Module overview
Open data, pitched as the raw material of the new industrial revolution, holds great promise, but how do you exploit this new resource?
This course is specifically designed to give students a greater understanding on how to innovate with open data. This course introduces the tools, techniques and skills required to rapidly innovate using data and how to pitch these ideas to potential investors. The course balances technical and non technical content throughout allowing development in all skill areas required to make a career in rich applications using open data.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Main current debates within the discipline and theories informing these debates
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Apply appropriate validation, cleaning and transformation to use, reuse and combine a multitude of complex datasets
- Critically evaluate and apply suitable UX and human engagement factors to build a compelling rich application
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Pitching an innovative idea to industry leaders
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically evaluate a large range of Infographics and interaction techniques suitable for different tasks
- Identify innovation opportunities for open data
Syllabus
Technical content:
- Open Data formats (CSV, JSON, XML, RDF)
- Web technologies (HTML5, Javascript, JQuery)
- Validating and cleaning data (csvlint, jsonlint, open refine)
- Visualising data (D3.js)
Non-technical content:
- Defining open data, benefits and risks
- Infographics and interaction
- Innovation and opportunities analysis
- UX design
- Human engagement and addiction
- Pitching to investors
Learning and Teaching
| Type | Hours |
|---|---|
| Completion of assessment task | 62 |
| Follow-up work | 12 |
| Wider reading or practice | 40 |
| Preparation for scheduled sessions | 12 |
| Lecture | 24 |
| Total study time | 150 |
Resources & Reading list
Textbooks
The Visual Display of Quantitative Information.
Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation.
Semantic Web Programming.
JavaScript & JQuery: Interactive Front-end Web Development.
HTML5 Foundations.
Interactive Data Visualization for the Web.
Information Graphics.
Information Visualization: Perception for Design.
Managing Technology Entrepreneurship and Innovation.
Contemporary Intellectual Property: Law and Policy.
Open Business Models: How To Thrive In The New Innovation Landscape.
HTML & CSS: Design and Build Web Sites.
Best practices in data cleaning: a complete guide to everything you need to do before and after collecting your data.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Continuous Assessment | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat Information
Repeat type: Internal & External