The Data and AI School of London is an NCFE approved centre for this qualification (NCFE Account No. 11001657). Enrol below to begin your learning journey.
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Level 3 · NCFE
Enrolling now NCFE Approved Qualification number 610/4006/X

NCFE Level 3 Technical Occupational Entry for the Data Technician - Diploma

Develop the knowledge, skills and behaviours that meet the minimum requirements for entry into a data technician occupation.

Duration: Self-paced online study; typically one to two years

Level
Level 3
Qualification number
610/4006/X
Awarding organisation
NCFE
Regulated status
On the RQF, regulated by Ofqual
Delivery
100% online
Study pattern
Self-paced online study; typically one to two years
NCFE This qualification is awarded by NCFE and regulated by Ofqual
NCFE Account No. 11001657

Course overview

This Technical Occupational Entry qualification is designed to provide learners with the knowledge, skills and behaviours relevant to developing competence in data. It is aligned to the Data Technician occupational standard and gives employers reliable evidence of a learner's attainment against the minimum requirements for entry into the occupation.

Across its graded mandatory units, learners build the practical capabilities needed to work with data across a variety of sectors, preparing them for employment in a data-focused role.

It is aimed at learners seeking employment in data and progression into the digital workforce. The qualification is internally assessed and externally quality assured, and is graded to reflect the standard of a learner's achievement.

What you will study

The units below, their unit reference numbers and their guided learning hours are taken directly from the NCFE qualification specification.

