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Level 4 · NCFE Self-paced
Enrolling now NCFE Approved Qualification number 603/7751/3

NCFE Level 4 Diploma: Data Analyst

An employer-recognised Higher Technical Qualification giving you the skills to launch or advance a career in data analysis.

Duration: 510 guided learning hours, 1200 total hours; self-paced online, one to two years

Level
Level 4
Qualification number
603/7751/3
Awarding organisation
NCFE
Regulated status
On the RQF, regulated by Ofqual
Guided learning hours
510 hours
Total qualification time
1200 hours
Delivery
100% online
Study pattern
510 guided learning hours, 1200 total hours; self-paced online, one to two years
NCFE This qualification is awarded by NCFE and regulated by Ofqual

Course overview

This is a Higher Technical Qualification (HTQ), approved by the Institute for Apprenticeships and Technical Education as meeting specific employer needs and industry requirements. It is designed for learners who want to begin or advance their career within data analysis.

Made up of mandatory units, the qualification covers legislation and security standards applied to data analytics, data fundamentals and lifecycle, data structures and databases, stakeholder engagement and user experience, organisational data, and data mining and statistical analysis. Assessment is one component: an internally assessed portfolio of evidence, externally quality assured by NCFE and graded pass, merit or distinction. The portfolio includes practical, task-based work.

It prepares learners for a variety of roles in data analysis, such as junior analyst, data analyst, marketing data analyst or problem analyst, as well as progression to related apprenticeships and higher education.

