Data Technician

Apprenticeships

Level 3

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The Green Skills Hub

This Apprenticeship provides the skills and knowledge needed for employment as an Data Technician.

Qualification

Data Technician

Qualification Level

Level 3

End Point Assessment Centre

TBC

Instruction Language

English

Qualification Duration

24 months (this does not include EPA period)

Included in

Our Green Skills Pathway

Employer Funding & Incentives

Funding

As of the 18th March 2024 the government announced significant changes to apprenticeship funding, aimed at supporting small and medium-sized enterprises (SMEs) in their hiring of young apprentices. Prime Minister Rishi Sunak unveiled plans to eliminate SME co-investment payments for apprentices under the age of 22, alongside increasing the transfer limit of apprenticeship levy funds from 25% to 50%.

read the full article

Incentives

Employers could get £1,000 each for taking on an apprentice who is either:

aged 16 to 18 years old, or
aged 19 to 25 years old and has an education, health and care (EHCP) plan or has been in the care of their local authority

What you can use the payment for
The payment is different to apprenticeship levy funds, so you can spend it on anything to support your organisation’s costs. For example, on uniforms, your apprentice’s travel or their salary. You do not have to pay it back.

Included in the Green Skills Pathway…

As part of our commitment to the environment, we are proud to offer an exciting opportunity for our apprentices. With every apprenticeship, learners will receive full funding to study our level 2 sustainability and environmental awareness short course, completely free of charge! Discover more about this fantastic course by clicking here.

Who is this course suitable for?

Data Technicians source, format, and present data securely for analysis, communicating outcomes to the right audience and applying legal and ethical principles when handling data.

Behaviours

B1: Manage own time to meet deadlines and manage stakeholder expectations whether working independently or in a multidisciplinary team.

B2: Work independently and methodically.

B3: Support social inclusion in the workplace. For example consider the needs of the audience.

B4: Takes responsibility for acting sustainably in their role for example switching off lights and systems when not in use, reducing file size and attachments on emails, and recycling.

Course Costs

This course is funded by the UK Government. The value of this training is:

£13,000.00

Employer 5% contribution

£650

End Point Assessment Cost (Paid by Kiwi)

£TBC

Enquire The Green Skills Hub

Students saved up to 50% on their fees

96 Students saved up to 50% on their fees

Course content

OCCUPATIONAL BRIEF OF STANDARD

KNOWLEDGE

- K1: Types of data, for example, structured, unstructured, qualitative, quantitative, numeric, strings, compound data types.
  
  K2: Common sources of data, for example, internal, external, open data sets, public and private.
  
  K3: Data storage formats and their importance for analysis, for example, relational database tables, spreadsheets, bespoke digital applications, comma separated value lists, text documents, voice and video.
  
  K4: Data element formats and how their selection can impact precision, analysis and communication, for example, integers, floating point numbers and their precision, scientific notation, date formatting as strings.
  
  K5: How to access and extract data from already identified sources.
  
  K6: How to collate and format data in line with organisational standards.
  
  K7: Why it may be important to anonymise data, for example for privacy, security and regulatory compliance, or to eliminate potential for bias.
  
  K8: How to anonymise data, for example one-for-one replacement of names, addresses or telephone numbers with distinct new values, without changing data structure or relationships.
  
  K9: Management and presentation tools to visualise and review the characteristics of data. Examples include spreadsheets with tables and charts, dashboarding tools, custom tools for particular data types, systems or contexts.
  
  K10: Communication tools and technologies for collaborative working, including the ability to share data and findings of data reviews. Examples include dashboards, shared whiteboards, or presentation tools for video conferencing for face-to-face contexts or digital presentation displays.
  
  K11: Communication methods, formats and techniques to help audiences understand data findings and their implications, for example written, verbal, non-verbal, presentation, email, conversation, storytelling and active listening.
  
  K12: Roles within an organisation needing access to data or to understand data findings, and how these roles impact the amount of detail needed in data communications, for example, customer, manager, peer; technical and non-technical.
  
  K13: How to combine data from multiple sources. For example using look ups, copy and paste and visualisation tools or data blending tools on bespoke systems.
  
