COMP07196 2020 Data Preparation and Visualisation

General Details

Full Title
Data Preparation and Visualisation
Transcript Title
Data Preparation&Visualisation
Code
COMP07196
Attendance
N/A %
Subject Area
COMP - Computing
Department
BUS - Business
Level
07 - NFQ Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Author(s)
Aine Doherty, Mary Carden
Programme Membership
SG_MBUSI_H08 202000 Bachelor of Arts (Honours) in Business & ICT
Description

This module is intended to introduce students to the concepts of gathering data from external sources and generating visualisations that could help drive business decisions. Understand how to display and support data analysis through a variety of techniques.

Learning Outcomes

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

1.

Obtain a basic understanding of data and be able to verify the correctness of the data.

2.

Utilise data preparation techniques to gather and organise different sources of data.

3.

Define key principles of data visualization.

4.

Describe common features of infographics.

5.

Generate visual representations of data to present summaries and potential solutions to problems based on the data.‚Äč

Teaching and Learning Strategies

A practical approach to teaching and learning will be used. Problem-based learning will be used where possible. The two hour lectures will be used to introduce core concepts and strengths/weaknesses of different visualisation techniques. The lab practicals will be used to learn key concepts by mining data and displaying the data in interactive and visual appropriate ways for easy consumption by a user.

Module Assessment Strategies

Students will be assessed 50/50 by continuous assessment and a final exam. The final exam with examine the core concepts of the subject. The continuous assessment will consist of ongoing work into gathering and sorting data from multiple sources and then a final project will be to interpret this data into a visual representation.

Repeat Assessments

Autumn written exam 

Indicative Syllabus

Utilise data preparation techniques to gather and organise different sources of data.

  • Brief look at using APIs to pull data from sources
  • Import data from multiple data sources and formats (JSON/XML/CSV).

Obtain a basic understanding of data and be able to verify the correctness of the data.

  • Data cleaning techniques to ensure consistency, particularly when merging information from multiple sources. 
  • Work with regular expressions.

Define key principles of Data Visualisation

  • Define and Understand objectives
  • Discuss Topics such as Data diversity , Data Comparison
  • How to view Data Sceptically  

Describe common features of infographics

  • Discuss how infographics can be used to display data correctly
  • How can you display data so that it can be visually effective
  • Look at a number of Tools for viewing data visualisations such as PowerBI and Tableau

Generate visual representations of data to present summaries and potential solutions to
problems based on the data.

  • Work with basic visualisations such as static charts and plots.
  • Integrate visualisations with other content on a web page.
  • Visualise timelines and implement interactivity (such as zooming, selecting content).
     

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Visualisation Project Project Group Project 30 % Week 12 3
2 Gather Data from Multiple Sources Continuous Assessment Assignment 20 % OnGoing 1,2,5
             

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam Final Exam Closed Book Exam 50 % End of Semester 1,3,4
             
             

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
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Required Reading
2015 Data Visualization with JavaScript No Starch Press
ISBN 9781593276058 ISBN-13 1593276052

You've got data to communicate. But what kind of visualization do you choose, how do you build it, and how do you ensure that it's up to the demands of the Web? In Data Visualization with JavaScript, you'll learn how to use JavaScript, HTML, and CSS to build the most practical visualizations for your data. Step-by-step examples walk you through creating, integrating, and debugging different types of visualizations and will have you building basic visualizations, like bar, line, and scatter graphs, in no time. Then you'll move on to more advanced topics, including how to: Create tree maps, heat maps, network graphs, word clouds, and timelines Map geographic data, and build sparklines and composite charts Add interactivity and retrieve data with AJAX Manage data in the browser and build data-driven web applications Harness the power of the Flotr2, Flot, Chronoline.js, D3.js, Underscore.js, and Backbone.js libraries If you already know your way around building a web page but aren't quite sure how to build a good visualization, Data Visualization with JavaScript will help you get your feet wet without throwing you into the deep end. Before you know it, you'll be well on your way to creating simple, powerful data visualizations.

Recommended Reading
2012 Data Mining
ISBN 9380931913 ISBN-13 9789380931913

Mining of Data with Complex Structures explores nature of data with complex structure including sequences, trees and graphs. Readers will find a detailed description of the state-of-the-art of sequence mining, tree mining and graph mining, and more.

Recommended Reading
2018-12 Introduction to Data Mining Pearson Higher Education
ISBN 0273769227 ISBN-13 9780273769224

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organised into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

Module Resources