Synoptic Data Science Assessment 2
| Code | School | Level | Credits | Semesters |
| ZDAT2004 | Computer Science | 2 | 20 | Full Year UK |
- Code
- ZDAT2004
- School
- Computer Science
- Level
- 2
- Credits
- 20
- Semesters
- Full Year UK
Summary
This assessment is intended to help learners remember consolidated knowledge from their second 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.
Target Students
Only available to those studying towards the Data Scientist Degree apprenticeship programme
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
Assessment
- 20% Exam 1: Knowledge Test
- 80% Coursework 1: Reflective piece of work
Assessed by end of designated period
Learning Outcomes
Understands how data science as a field and a community has developed and functions in relation to other disciplines.
Understands the importance Ethics and Professionalism in relation to data science.
Demonstrates progress towards achieving all the KSBs for the Data Scientist Apprenticeship
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.
K2. How Data Science operates within the context of data governance, data security, and communications. How Data Science can be applied to improve an organisation’s processes, operations and outputs. How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Science work, and the impact of international regulations (including the General Data Protection Regulation.)
K3. How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:
1. Data processing and storage, including on-premise and cloud technologies.
2. Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.
3. Data-driven decision making and the good use of evidence and analytics in making choices and decisions.
K4. How to design, implement and optimise analytical algorithms – as prototypes and at production scale– using:
1. Statistical and mathematical models and methods.
2. Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.
3. Applications such as computer vision and Natural Language Processing.
4. An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
5. Development standards, including programming practice, testing, source control.
S1. Identify and clarify problems an organisation faces, and reformulate them into Data Science problems. Devise solutions and make decisions in context by seeking feedback from stakeholders. Apply scientific methods through experiment design, measurement, hypothesis testing and delivery of results. Collaborate with colleagues to gather requirements.
S2. Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, prole, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.
S3. Identify and use an appropriate range of programming languages and tools for data manipulation, analysis, visualisation, and system integration. Select appropriate data structures and algorithms for the problem. Develop reproducible analysis and robust code, working in accordance with software development standards, including security, accessibility, code quality and version control.
S4. Use analysis and models to inform and improve organisational outcomes, building models and validating results with statistical testing: perform statistical analysis, correlation vs causation, feature selection and engineering, machine learning, optimisation, and simulations, using the appropriate techniques for the problem.
S5. Implement data solutions, using relevant software engineering architectures and design patterns. Evaluate Cloud vs. on-premise deployment. Determine the implicit and explicit value of data. Assess value for money and Return on Investment. Scale a system up/out. Evaluate emerging trends and new approaches. Compare the pros and cons of software applications and techniques.
S6. Find, present, communicate and disseminate outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Use the best medium for each audience, such as technical writing, reporting and dashboards. Visualise data to tell compelling and actionable narratives. Make recommendations to decision makers to contribute towards the achievement of organisation goals.
S7. Develop and maintain collaborative relationships at strategic and operational levels, using methods of organisational empathy (human, organisation and technical) and build relationships through active listening and trust development.
S8. Use project delivery techniques and tools appropriate to their Data Science project and organisation. Plan, organise and manage resources to successfully run a small Data Science project, achieve organisational goals and enable effective change.
B2. Empathy and positive engagement to enable working and collaborating in multi-disciplinary teams, championing and highlighting ethics and diversity in data work.
B3. Adaptability and dynamism when responding to varied tasks and organisational timescales, and pragmatism in the face of real-world scenarios.
B4. Consideration of problems in the context of organisation goals.
B5. An impartial, scientific, hypothesis-driven approach to work, rigorous data analysis methods, and integrity in presenting data and conclusions in a truthful and appropriate manner.
B6. A commitment to keeping up to date with current thinking and maintaining personal development. Including collaborating with the data science community.