어소시에이트 데이터 사이언티스트

You can:

• apply analytical methods including exploratory data analysis and statistical testing to a specific data set, to reach accurate and reliable conclusions
• understand and use different performance and accuracy metrics for model validation in data science projects, hypothesis testing and information retrieval
• compare selected applied mathematics and statistical methods and identify their differences
• access and use the statistical and scientific tools available within the organisation

You can:

• identify the appropriate engineering support to design and provide products for the organisation
• recognise the processes and tools for cleansing and preparing data, and put reusable processes and checks in place
• use different architectures, including cloud and on-premise
• use data manipulation and transformation tools

You can:

• apply fundamental principles of data ethics and privacy in your work under supervision
• share data ethics and privacy risks through appropriate channels

You can:

• adopt an inquisitive and curious approach to data
• seek out and research new data science techniques to support learning
• ask questions to improve your knowledge and learn about data science norms
• see possibilities for improvements and innovation

You can:

• show an understanding of the organisation and the benefits of data science
• collaborate to help identify user needs and develop and deliver data science products
• communicate effectively and present analysis and visualisations tailored to your audience

You can:

• manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
• confidently talk about the benefits of data science approaches to existing and potential customers
• demonstrate a good understanding of key data science techniques, such as machine learning, and you can use them to build data science solutions, including reports, models and dashboards

You can:

• write and test scripts and create basic models in one or more languages
• collaborate on shared codebases, using a variety of methodologies

You can:

• show an awareness of the differences between delivery methods, such as Agile and waterfall, and can propose the most appropriate method to deliver each product
• manage your contribution to tasks to fit in with the work of your wider team

할 수 있는 일:

• 보건·돌봄 시스템의 주요 부분과 조직이 지원하는 서비스를 설명한다
• 자신의 업무에서 환자 안전과 비밀 유지가 중요한 이유를 설명한다

할 수 있는 일:

• 개인정보 보호 원칙을 자신의 업무에 적용한다
• 개인정보 영향평가에 기여한다
• 정보 요청과 기록을 올바르게 처리한다

할 수 있는 일:

• 가명처리된 데이터로 작업하고, 결과물을 공개하기 전에 공개 점검을 적용한다
• 연결되었거나 상세한 데이터로 환자를 식별할 수 있는 경우를 알아본다

할 수 있는 일:

• 편향, 드리프트, 모델 출력에 대한 과도한 의존 등을 통해 모델이 환자에게 어떻게 해를 끼칠 수 있는지 설명한다
• 모델을 사용할 집단에서 검증해야 하는 이유를 설명한다

할 수 있는 일:

• 임상 용어 체계와 분류의 차이를 설명한다
• SNOMED CT와 ICD 같은 일반적인 용어 체계를 알아본다