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Apprenticeship training course

Data analyst (level 4)

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Information about Data analyst (level 4)

Collect, organise and study data to provide business insight.

Knowledge, skills and behaviours
View knowledge, skills and behaviours

Knowledge

  • current relevant legislation and its application to the safe use of data
  • organisational data and information security standards, policies and procedures relevant to data management activities
  • principles of the data life cycle and the steps involved in carrying out routine data analysis tasks
  • principles of data, including open and public data, administrative data, and research data
  • the differences between structured and unstructured data
  • the fundamentals of data structures, database system design, implementation and maintenance
  • principles of user experience and domain context for data analytics
  • quality risks inherent in data and how to mitigate or resolve these
  • principal approaches to defining customer requirements for data analysis
  • approaches to combining data from different sources
  • approaches to organisational tools and methods for data analysis
  • organisational data architecture
  • principles of statistics for analysing datasets
  • the principles of descriptive, predictive and prescriptive analytics
  • the ethical aspects associated with the use and collation of data

Skills

  • Use data systems securely to meet requirements and in line with organisational procedures and legislation including principles of Privacy by Design
  • implement the stages of the data analysis lifecycle
  • apply principles of data classification within data analysis activity
  • analyse data sets taking account of different data structures and database designs
  • assess the impact on user experience and domain context on data analysis activity
  • identify and escalate quality risks in data analysis with suggested mitigation or resolutions as appropriate
  • undertake customer requirements analysis and implement findings in data analytics planning and outputs
  • identify data sources and the risks and challenges to combination within data analysis activity
  • apply organizational architecture requirements to data analysis activities
  • apply statistical methodologies to data analysis tasks
  • apply predictive analytics in the collation and use of data
  • collaborate and communicate with a range of internal and external stakeholders using appropriate styles and behaviours to suit the audience
  • use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
  • collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs
  • select and apply the most appropriate data tools to achieve the optimum outcome

Behaviours

  • maintain a productive, professional and secure working environment
  • show initiative, being resourceful when faced with a problem and taking responsibility for solving problems within their own remit
  • work independently and collaboratively
  • logical and analytical
  • identify issues quickly, investigating and solving complex problems and applying appropriate solutions. Ensures the true root cause of any problem is found and a solution is identified which prevents recurrence.
  • resilient - viewing obstacles as challenges and learning from failure.
  • adaptable to changing contexts within the scope of a project, direction of the organisation or Data Analyst role.
Apprenticeship category (sector)
Digital
Qualification level
4
Equal to higher national certificate (HNC)
Course duration
24 months
Maximum funding
£15,000
Maximum government funding for
apprenticeship training and assessment costs.
Job titles include
  • Data Analyst
  • Departmental Data Analyst
  • Problem Analyst
  • Junior Analyst
  • Marketing Data Analyst
  • Energy Data Analyst

View more information about Data analyst (level 4) from the Institute for Apprenticeships and Technical Education.