Unit Title Unit reference Hours
01 Data fundamentals
4 learning outcomes
  1. Understand the value, types and sources of data
    • 1.1 The value of data to an organisation
    • 1.2 How a range of quantitative and qualitative data can be used to highlight and explain trends
    • 1.3 How common sources of data are used within an organisation (for example, internal, external, open datasets, public and private)
    • 1.4 How trusted external or third-party data is used to support an organisation’s data strategy
  2. Understand the use of data and how to extract data from a range of sources
    • 2.1 The purpose and use of data formats: > numeric > temporal > text > geospatial > media > logical > references
    • 2.2 The importance of selecting the most appropriate data suitable for analysis
    • 2.3 How to access, extract and migrate data from a range of sources
  3. Understand how data underpins digital interactions and how it is obtained through customer centric interactions
    • 3.1 The significance of data and how it underpins digital interactions and connections across the digital landscape (for example, transactional or booking data)
    • 3.2 How data can be obtained through customer centric interactions: > applications > devices > internet of things (IoT)
  4. Understand and be able to collect, collate and format data and save to meet requirements
    • 4.1 How to collate data from multiple sources to produce a dataset to meet requirements
    • 4.2 Collect data from a range of sources and migrate, format and save the new dataset
 A/651/1111 45
02 Data architecture and legislation
3 learning outcomes
  1. Understand data architecture
    • 1.1 The role of data architecture frameworks (for example, The Open Group Architecture Framework (TOGAF)) in supporting business strategy
    • 1.2 The function of data architecture frameworks in supporting an organisation’s data architecture strategy (for example, access, managed, shared)
    • 1.3 The types of data architecture (for example, warehouse mart, lake) and their different uses within an organisation,
    • 1.4 The characteristics of data architecture (for example, governance, compliance, security)
  2. Understand legal and regulatory requirements and store, manage and distribute data in compliance with standards and legislation
    • 2.1 The purpose and use of legislation and standards to support the use of data: > Data Protection Act (DPA) 2018 > Computer Misuse Act 1990 > Copyright, Designs and Patents Act 1988 > Payment Card Industry Data Security Standard (PCI DSS) ISO/IEC 27001
    • 2.2 The purpose and use of intellectual property rights (IPR) to support the use of data
    • 2.3 The purpose and use of the data sharing code of practice
    • 2.4 The concept of marketing consent and how this applies to data analysis
    • 2.5 How to define personally identifiable information (PII) and why it is important to protect this information
    • 2.6 The impact of non- compliance with legal and regulatory requirements on an organisation
    • 2.7 How to collect datasets in line with Data Standards Authority (DSA) recommendations (for example, transparency, accountability, fairness)
    • 2.8 The purpose of security controls and procedures to ensure data security (for example, encryption, resilience)
    • 2.9 Store, manage and distribute data in compliance with data security standards and legislation
  3. Understand the ethical use of data
    • 3.1 The purpose and use of the Data Ethics Framework to support the use of data: > transparency > accountability > fairness
    • 3.2 The ethical considerations when gathering, analysing and presenting data (for example, consent, contract, legal obligations)
 D/651/1112 45
03 Data cleansing
2 learning outcomes
  1. Understand common data quality issues, apply data cleansing measures and test and assess confidence and integrity in data
    • 1.1 The characteristics and impact of common data quality issues: > inconsistent data (for example, duplicate entries, out-of-date data) > human error (for example, spelling errors, introduction of bias) > compliance issues (for example, the Data Protection Act 2018)
    • 1.2 The application of data cleansing methods, including: > correction of typos > removal of duplicate entries > excluding out-of- date data > parse data > replacing null/missing values
    • 1.3 The importance of data quality in ensuring confidence and integrity: > usability > validity > reliability > repeatability > source of data (for example, primary or secondary data) > appropriateness to task based on bias identified within the dataset
    • 1.4 Apply appropriate data cleansing measures
    • 1.5 Test and assess confidence and integrity in the data
  2. Understand and apply cross-checking methods
    • 2.1 The application of cross- checking methods for validation and verification: > validation (for example, length, format, data type) > verification: >> double keying >> proofreading data
    • 2.2 The importance of taking corrective action when validating data
    • 2.3 Apply cross-checking methods to identify faults and data results to meet requirements
 F/651/1113 45
04 Blending and merging data
2 learning outcomes
  1. Understand how to filter data
    • 1.1 The importance of filtering data (for example, accuracy, reliability)
    • 1.2 How to filter data to meet project requirements
  2. Understand the value of blended data and manipulate, link and audit data
    • 2.1 The value of blended data (for example, deeper business insights)
    • 2.2 The application of blending and manipulation techniques: > data joining (for example, inner, full) > consolidation (for example, combining separate worksheets into one worksheet) > merging dataset (for example, combining files with the same structure into one dataset)
    • 2.3 Provide blended data from multiple sources in an appropriate format
    • 2.4 The importance of manipulating and linking different datasets
    • 2.5 Apply manipulation techniques to link different datasets and meet requirement
    • 2.6 Assess the integrity of blended and manipulated data results: > validity > scope > anomalies
 H/651/1114 54
05 Statistical analysis
1 learning outcome
  1. Understand and apply data modelling, statistical methods and algorithms
    • 1.1 The application of data modelling techniques to extract relevant data: > conceptual > logical > physical
    • 1.2 The application of statistical methods to normalise data and to identify trends and patterns: > standard deviation – measures the variance from the mean > linear regression – identifies relationship between data variables > clustering – used to group related data points within a dataset > time series modelling – identifies patterns over time (for example, weekly or monthly trends) > correlation – identifies a relationship between datasets
    • 1.3 The process of data normalisation to remove redundancy and improve integrity
    • 1.4 The features and function of algorithms to solve problems within data (for example, identifying patterns and trends, provides predictive analytics)
    • 1.5 Apply appropriate data modelling techniques and algorithms to identify trends and patterns in data
    • 1.6 Apply an appropriate statistical method to interpret trends and patterns in data in data.
 J/651/1115 54
06 Data visualisation
1 learning outcome
  1. Understand data management and visualisation tools and apply visualisation tools and techniques to communicate data
    • 1.1 The use of data management tools to govern, process, secure and store data
    • 1.2 The use of data visualisation tools to manage, summarise and display data (for example, Power BI, Microsoft Excel)
    • 1.3 The use of presentation tools to review and communicate data (for example, Microsoft PowerPoint, Canva)
    • 1.4 The application of visualisation techniques used to present data for specific audiences (for example, charts/graphs, tables, infographics)
    • 1.5 Apply a range of visualisation tools and techniques to identify trends and patterns in data and communicate results to meet technical and non-technical audience requirements
 K/651/1116 36
07 Presentation and communication of data
2 learning outcomes
  1. Understand and apply communication methods, formats and techniques appropriate for the use of data
    • 1.1 The application of data communication methods: > written (for example, business case, report) > verbal (for example, public speaking, conversation) > non-verbal (for example, tone of voice, body language, active listening)
    • 1.2 The application of a range of formats used in the communication of data (for example, presentation, emails, virtual/augmented reality)
    • 1.3 The application of communication techniques: > technical/non- technical (for example, complexity levels of language) > active listening > tailoring to audience > use of open questioning > reflection and review > storyboarding
    • 1.4 The use of communication tools and technologies for collaborative working
  2. Understand technical documentation and summarise data within a technical document
    • 2.1 The importance of using clear and consistent technical documentation when communicating gathered data
    • 2.2 Apply initiative to analyse findings from gathered data and summarise within a clear and consistent technical document
 L/651/1117 36
08 Collaboration and continuing professional development (CPD)
4 learning outcomes
  1. Understand digital transformation
    • 1.1 The impact of digital transformation (for example, new IT system) on data related occupations and within an overall business context: > customer issues and problems > business value > brand awareness > cultural/diversity awareness > internal and external stakeholders: >> user experience >> accessibility >> level of technical knowledge
  2. Understand learning techniques and sources of knowledge, and review own development needs
    • 2.1 How learning techniques (for example, evaluation and reflection) support and contribute to continuing professional development (CPD) of data related occupations
    • 2.2 The use of a range of sources of knowledge and verified information applicable to data related occupations (for example, professional networks, academic publications)
    • 2.3 Review own development needs and use a range of sources to keep up-to-date with developments in technologies, trends and innovation
  3. Understand multidisciplinary teams and working with others
    • 3.1 The purpose of a multidisciplinary team
    • 3.2 How the roles within a multidisciplinary team are identified
    • 3.3 The value of communication within multidisciplinary teams
    • 3.4 The importance of valuing difference and being sensitive to the needs of others
  4. Understand technical and non-technical stakeholders and apply prioritisation skills within a project
    • 4.1 A range of technical and non-technical stakeholders within an organisation: > customer/client > management > peer/colleague
    • 4.2 The benefits of logical reasoning. taking a thorough and organised approach when working within a project
    • 4.3 Apply prioritisation and time management skills to meet the requirements of a project
 M/651/1118 45
Total guided learning hours 360

Entry requirements

  • There are no specific prior skills or knowledge required.
  • Primarily aimed at learners aged 19 and over.
  • A Level 2 qualification, alongside good English and maths skills, is helpful.
Take it with you

The course guide

Everything on this page as one document you can keep, print, or send to whoever is deciding with you.

