Synoptic Data Science Assessment 1
| Code | School | Level | Credits | Semesters |
| ZDAT1004 | Computer Science | 1 | 20 | Summer UK |
- Code
- ZDAT1004
- School
- Computer Science
- Level
- 1
- Credits
- 20
- Semesters
- Summer UK
Summary
This assessment is intended to help learners remember consolidated knowledge from their first year of learning. It will be delivered in two parts:
1) A multiple-choice knowledge test intended to mirror the knowledge test in the End Point Assessment
2) A reflective piece on the learner’s progress towards the KSBs as part of a demonstration of their approach to their own professional development, and to foster an ongoing positive attitude to their development beyond their apprenticeship journey.
While this assessment should be considered as “Assessment of Learning,” it is also intended as an instrument for learning to clearly signpost to learners that they are working towards an End Point Assessment, and that the knowledge, skills and behaviours should be developed in an integrated fashion, and that data science is not just a series of siloed domains. Questions for this assessment may require learners to draw from their on-the-job experience as well as the academic programme.
Target Students
Only available to those studying towards the Data Scientist Degree apprenticeship programme
Assessment
- 80% Assignment: Reflective piece of work.
- 20% Knowledge test
Assessed by end of designated period
Learning Outcomes
Understanding of the context and history of Data Science
A high-level understanding of ethics and professionalism especially with respect to the use of data.
An understanding of the nature of data and the different types of data
Understanding of the purpose of Data Science and the professional Data Scientist
Can apply a wide range of mathematical and statistical techniques to solve data science problems.
Can assess and make progress towards achieving the Data Scientist Apprenticeship KSBs
KSBs
K1. The context of Data Science and the Data Science community in relation to computer science, statistics and software engineering. How differing schools of thought in these disciplines have driven new approaches to data systems.
B6. A commitment to keeping up to date with current thinking and maintaining personal development. Including collaborating with the data science community.