Summary of module for applicants:

This module aims to develop a critical understanding of the world of data and Data Science from an ‘ecological’ perspective. This will focus on an understanding the environment of production, dissemination, harvesting and use of data in the data value chain as well as the development of niche areas from a perspective of evolution, competition, life cycle, cross-fertilisation and the niche space. This module focuses on many aspects of working in an Industry 4.0 economy.

Main topics of study:

• Understanding data from an ecological perspective
• Elements of the data value chain: from data acquisition, data management, data analysis to data visualisation and decision support
• Data quality and metadata issues
• Technological impacts
• Organisational impacts
• Data and society, big data, open data and transparency agendas
• Data and the environment; carbon footprint
• Legal and security issues of data, personal data and privacy
• Business models
• Location and ecology of tech hubs and start-ups in the UK

Learning Outcomes for the module

• Digital Proficiency - Code = (DP)
• Industry Connections - Code = (IC)
• Emotional Intelligence Development - Code = (EID)
• Social Intelligence Development - Code = (SID)
• Physical Intelligence Development - Code = (PID)
• Cultural Intelligence Development - Code = (CID)
• Community Connections - Code = (CC)
• UEL Give-Back - Code = (UGB)

At the end of this module, students will be able to:

Knowledge

1 Demonstrate a high level of understanding of the the data value chain and its components (IC)

2 Demonstrate a high level of understanding of the nature, key issues and dependencies within the data and start-up business ecology (IC)

Thinking skills

3 Critically evaluate data application areas from a data ecological perspective (IC, EID)

4 Demonstrate a high level of understanding of the complex symbiosis of data with government, business and society (IC, EID)

Subject-based practical skills

5 Specify the value chain and map out the ecological components and interactions of data application areas (IC)

6 Evaluate the quality of data sets and create metadata (DP)

7 Online data collection and exploration (DP, IC)

Skills for life and work (general skills)

8 Critically evaluate published research and reporting of data issues (EID)

9 Apply problem solving (DP, IC, PID)