  • All 8 units, with the hours behind each one
  • How you are assessed, and who checks the marking
  • How paying works, and what is due when
  • Entry requirements and where the qualification leads
Download the guide (PDF)

Generated from this qualification's record, so the units and hours in it are the ones on this page. The fee is here on the page rather than in the guide, so a saved copy cannot quote you an old one.

How you are assessed

Your work is assessed by our qualified assessors, internally quality assured, and externally quality assured by NCFE. Assessment is against the criteria published in the qualification specification, and your assessor tells you what is needed before you start each unit.

For what happens after you submit: who marks it, who checks the marking, how long feedback takes and what to do if you disagree with a decision, see how assessment works.

If you need an adjustment to how you are assessed, for a disability or any other reason, ask us before you begin. We arrange adjustments under our Reasonable Adjustments policy, and you can request one online.

How we assess and quality assure is set out in DAIS-POL-016 Assessment and Internal Quality Assurance.

How you study, and what you need

You study online through our virtual learning environment, which is where the teaching material, your assessment submissions, your feedback and your progress all live. You work at your own pace, with tutor support throughout. Everyone completes a short online induction before starting an assessed unit.

What you need to take part
Device Laptop or desktop computer
Operating system Windows 10 or macOS 10.15 or later
Browser Google Chrome or Mozilla Firefox, latest version
Internet speed 10 Mbps download and 5 Mbps upload
Webcam Required for live sessions and identity verification
Microphone Required for live sessions

These are the minimum requirements published in DAIS-POL-024 Online Learning and Digital Delivery.

Support, and how quickly we reply

These are the response times we commit to in policy, not an aspiration.

What How Response
General questions Message your tutor in the VLE Within 2 working days
Assessment feedback Returned in the VLE or e-portfolio Within 10 working days of submission
Technical problems Technical support email Within 1 working day
Urgent welfare concerns Email to the Designated Safeguarding Lead Within 1 working day

Published in DAIS-POL-024 and DAIS-POL-009 Learner Support.

Registration and your certificate

We register you with NCFE before you begin any assessed unit, and we collect your Unique Learner Number as part of that. When your assessment decisions are finalised and quality assured, we claim your certificate through the NCFE portal and you are notified digitally. The certificate comes from NCFE, not from us.

Set out in DAIS-POL-008 Learner Registration and Certification.

Progression

Learners who achieve this qualification could progress to the following:

  • employment:
    • data support analyst
    • data technician
    • junior data analyst
    • junior information analyst

Progression to higher-level studies

Level 3 qualifications can support progression to higher-level study, which requires knowledge and skills

different from those gained at levels 1 and 2. Level 3 qualifications enable learners to:

  • apply factual, procedural and theoretical subject knowledge
  • use relevant knowledge and methods to address complex, non-routine problems
  • interpret and evaluate relevant information and ideas
  • understand the nature of the area of study or work
  • demonstrate an awareness of different perspectives and approaches
  • identify, select and use appropriate cognitive and practical skills
  • use appropriate research to inform actions
  • review and evaluate the effectiveness of their own methods

Common questions

Who awards NCFE Level 3 Technical Occupational Entry for the Data Technician - Diploma, and how do I get my certificate?

NCFE Level 3 Technical Occupational Entry for the Data Technician - Diploma is awarded by NCFE, an Ofqual-approved awarding organisation, and sits on the Regulated Qualifications Framework (RQF). The qualification number is 610/4006/X. The Data and AI School of London is an NCFE approved centre (Account No. 11001657). We register you with NCFE before you start any assessed unit and collect your Unique Learner Number. Once your work is assessed and quality assured we claim your certificate through the NCFE portal, and it is issued by NCFE rather than by us.

What do I need before I can start?

There are no specific prior skills or knowledge required. We assess every applicant individually and will tell you if a different level would suit you better.

Can I study this entirely online?

Yes. Teaching, materials and assessment are all delivered through our Moodle-based virtual learning environment, so you can study from anywhere in the UK. You need a laptop or desktop computer, a current version of Chrome or Firefox, and a broadband connection of at least 10 Mbps down and 5 Mbps up. Tutor support is available throughout, and we answer questions within 2 working days and return assessment feedback within 10 working days of submission.

Can I enrol now, and when do I pay?

The fee is £995. Enrolment is open, so you can apply online today. Nothing is paid at application: payment is taken only after you accept an offer.

Not sure this is the right qualification?

Ask before you apply. We will say honestly if a different level would suit you better.

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