The aims of this module are to teach you how design and create graphics to represent data. In particular, to:

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1 MODULE SPECIFICATION UNDERGRADUATE PROGRAMMES KEY FACTS Module name Data Visualization Module code IN3030 School Mathematics, Computer Science and Engineering Department or equivalent Department of Computing UK credits 15 ECTS 7.5 Level 6 Delivery location (partnership programmes only) MODULE SUMMARY Module outline and aims Using graphics to explore and identify patterns in data is an approach increasingly adopted in modern data analysis and communication. Going well beyond traditional ideas of charts and graphs, data visualization is used in a range of disciplines from journalism, analytics, systems analysis, policy making, health sciences to graphic design and visual arts. Yet the lack of tools for creating data visualization and lack of people with skills to use them means that few are in a position to produce data visualizations themselves. This module is therefore designed to equip you with the technical and design skills to create and use data visualization applications for the workplace and research environments. The aims of this module are to teach you how design and create graphics to represent data. In particular, to: deliver a practical 'hands-on' module that allows you to build your own data visualization applications on a weekly basis; identify principles of good information visualization design by building, using and evaluating applications that you and others have created; provide structured guidelines for the data visualization workflow: acquisition, parsing, filtering, mining, representation, refining and interacting. Content outline Session 1: Session 2: Session 3: Building data visualization applications Using data in visualization Representing data with colour

2 Session 4: Session 5: Acquiring and parsing data Building interaction Review Session: Evaluating good visualization design and programming workshop Session 6: Session 7: Session 8: Session 9: Session 10: Representing data with text and symbolization Laying out data Transforming data Representing networks Design principles for effective data visualization WHAT WILL I BE EXPECTED TO ACHIEVE? On successful completion of this module, you will be expected to be able to: Knowledge and understanding: - Apply design principles for effective data visualization - Understand and apply theories of colour, layout and symbolization in data visualization - Find sources of data and know how to parse them for data visualization Skills: - Program graphical applications in the Processing language - Explore, identify and communicate patterns in data using visualization - Structure a data visualization task into Fry s seven-stage workflow Values and attitudes: - Adopt a constructively critical approach to evaluating data visualization - Be motivated to produce aesthetically pleasing data visualization HOW WILL I LEARN? There will be 10 weekly sessions with each session material available on the Moodle learning environment. You will be encouraged to read the materials in advance of each face-to-face session so that in the classroom, we can discuss the issues raised. We will be using the system called Processing (processing.org) to develop applications. This is a programming environment designed specifically for the quick generation of graphical applications. It is based around the Java language, but is designed to make programming much easier, especially for those who have never programmed before. Each week there will be a supervised lab session where you can practice building and using your data visualization applications with support from members of staff. Every week you will build an example data visualization application. You will get an opportunity to use this and other applications to do some real data exploration and analysis. The lessons learned from this process will be used to improve and build

3 more sophisticated applications in the following sessions. Because Processing is freely available and runs on most operating systems, you are encouraged to install it on your own computer so that you can work with it from home. You can use the online discussion forum to share ideas and give/receive support as you learn. Teaching pattern: Teaching component Exercise/assessme nt completion Online discussion Lecture and discussion sessions Practical classes Teachin g type Contact hours (schedule d) Selfdirected study hours (independen t) Placeme nt hours Lecture Threaded discussio n Lecture Practical classes and workshop s Total studen t learnin g hours Totals: WHAT TYPES OF ASSESSMENT AND FEEDBACK CAN I EXPECT? Assessments There will be a single piece of coursework that will comprise a data visualization application you have built along with evidence that you have used it to uncover patterns in the data it uses. The assessment will also require you to provide evidence that you have incorporated good practice and guidelines into the design and use of your application. On successful completion of the assessment, you will have: identified a research question about a real dataset that can be answered with data visualization; built a working Processing sketch that supports visualization of a real dataset; applied good practice in the design of colour, layout, symbolisation and

4 interaction in your visualization; provided a justification of your design decisions; provided some insight into the data you have visualized. Assessment pattern: Assessment component Assessment type Weighting Minimum qualifying mark Coursework Written N/A Reassessment Task Coursework N/A Assessment criteria You will be awarded marks for the following: Pass/Fail? identifying (a) research question(s) that can be answered effectively through data visualization; building a working Processing sketch that provides some insight into the data you are representing and the research questions(s) you are answering; demonstrating that you have considered good practice in the design of your data visualization; providing insight into the data demonstrating that visualization has provided this insight. Feedback on assessment There will be weekly non-assessed exercises with feedback in class from staff as well as opportunities to get feedback via online discussion on Moodle. The assessed coursework will be marked with written and oral feedback supplied via Moodle within four weeks of submission. Assessment Regulations The Pass mark for the module is 40%. Any minimum qualifying marks for specific assessments are listed in the table above. The weighting of the different components can also be found above. The Programme Specification contains information on what happens if you fail an assessment component or the module. INDICATIVE READING LIST Fry, B. (2008) Visualizing Data O'Reilly, ISBN:

5 Processing (2012) Reas, C. and Fry, B. (2010) Getting Started with Processing O'Reilly, ISBN: Shiffman, D. (2008) Learning Processing Morgan Kaufmann, ISBN: Version: 3.0 Version date: July 2015 For use from: Appendix: see for the full list of JACS codes and descriptions CODES HESA Code Description Price Group 121 IT, Systems Sciences and Computer Software Engineering C JACS Code Description Percentage (%) I200 The study, design or application of computers systems which capture, process and transmit information. 100