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 Legislation and security standards applied to data analytics (mandatory)
4 learning outcomes
  1. Explore relevant legislation and their influence on the safe use of data
    • P1 Describe the key features of relevant regulatory requirements in relation to the role of a data analyst
    • P2 Explain the importance of complying with legislation and the impact of non- compliance
  2. Investigate the impact of relevant security standards, security frameworks and organisational policies and procedures on data management activities
    • P3 Define relevant security standards and security frameworks related to data management activities
    • P4 Outline policies and procedures used by an organisation to ensure compliance with standards and frameworks
  3. Examine ethical data principles and the role of AI in the collation and utilisation of data
    • P5 Describe the value of applying ethical data principles when handling data
  4. Demonstrate effective use and compliance of data systems when securely collating and utilising data
    • P6 Collect and use data for an analysis activity whilst adhering to legal and organisational requirements
 A/651/0924 30
02 Data fundamentals and lifecycle (mandatory)
4 learning outcomes
  1. Explore the purpose of different data types and their use, characteristics, and applications
    • P1 Describe the properties and uses of qualitative and quantitative data types
    • P2 Explain the characteristics and application of common linear and non-linear data structures
  2. Perform analysis of datasets and relevant data structures
    • P3 Explore basic datasets and relevant data structures by applying fundamental techniques and principles
    • P4 Describe relevant data structures and how to appropriately classify data
  3. Examine the importance of data classification and considerations needed for data management
    • P5 Summarise types of data classification methods and considerations for effective data management
    • P6 Outline the purpose of metadata
  4. Implement the fundamental principles of the data lifecycle
    • P7 Identify the role and requirements for each stage of the data lifecycle and how this is applied to routine data tasks
    • P8 Carry out a routine daily data task following each stage of the data lifecycle
 D/651/0925 90
03 Data structure and databases (mandatory)
3 learning outcomes
  1. Examine the fundamentals of database system design
    • P1 Describe the features and application of both relational and non- relational databases
    • P2 Explain the database development lifecycle
  2. Explore the application of data modelling and database design and the implementation and maintenance of database systems and process
    • P3 Explain the process and purpose of data modelling using suitable design tools
    • P4 Describe the characteristics of different data formats within different types of databases and outline processes to implement and maintain databases
    • P5 Outline appropriate database designs to meet future analytical needs using the data analysis lifecycle
  3. Investigate common quality risks in data and implement mitigation techniques
    • P6 Explain various types of inconsistencies in data and outline the impact of using unclean data
    • P7 Summarise the risks associated with combining data and state methods for escalating data risks
    • P8 Discuss the ability to identify data risks and explain how mitigation techniques can be used
 F/651/0926 90
04 Stakeholder engagement and user experience in data analytics (mandatory)
3 learning outcomes
  1. Examine how to plan and deliver an effective analysis for a range of stakeholders
    • P1 Identify internal and external stakeholders and list techniques used to gather user requirements
    • P2 Describe factors that could influence data analytic planning and outline the significance of defining the scope of data analysis
    • P3 Explain the importance of identifying dataset NCFE Level 4 Diploma: Data Analyst (603/7751/3) 23 (LOs) needs and how these will be sourced
    • P4 Demonstrate the ability to undertake analysis based on stakeholder requirements and clearly communicate findings
  2. Demonstrate how to collate and display data using the most appropriate visualisation tools and assess the impact on user experience
    • P5 Collect and present data using the most appropriate visualisation tools
    • P6 Describe how a visualisation impacts user experience
    • P7 Explain how end- user requirements shape data presentation approaches and state the impact of not meeting these requirements
  3. Explore factors that can impact user experience in relation to data analytics
    • P8 Explore a range of accessibility measures and usability theories and describe how these can be applied to improve the user experience
 H/651/0927 90
05 Organisational data (mandatory)
3 learning outcomes
  1. Explore common data combination techniques and identify data sources
    • P1 Describe the purpose and outline the application of common techniques for matching and comparing data
    • P2 Identify appropriate data sources for combining data
    • P3 Discuss the risks and challenges associated with data combination and how to mitigate them
  2. Examine common data analytics methods and the functions and features of the tools used to support this
    • P4 Explore common data analytics methods and the functions and features of common data analytics tools used to support this
    • P5 Explain the factors that influence the selection of tools and NCFE Level 4 Diploma: Data Analyst (603/7751/3) 26 methods of data analysis
  3. Investigate organisational data architecture and demonstrate how to design data models
    • P6 Describe the role and purpose of organisational data architecture and the characteristics of storage repositories that support data management
    • P7 Explain the stages of data modelling
    • P8 Create a logical data model to meet organisational requirements
 J/651/0928 90
06 Data mining and statistical analysis (mandatory)
3 learning outcomes
  1. Examine the purpose of common statistical methodologies and their application to meet requirements
    • P1 Explain the purpose of common statistical methodologies
    • P2 Define how to use statistical methods to meet data analysis requirements
  2. Explore the purpose and application of statistical analysis
    • P3 Outline the purpose and application of descriptive, predictive and prescriptive analytics and identify factors that influence the selection of a model
    • P4 Explain the purpose of statistical programming languages and tools used for manipulating and processing data
    • P5 Describe the techniques used to prepare data for analysis
  3. Demonstrate the selection and use of appropriate tools for statistical analysis
    • P6 Explain how statistical programming languages can be used for predictive analytics
