This work demonstrates the following objectives:
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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.)
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How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:
- Data processing and storage, including on-premise and cloud technologies.
- Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.
- Data-driven decision making and the good use of evidence and analytics in making choices and decisions.
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How to design, implement and optimise analytical algorithms – as prototypes and at production scale – using:
- Statistical and mathematical models and methods.
- Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.
- Applications such as computer vision and Natural Language Processing.
- An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
- Development standards, including programming practice, testing, source control.
- 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.
- Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, profile, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.
- 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.
- 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.
- 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.
- An inquisitive approach: the curiosity to explore new questions, opportunities, data, and techniques; tenacity to improve methods and maximise insights; and relentless creativity in their approach to solutions.
- Consideration of problems in the context of organisation goals
- A commitment to keeping up to date with current thinking and maintaining personal development. Including collaborating with the data science community
- 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
- 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
- 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.
- Identify and reflect upon the key Knowledge, Skills and Behaviours (KSBs) that you have developed in relation to the degree apprenticeship standard.
- Monitor and build an evidence-base of the KSBs appropriate to the stage of your apprenticeship completion.