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

Employ appropriate techniques and tools to process and analyse data.
A2

Integrate data science principles, methods, techniques and tools covered in this unit to plan and execute a data science project.
K1

Interpret the principles of modern data science as well as data science lifecycle.
K2

Differentiate between the most common forms of data types and representations.
K3

Critique and apply a core collection of elementary techniques for data preparation, processing, management, exploration, and visualisation.
K4

Examine a core collection of methods and algorithms for data analysis and mining.
S1

Demonstrate competent skills in using data science technology for solving complex problems at an appropriate level of difficulty.
S2

Contrast and use data science software and tools.
S3

Implement any chosen data science solution and communicate the results effectively.
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

Employ appropriate techniques and tools to process and analyse data.
A2

Integrate data science principles, methods, techniques and tools covered in this unit to plan and execute a data science project.
K1

Interpret the principles of modern data science as well as data science lifecycle.
K2

Differentiate between the most common forms of data types and representations.
K3

Critique and apply a core collection of elementary techniques for data preparation, processing, management, exploration, and visualisation.
K4

Examine a core collection of methods and algorithms for data analysis and mining.
S1

Demonstrate competent skills in using data science technology for solving complex problems at an appropriate level of difficulty.
S2

Contrast and use data science software and tools.
S3

Implement any chosen data science solution and communicate the results effectively.