    • P7 Describe how to use analytical and NCFE Level 4 Diploma: Data Analyst (603/7751/3) 29 (LOs) modelling techniques to predict trends and patterns in data perform predictive analytics NCFE Level 4 Diploma: Data Analyst (603/7751/3) 30 The key requirements of the assessment strategies or principles that relate to units in this qualification are summarised below. The centre must ensure that individuals undertaking assessor or quality assurer roles within the centre conform to the assessment requirements for the unit they are assessing or quality assuring. Knowledge learning outcomes (LOs) • assessors will need to be both occupationally knowledgeable and qualified to make assessment decisions • internal quality assurers (IQAs) will need to be both occupationally knowledgeable and qualified to make quality assurance decisions Competence/skills LOs • assessors will need to be both occupationally competent and qualified to make assessment decisions • IQAs will need to be both occupationally knowledgeable and qualified to make quality assurance decisions NCFE Level 4 Diploma: Data Analyst (603/7751/3) 31 Section 3: explanation of terms This table explains how the terms used at level 4 in the unit content are applied to this qualification (not all verbs are used in this qualification). Analyse Break down the subject or complex situations into separate parts and examine each part in detail. Identify the main issues and show how the main ideas are related to practice and why they are important. Reference to current research or theory may support the analysis. Critically analyse This is a development of ‘analyse' that explores limitations as well as positive aspects of the main ideas in order to form a reasoned opinion. Clarify Explain the information in a clear, concise way showing depth of understanding. Classify Organise accurately according to specific criteria. Collate Collect and present information arranged in sequence or logical order that is suitable for purpose. Compare Examine the subjects in detail; consider and contrast similarities and differences. Critically compare This is a development of ‘compare' where the learner considers and contrasts the positive aspects and limitations of the subject. Consider Think carefully and write about a problem, action or decision showing how views and opinions have been developed. Demonstrate Practical application of an element/content to show that you understand theories/concepts in a practical sense. Describe Provide a broad range of detailed information about the subject or item in a logical way. Discuss Write a detailed account that includes contrasting perspectives. Draw conclusions Make a final decision or judgement based on reasons. Evaluate Examine strengths and weaknesses, arguments for and against and/or similarities and differences. Judge the evidence from the different perspectives and make a valid conclusion or reasoned judgement. Apply current research or theories to support the evaluation. Critically evaluate This is a development of ‘evaluate' where the learner debates the validity of claims from the opposing views and produces a convincing argument to support the conclusion or judgement. Examine Look closely at something. Think and write about the detail, and question it where appropriate. NCFE Level 4 Diploma: Data Analyst (603/7751/3) 32 Explain Apply reasoning to account for how something is or to show understanding of underpinning concepts. Responses could include examples to support these reasons. Explore Consider an idea or topic broadly, searching out related and/or particularly relevant, interesting or debatable points. Identify Apply an in-depth knowledge to give the main points accurately (a description may also be necessary to gain higher marks when using compensatory marking). Investigate Inquire into (a situation or problem) to explore solutions. Justify Give a detailed explanation of the reasons for actions or decisions. Perform Present/enact/demonstrate practically. Reflect Learners should consider their actions, experiences or learning and the implications of these in order to suggest significant developments for practice and professional development. Review and revise Look back over the subject and make corrections or changes based on additional knowledge or experience. Summarise Give the main ideas or facts in a concise way to develop key issues. Test Complete a series of checks utilising a set procedure. NCFE Level 4 Diploma: Data Analyst (603/7751/3) 33 Section 4: support Support materials The following support materials are available to assist with the delivery of this qualification and are available on the NCFE website: • Qualification Factsheet Useful websites Centres may find the following websites helpful for information, materials and resources to assist with the delivery of this qualification: • Institute for Apprenticeships and Technical Education • Legislation.gov.uk • Real Python • Stack Overflow • Kaggle • GitHub These links are provided as sources of potentially useful information for delivery/learning of this subject area. NCFE does not explicitly endorse these websites or any learning resources available on these websites. For official NCFE-endorsed learning resources, please see the additional and teaching materials sections on the qualification's page on the NCFE website. Other support materials The resources and materials used in the delivery of this qualification must be age-appropriate and due consideration should be given to the wellbeing and safeguarding of learners in line with your institute's safeguarding policy when developing or selecting delivery materials. Reproduction of this document Reproduction by approved centres is permissible for internal use under the following conditions: • you may copy and paste any material from this document; however, we do not accept any liability for any incomplete or inaccurate copying and subsequent use of this information • the use of PDF versions of our support materials on the NCFE website will ensure that correct and up-to-date information is provided to learners • any photographs in this publication are either our exclusive property or used under licence from a third party: o they are protected under copyright law and cannot be reproduced, copied, or manipulated in any form o this includes the use of any image or part of an image in individual or group projects and assessment materials o all images have a signed model release NCFE Level 4 Diploma: Data Analyst (603/7751/3) 34 Contact us Q6 Quorum Park Benton Lane Newcastle upon Tyne NE12 8BT Tel: 0191 239 8000* Fax: 0191 239 8001 Email: customersupport@ncfe.org.uk Website: www.ncfe.org.uk Information in this Qualification Specification is correct at the time of publishing but may be subject to change. (Company No. 2896700). CACHE; Council for Awards in Care, Health and Education; and NNEB are registered trademarks owned by NCFE. All the material in this publication is protected by copyright. * To continue to improve our levels of customer service, telephone calls may be recorded for training and quality purposes. NCFE Level 4 Diploma: Data Analyst (603/7751/3) 35 Appendix A: units To simplify cross-referencing assessments and quality assurance, we have used a sequential numbering system in this document for each unit. Knowledge only units are indicated by a star. If a unit is not marked with a star, it is a skills unit or contains a mix of knowledge and skills. Mandatory units Unit number Regulated unit number Unit title Level Credit GLH Unit 01 A/651/0924 Legislation and security standards applied to data analytics 4 10 30 Unit 02 D/651/0925 Data fundamentals and lifecycle 4 20 90 Unit 03 F/651/0926 Data structure and databases 4 20 90 Unit 04 H/651/0927 Stakeholder engagement and user experience in data analytics 4 20 90 Unit 05 J/651/0928 Organisational data 4 20 90 Unit 06 K/651/0929 Data mining and statistical analysis 4 30 120 The units above may be available as stand-alone unit programmes. Please visit the NCFE website for further information.
 K/651/0929 120
Total guided learning hours 510

