unsupervised clustering techniques such as K-means and Principal Component Analysis (PCA) will be discussed.
  • Programming languages such as Python/R (but not restricted to) will be used..


Coursework & Assessment Breakdown

Coursework & Continuous Assessment
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Moodle Quiz Continuous Assessment Open Book Exam 20 % Week 4 1
2 Problem based assignment Continuous Assessment Open Book Exam 20 % Week 8 2,3
3 Individual Project Project Project 60 % Week 13 1,2,3,4,5

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 1 Weekly 1.00
Laboratory Practical Computer Laboratory Practical 2 Weekly 2.00
Independent Learning Not Specified Independent Learning 4 Weekly 4.00
Independent Learning Not Specified Indepenedent Learning 5 Weekly 5.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Online Lecture 1 Weekly 1.00
Laboratory Practical Online Online Lab 1 Weekly 1.00
Total Online Learning Average Weekly Learner Contact Time 2.00 Hours

Required & Recommended Book List

Required Reading
2012-03-31 Discovering Statistics Using R SAGE
ISBN 9781446200452 ISBN-13 1446200450

The R version of Andy Field's hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

Required Reading
11/04/2017 Python: Data Analytics and Visualization Packt Publishing

Module Resources