Delivery is flexible to suit the individual apprentice and their employer. The apprentice will have a minimum of one tutorial per month either in person within the workplace or remotely via an online meeting. Group sessions may be available on some programmes.

• The apprentice must achieve Level 2 English and Maths prior to completing the end point assessment (If the apprentice doesn’t already hold Level 2 English and Maths, this content will be blended into the study programme if it is required).
• Apprentices must be employed to study for this qualification. Please see our Apprenticeship vacancies.
• The apprentice must be at least 16 years of age.

• Understand a range of different types of existing data. Common sources of data - internal, external, open data sets, public and private. Data formats and their importance for analysis. Data architecture - the framework against which data is stored and structured including on premises and cloud.
• How to access and extract data from a range of already identified sources.
• How to collate and format data in line with industry standards.
• Data formats and their importance for analysis management and presentation tools to visualise and review the characteristics of data communication tools and technologies for collaborative working.
• Communication methods, formats and techniques, including: written, verbal, non-verbal, presentation, email, conversation, audience and active listening Range of roles within an organisation, including: customer, manager, client, peer, technical and non-technical.
• The value of data to the business.
• How to undertake blending of data from multiple sources.
• Algorithms, and how they work using a step-by-step solution to a problem, or rules to follow to solve the problem and the potential to use automation.
• How to filter details, focusing on information relevant to the data project.
• Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data.
• The range of common data quality issues that can arise e.g., misclassification, duplicate entries, spelling errors, obsolete data, compliance issues and interpretation/ translation of meaning.
• Different methods of validating data and the importance of taking corrective action.
• Communicating the results through basic narrative.
• Legal and regulatory requirements e.g., Data Protection, Data Security, Intellectual Property Rights (IPR), Data sharing, marketing consent, personal data definition. The ethical use of data.
• The significance of customer issues, problems, business value, brand awareness, cultural awareness/ diversity, accessibility, internal/ external audience, level of technical knowledge and profile in a business context.
• The role of data in the context of the digital world including the use of eternal trusted open data sets, how data underpins every digital interaction and connectedness across the digital landscape including applications, devises and customer centricity.
• Different learning techniques, learning techniques and the breadth and sources of knowledge.

• Source and migrate data from already identified different sources.
• Collect, format and save datasets.
• Summarise and explain gathered data.
• Blend data sets from multiple sources and present in format appropriate to the task.
• Manipulate and link different data sets as required.
• Use tools and techniques to identify trends and patterns in data.
• Apply basic statistical methods and algorithms to identify trends and patterns in data.
• Apply cross checking techniques for identifying faults and data results for data project requirements.
• Audit data results.
• Demonstrate the different ways of communicating meaning from data in line with audience requirements.
• Produce clear and consistent technical documentation using standard organisational templates.
• Store, manage and distribute in compliance with data security standards and legislation.
• Explain data and results to different audiences in a way that aids understanding.
• Review own development needs.
• Keep up-to-date with developments in technologies, trends and innovation using a range of sources.
• Clean data i.e., remove duplicates, typos, duplicate entries, out of date data, parse data (e.g., format telephone numbers according to a national standard) and test and assess confidence in the data and its integrity.
• Operate as part of a multi-functional team.
• Prioritise within the context of a project.

• Manage own time to meet deadlines and manage stakeholder expectations.
• Work independently and take responsibility.
• Use own initiative.
• Demonstrate a thorough and organised approach.
• Work with a range of internal and external customers.
• Value difference and be sensitive to the needs of others.

This apprenticeship does not feature any additional external qualifications.

Assessment is a mixture of practical demonstrations, creation of a portfolio of work, discussion and successful completion of an End Point Assessment.