Entry requirements

  • No specific prior skills or knowledge required, though a relevant Level 3 qualification is helpful.
  • Learners must be at least 18 years old.
  • Sound English and maths skills are recommended given the analytical nature of the course.
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 6 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

Progression: prepares you for senior data analyst roles and for further specialist study in data engineering. This qualification does not carry UCAS Tariff points.

Other names for this qualification

The regulated title is NCFE Level 4 Diploma: Data Analyst, qualification number 603/7751/3. People also look for it as:

  • Level 4 Data Analyst
  • Data Analyst Level 4
  • NCFE Level 4 Data Analyst
  • Data Analyst HTQ
  • HTQ Data Analyst
  • 603/7751/3
  • NCFE 603/7751/3
  • Data Analyst course online
  • Data Analyst qualification UK
  • Data Analyst distance learning

If you arrived searching one of those, you are in the right place: they all describe this one qualification.

Fees and paying for it

Ways to pay
Pay in full, or spread the cost over 3 monthly instalments.
Nothing is paid when you apply. Payment is only taken after you accept an offer, and we will confirm the exact amounts with you before then. Fees and how to pay.
Investing in your own skills
Your fee pays for the part that gives a qualification its value: assessment by a qualified assessor, independent quality assurance, and a certificate issued by NCFE on the Regulated Qualifications Framework, regulated by Ofqual.
You can start now rather than waiting for a September intake, and study around work. Tutor replies come within 2 working days, and assessment feedback within 10.
You finish with evidence as well as knowledge: a portfolio of your own work, and a qualification that stays on your record for life. Of everything you can spend money on, your own skills are the one thing that follows you into every job you ever take.

Many employers will pay for this, and most people never ask. Send them the one-page case: what it costs, what it asks of the business, and how to check us independently. How employer funding works.

Common questions

Who awards NCFE Level 4 Diploma: Data Analyst, and how do I get my certificate?

NCFE Level 4 Diploma: Data Analyst is awarded by NCFE, an Ofqual-approved awarding organisation, and sits on the Regulated Qualifications Framework (RQF). The qualification number is 603/7751/3. The Data and AI School of London is an NCFE approved centre. 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.

How long does NCFE Level 4 Diploma: Data Analyst take?

This qualification carries 510 guided learning hours and a total qualification time of 1200 hours. It is studied online at your own pace, so how long it takes in calendar time depends on how much you can commit each week.

What do I need before I can start?

No specific prior skills or knowledge required, though a relevant Level 3 qualification is helpful. 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 £2,995. 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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