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Data Analyst Apprenticeship

Collect, organise and study data to provide business insight.

data analyst working

Data Analysts work in many different sectors across most industries. They try to help organisations understand their data and help them answer questions and solve problems.

They work with data at many different levels from gathering, inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names. In today’s world, data analysis plays a crucial role in making decisions more evidence-based and helping organisations operate more effectively.

They interact with internal or external clients. Internally they can work with many people within their organisation, at different levels. Externally, a data analyst may provide data analysis services to other organisations on behalf of their employer. They may be responsible for working within the data architecture of the company and ensuring that the data is handled in a compliant, safe and appropriately secure manner, understanding and adhering to company data policy and legislation.

  • Identify data sources to meet the organisation’s requirement, using evidence-based decision making to establish a rationale for inclusion and exclusion of various data sets and models.
  • Liaise with the client and colleagues from other areas of the organisation to establish reporting needs and deliver insightful and accurate information.
  • Collect, compile and, if needed, cleanse data, such as sales figures, Digital Twins etc. solving any problems that arise, to or from a range of internal and external systems.
  • Produce performance dashboards and reports in the Visualisation and Model Building Phase.
  • Support the organisation by maintaining and developing reports for analysis to aid with decisions, and adhering to organisational policy/legislation.
  • Produce a range of standard and non standard statistical and data analysis reports in the Model Building phase.
  • Identify, analyse, and interpret trends or patterns in data sets.
  • Draw conclusions and recommend an appropriate response, offer guidance or interpretation to aid understanding of the data.
  • Summarise and present the results of data analysis to a range of stakeholders, making recommendations.
  • Provide regular reports and analysis to different management or leadership teams, ensuring data is used and represented ethically in line with relevant legislation (e.g. GDPR which incorporates Privacy by Design).
  • Ensure data is appropriately stored and archived, in line with relevant legislation e.g. GDPR.
  • Practice continuous self learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development.

Data analysis is a fast-moving and changing environment, and data analysts need to continue to stay abreast of, and engaged with, changes and trends in the wider industry; including data languages, tools and software, and lessons learnt elsewhere.

Level 4 Data Analyst Apprenticeship is designed for those looking to further their career in data analysis or existing staff looking to develop their skills.

 Suitable Audience:

  • Newly Recruited Apprentices
  • Data Analyst
  • Junior Analyst
  • Departmental Data Analyst
  • Problem Analyst
  • Marketing Data Analyst

Core Module Content

  • Current relevant legislation and 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, 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.