Learning outcome
1.1

Strategy and planning
1.2

Security and privacy
1.3

Governance, risk and compliance
1.4

Advice and guidance
2.5

Change implementation
2.6

Change analysis
2.7

Change planning
3.8

Systems development
3.9

Data and analytics
3.10

User experience
3.11

Content management
3.12

Computational science
4.13

Technology management
4.14

Service management
4.15

Security services
5.16

People management
5.17

Skills management
6.18

Stakeholder management
6.19

Sales and marketing
A1

Communicate the stages, outcomes, and implications of the data analytics process, with reference to established academic literature.
A2

Apply big data analytics technology to a real-world or simulated dataset.
K1

Describe the different types of data (e.g. structured, semi-structured, unstructured) and their sources (e.g. sensors, medical, business, social data)
K2

Discuss the stages of the big data analytics lifecycle.
K3

Outline the main tools and techniques in this area.
K4

Explain the importance of big data governance.
S1

Create and deliver reports using an analytical tool(s) on a real-world or simulated dataset.
S2

Research, explore and explain the current range of big data and analytics solutions and emerging trends and future issues.
S3

Explain contemporary IT industry practices/presentations relevant to Big Data and Analytics, and relate them to professional standards and your own career aspirations
Learning outcome
1.1

ICT Fundamentals

1.2

ICT Infrastructure

1.3

Information & Data Science and Engineering

1.4

Computational Science and Engineering

1.5

Application Systems

1.6

Cyber Security

1.7

ICT Projects

1.8

ICT Management and Governance

2.1

Professional ICT Ethics
2.2

Impacts of ICT

2.3

Working Individually and in ICT development teams

2.4

Professional Communication

2.5

The Professional ICT Practitioner

A1

Communicate the stages, outcomes, and implications of the data analytics process, with reference to established academic literature.
A2

Apply big data analytics technology to a real-world or simulated dataset.
K1

Describe the different types of data (e.g. structured, semi-structured, unstructured) and their sources (e.g. sensors, medical, business, social data)
K2

Discuss the stages of the big data analytics lifecycle.
K3

Outline the main tools and techniques in this area.
K4

Explain the importance of big data governance.
S1

Create and deliver reports using an analytical tool(s) on a real-world or simulated dataset.
S2

Research, explore and explain the current range of big data and analytics solutions and emerging trends and future issues.
S3

Explain contemporary IT industry practices/presentations relevant to Big Data and Analytics, and relate them to professional standards and your own career aspirations