TRON07034 2020 Data Analytics and Visualisation
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.
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 the failed elements and the project.
- Feature scaling and standardisation.
- Handling noise and missing data.
- Handling categorical data.
- 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.
- 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
|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
|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|
Online Learning Mode Workload
|Laboratory Practical||Online||Online Lab||1||Weekly||1.00|
Required & Recommended Book List
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.
11/04/2017 Python: Data Analytics and Visualization Packt Publishing