Data Technician Apprenticeship Level 3

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.

K2: How to access and extract data from a range of already identified sources

K3: How to collate and format data in line with industry standards

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

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

K6: The value of data to the business How to undertake blending of data from multiple sources

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

K8: How to filter details, focusing on information relevant to the data project

K9: Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data

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

K11: Different methods of validating data and the importance of taking corrective action

K12: Communicating the results through basic narrative

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

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

K16: Different learning techniques, learning techniques and the breadth and sources of knowledge

Skills

S1: Source and migrate data from already identified different sources

S2: Collect, format and save datasets

S3: Summarise and explain gathered data

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

S5: Manipulate and link different data sets as required

S6: Use tools and techniques to identify trends and patterns in data

S7: Apply basic statistical methods and algorithms to identify trends and patterns in data

S8: Apply cross checking techniques for identifying faults and data results for data project requirements

S9: Audit data results

S10: Demonstrate the different ways of communicating meaning from data in line with audience requirements

S11: Produce clear and consistent technical documentation using standard organisational templates

S12: Store, manage and distribute in compliance with data security standards and legislation

S13: Explain data and results to different audiences in a way that aids understanding.

S14: Review own development needs

S15: Keep up to date with developments in technologies, trends and innovation using a range of sources

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.

S17: Operate as part of a multi-functional team

S18: Prioritise within the context of a project

Candidates will likely require five GCSEs at Grades 9-4, (Formerly Grades A*-D) - especially English, Maths and a Science or Technology subject; a relevant Level 3 qualification or other relevant qualifications and experience.

Every employer is different and therefore the entry requirements can vary. As a guide, you ideally need GCSE Grade 5 (formerly Grades B or C) in English and Maths. However, we will work with you and provide additional support to help you achieve their requirements prior to your apprenticeship.

Individual employers will set the additional selection criteria for their apprenticeships.

As well as containing information about training and assessment, all apprenticeship standards must contain an End-Point Assessment (EPA). An independent organisation must be involved in the EPA of each apprentice so that all apprentices following the same standard are assessed consistently.

The final, End-Point Assessment is completed in the last few months of the Apprenticeship.

It is based on:
• A portfolio – produced towards the end of the apprenticeship, containing evidence from real work projects which have been completed during the Apprenticeship.
• Two knowledge tests, one core test and one specialist knowledge test.
• An employer reference - as to the Apprentice’s suitability and preparedness to go through the final independent assessment.
• A Case Study presentation and interview with an assessor - exploring what has been produced in the portfolio and the project as well as looking at how it has been produced
• An independent assessor will assess each element of the End-Point Assessment and will then decide whether to award successful Apprentices with a pass, a merit or a distinction.

Data Analyst, Data Technician, Information Analyst

Typical job duties include:

Duty 1 Source data from a collection of already identified trusted sources in a secure manner

Duty 2 Collate and format data to facilitate processing and presentation for review and further advanced analysis by others

Duty 3 Present data for review and analysis by others, using required medium for example tables, charts and graphs

Duty 4 Blend data by combining data from various sources and formats to explore its relevance for the business needs

Duty 5 Analyse simple and complex structured and unstructured data to support business outcomes using basic statistical methods to analyse the data.

Duty 6 Validate results of analysis using various techniques, e.g cross checking, to identify faults in data results and to ensure data quality

Duty 7 Communicate results verbally, through reports and technical documentation and tailoring the message for the audience

Duty 8 Store, manage and share data securely in a compliant manner

Duty 9 Collaborate with people both internally and externally at all levels with a view to creating value from data

Duty 10 Practise continuous self learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development

Awarding Body:
British Computer Society