Project Learning Outcomes

Question 1

4.1

Answer 1

Explain and apply basic techniques in Analysis, Algebra and Statistics to solve seen and unseen problems, and to present, evaluate and interpret qualitative and quantitative data in class or in the workplace.

Question 2

4.2

Answer 2

Analyse and construct data science-related arguments logically, carefully identifying assumptions, scope and limitations.

Question 3

4.3

Answer 3

Use appropriate resources and tools to identify and select appropriate sources of information in order to create logically structured introductory content e.g. for essays, bibliographies, reports, posters and presentations.

Question 4

4.4

Answer 4

Apply basic psychological and sociological principles of the science of communication and cognition to communicating data science content to specialist and non-specialist audiences using a range of media, and identify ethical challenges concerning data and communication.

Question 5

4.5

Answer 5

Individually and collaboratively programme in appropriate languages to solve problems in class or in the workplace, and select an appropriate set of software and collaborative editing tools to explain and visualise the analysis.

Question 6

4.6

Answer 6

Select an appropriate range of skills gained in different disciplinary contexts in order to address, solve and communicate interdisciplinary questions collaboratively in class or in the workplace.

Question 7

4.7

Answer 7

Identify and reflect upon the key Knowledge, Skills and Behaviours (KSBs) that you have developed in relation to the degree apprenticeship standard.

Question 8

4.8

Answer 8

Monitor and build an evidence-base of the KSBs appropriate to the stage of your apprenticeship completion.

Question 9

5.1

Answer 9

Explain and apply intermediate core techniques in mathematics, statistics and computer science to collect and manage data, to model real scenarios and to predict properties, future evolution and leverage points.

Question 10

5.2

Answer 10

Differentiate between different paradigms for data modelling and select appropriate ones based on the nature of the study system, clearly identifying assumptions and limitations of the approach.

Question 11

5.3

Answer 11

Manage data-driven research projects by identifying logical steps and milestones, and combining independent study with group work.

Question 12

5.4

Answer 12

Effectively communicate across multi-disciplinary interfaces connecting computer science, mathematical modelling, statistics, communication, non-specialists and management, e.g. with team members in class and in the workplace, managers or clients.

Question 13

5.5

Answer 13

Effectively programme using an appropriate choice of languages, software packages and programming libraries in order to solve problems in data handling, mathematical modelling and spatial visualisation.

Question 14

5.6

Answer 14

Integrate knowledge across mathematics, statistics, computer science and communication, also incorporating other people’s disciplinary expertise, in order to tackle interdisciplinary problems in class or real-work contexts.

Question 15

5.7

Answer 15

Identify, analyse and reflect upon the key Knowledge, Skills and Behaviours (KSBs) that you have developed in relation to the degree apprenticeship standard.

Question 16

5.8

Answer 16

Critically assess how the portfolio evidence base of your personal development activities relates to the KSBs appropriate to the stage your apprenticeship completion; identify gaps, and prioritise and plan personal development targets for successful
ongoing achievement.

Question 17

6.1

Answer 17

Explain, apply and critically evaluate advanced data science topics and concepts, e.g. in inference, information and visualisation.

Question 18

6.2

Answer 18

Critically evaluate inferential arguments from different sources and perform rigorous, robust and reproducible mathematical and computational analyses both in class and in the workplace.

Question 19

6.3

Answer 19

Conduct independent research on advanced topics, organising and managing your own time effectively, and using appropriate sources of information and support.

Question 20

6.4

Answer 20

Communicate advanced ideas and analyses in data science both in writing and orally to multi-disciplinary audiences.

Question 21

6.5

Answer 21

Identify or implement appropriate programming and software tools to analyse big data applications both in class and in the workplace.

Question 22

6.6

Answer 22

Apply and integrate your knowledge to make connections in a wider context in order to address multi-disciplinary challenges and to communicate your analysis to a range of stakeholders.

Question 23

6.7

Answer 23

Identify, critically evaluate, reflect upon and discuss the key Knowledge, Skills and Behaviours (KSBs) that you have developed in relation to the degree apprenticeship.

Question 24

6.8

Answer 24

Conduct a successful professional discussion to successfully evidence, and reflect critically upon, the occupational standard relevant to your apprenticeship through the application of KSBs in the work environment.