During Your Programme
These web pages aim to provide a handy access point for information that you will need during your Junior Data Analyst apprenticeship programme.
On this page:
Your Tutors & Coaches
Explaining the Roles
There are three key roles in the team which will support your apprenticeship. Some members of staff cover more than one of these.
Your coach will meet with you each month to help develop and evidence your skills and behaviours within the workplace. Your line manager will attend part of this session. They will help you gather evidence to demonstrate your overall competency.
Your learner progress adviser will help provide additional support where needed and monitor your progress. You will meet with them, and your line manager, every three months.
Your E-Portfolio: OneFile
We use One File as our e-portfolio system. This is where your assignments will be set, you will submit your work and you will log your off-the-job training.
Your regular reviews will be stored on here as will some resources for your course. You will complete a learning journal, be able to monitor your progress towards your Knowledge, Skills and Behaviours and refer to your workplace training plan.
Our training video will demonstrate all the key features of OneFile you need to know. Ongoing support will also be provided when it is needed.
Knowledge, Skills and Behaviours
During your Junior Data Analyst apprenticeship, you will learn to demonstrate the following knowledge, skills and behaviours.
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K1: 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.
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K2: How to access and extract data from a range of already identified sources
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K3: How to collate and format data in line with industry standards
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K4: 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
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K5: 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
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K6: The value of data to the business How to undertake blending of data from multiple sources
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K7: 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
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K8: How to filter details, focusing on information relevant to the data project
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K9: Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data
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K10: 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
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K11: Different methods of validating data and the importance of taking corrective action
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K12: Communicating the results through basic narrative
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K13: 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
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K14: 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
K15: The role of data in the context of 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, IoT, customer centricity
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K16: Different learning techniques, learning techniques and the breadth and sources of knowledge
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S1: Source and migrate data from already identified different sources
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S2: Collect, format and save datasets
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S3: Summarise and explain gathered data
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S4: Blend data sets from multiple sources and present in format appropriate to the task
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S5: Manipulate and link different data sets as required
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S6: Use tools and techniques to identify trends and patterns in data
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S7: Apply basic statistical methods and algorithms to identify trends and patterns in data
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S8: Apply cross checking techniques for identifying faults and data results for data project requirements
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S9: Audit data results
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S10: Demonstrate the different ways of communicating meaning from data in line with audience requirements
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S11: Produce clear and consistent technical documentation using standard organisational templates
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S12: Store, manage and distribute in compliance with data security standards and legislation
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S13: Explain data and results to different audiences in a way that aids understanding.
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S14: Review own development needs
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S15: Keep up to date with developments in technologies, trends and innovation using a range of sources
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S16: 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.
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S17: Operate as part of a multi-functional team
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S18: Prioritise within the context of a project
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B1: Manage own time to meet deadlines and manage stakeholder expectations
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B2: Work independently and take responsibility
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B3: Use own initiative
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B4: A thorough and organised approach
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B5: Work with a range of internal and external customers
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B6: Value difference and be sensitive to the needs of others
Completing Your Apprenticeship
End Point Assessment (EPA) is the process at the end of your programme for you to demonstrate to a third party assessor that you have acquired the knowledge, skills and behaviours set out in the apprenticeship standard. This will only happen once yourself, your line manager and Skills Coach have agreed that you are ready.
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