  K14: Understand the capabilities within data analysis, visualisation, and querying tools, for example, spreadsheets or database viewers or digital display screens on bespoke systems for use in answering questions, solving problems, and the potential to use automation for repeated data manipulation.
  
  K15: How to filter details, focusing on information relevant to the data tasks and purpose.
  
  K16: Basic statistical methods to extract relevant information from structured and unstructured data, for example, counting rows, calculating the mean and standard deviation of numeric fields, counting words in a document, listing the most common values, calculating percentage contributions or percentage differences between data items.
  
  K17: Common data quality issues that can arise for example misclassification, duplicate entries, spelling errors, obsolete data, compliance issues and misinterpretation or translation of meaning.
  
  K18: Methods of validating data and the importance of taking corrective action, for example checking the source of information, identification and standardisation of outliers, adjusting item counts or totals of values.
  
  K19: Legal and regulatory requirements surrounding the use of data for example GDPR, Data Protection Act, data security, intellectual property rights, data sharing, marketing consent, personal data definition, and sector specific standards.
  
  K20: The ethical use of data, including in relation to its use with Artificial Intelligence and other automated systems, and the potential impacts of unethical use of data on the organisation.
  
  K21: The value of data to an organisation, for example to understand behaviours, to assess stakeholder sentiment, to interpret inputs received, to identify trends, to improve decision making and efficiency, or to build strategic or tactical plans to address a current situation.
  
  K22: The significance of understanding cultural awareness, diversity and accessibility with respect to data sets.
  
  K23: The relationships between data, machine learning, Internet of Things (IoT), Artificial Intelligence (AI) and Generative AI. For example, the impact of data and any biases within it on training AI models, and the impact of AI on data volume, quality, security, privacy and ethical considerations.
  
  K24: Sustainable data practices for example organisational policies and procedures relating to environmental impact and sustainability, green data centres, and responsible data storage.
  
  K25: Principles and policies of equity, diversity and inclusion in the workplace and their impact on the organisation.
  
  K26: Understand when and how to apply the principles of prompt engineering to identify and research effective data transformation techniques to ensure data quality and integrity.

SKILLS

S1: Select and migrate data from already identified sources.

S2: Format and save datasets.

S3: Summarise, analyse and explain gathered data.

S4: Combine data sets from multiple sources and present in format appropriate to the task.

S5: Use tools and/or apply basic statistical methods to identify trends and patterns in data.

S6: Identify faults and cleanse data to improve data quality, for example identifying gaps, duplicate entries, outliers and unusual variances, including cross-checking across data elements or between data sources.

S7: Audit data results for maintenance of data quality, reviewing a data set once all sources are combined, to ensure accuracy, completeness, consistency and traceability from original data.

S8: Demonstrate the different ways of communicating meaning from data in line with audience requirements.

S9: Produce clear and consistent documentation of the data provided to others and of actions completed. Where appropriate or mandated by the working context, this documentation should use standard organisational templates.

S10: Store, manage and distribute data in compliance with organisational, national, sector specific standards and or legislation.

S11: Considers sustainability and ways to reduce impact. For example, using cloud storage, sharing links to files, avoid storing multiple versions of files, and reducing the use of physical handouts of documentation.

S12: Parse data against standard formats, and test and assess confidence in the data and its integrity.

S13: Operate collaboratively in a working context that accounts for, and takes advantage of, the roles, skills and activities of others, especially those interacting with the same data sets or working towards a common goal.

S14: Prioritise own activities within the context of the duties to be performed, taking account of any known or expected impact on others.

S15: Follows equity, diversity and inclusion policies in the organisation for a common goal.

S16: Demonstrate the ability to use different tools and methods to formulate and utilise effective prompts to research, apply, and evaluate data transformation techniques.

TESTIMONIALS

Kiwi Education is an fantastic training provider who really does put it’s students at the heart of their delivery. Our staff have felt supported throughout their apprenticeship

– False Flag Tattoo

Area: Professional Accounting Taxation Technician AAT Apprenticeship

TESTIMONIALS

Our staff enrolled on courses have not only improved within their roles but with the support from Kiwi Education they have excelled and progressed from one level to a higher level. We are thrilled with the service from Kiwi Education.

– Costa Coffee

Area: Team Leader Supervisor Apprenticeship

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