COMP06248 2018 Data Preparation and Visualisation

General Details

Full Title
Data Preparation and Visualisation
Transcript Title
Data Preparation and Visualisa
Code
COMP06248
Attendance
N/A %
Subject Area
COMP - Computing
Department
COMP - Computing & Creative Practices
Level
06 - NFQ Level 6
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2018 - Full Academic Year 2018-19
End Term
9999 - The End of Time
Author(s)
Fran O'Regan, John Weir, Donny Hurley
Programme Membership
SG_KSMAR_C06 201800 Higher Certificate in Science in Computing in Smart Technologies SG_KSMAR_B07 201800 Bachelor of Science in Computing in Smart Technologies SG_KAPPL_C06 201800 Higher Certificate in Science in Computing in Application Design and User Experience SG_KAPPL_B07 201800 Bachelor of Arts in Computing in Application Design and User Experience SG_KAPPL_H08 201900 Bachelor of Arts (Honours) in Computing in Application Design and User Experience SG_KSMAR_H08 201900 Bachelor of Science (Honours) in Computing in Smart Technologies SG_KSMAR_C06 201900 Higher Certificate in Science in Computing in Smart Technologies
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.

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

2.

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

3.

Discuss and distinguish the methods of storing data/data warehousing for large data sets.

4.

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

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 continous 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 and/or project.

Indicative Syllabus

Utilise data mining 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.

Discuss and distinguish the methods of storing data/data warehousing for large data sets.

  • Data cleaning techniques to ensure consistency, particularly when merging information from multiple sources. 
  • Introduction to Data Warehousing and how it can be used for Data Analysis.

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 4
2 Gather Data from Multiple Sources Continuous Assessment Assignment 20 % OnGoing 1,2,3
             

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 1,2,3,4
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lectures 1 Weekly 1.00
Laboratory Practical Computer Laboratory Practical 2 Weekly 2.00
Independent Learning Not Specified Independent Learning 3 Weekly 3.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Recommended Reading
2015-03-26 Data Visualization with JavaScript Penguin Random House LLC (No Starch)
ISBN 1593276052 ISBN-13 9781593276058

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
2018-08-24 Introduction to Data Mining: Global Edition Pearson Education
ISBN 0273769227 ISBN-13 9780273769224
Recommended Reading
2011 Data Mining: Concepts and Techniques (EDN 3) by Jian Pei,Micheline Kamber,Jiawei TBS
ISBN 9380931913 ISBN-13 9789380931913

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Module Resources