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 2 · NCFE Self-paced
Enrolling now NCFE Approved Qualification number 603/3916/0

NCFE Level 2 Certificate in Data Analysis

Learn to source, check, clean, interpret and present data using spreadsheets, and understand the data analyst role. 120 guided learning hours, assessed by portfolio with no exams.

Duration: 120 guided learning hours, 135 total hours, self-paced online

Level
Level 2
Qualification number
603/3916/0
Awarding organisation
NCFE
Regulated status
On the RQF, regulated by Ofqual
Guided learning hours
120 hours
Total qualification time
135 hours
Delivery
100% online
Study pattern
120 guided learning hours, 135 total hours, self-paced online
NCFE This qualification is awarded by NCFE and regulated by Ofqual
NCFE Account No. 11001657

Course overview

This is a regulated Level 2 qualification (NCFE qualification number 603/3916/0). It is designed for learners who want to improve their knowledge and skills in data analysis. It will support learners in a number of sectors and job roles where an understanding of data analysis is required, and learners may also progress to further study.

The qualification focuses on the study of data analysis, offers breadth and depth of study incorporating a key core of knowledge, and provides opportunities to acquire a number of practical and technical skills. Its objective is to give learners the knowledge and skills to perform some basic data analysis to help make informed, data driven decisions.

You study five mandatory units totalling 120 guided learning hours. Unit 01 introduces what data is, the difference between data and information, the units used to measure data size, big data, and the four types of data analytics (diagnostic, descriptive, predictive and prescriptive), along with qualitative and quantitative data and ethical data analysis.

Unit 02 covers the role of a data analyst, the technical and personal skills needed, career progression routes, exploratory data analysis and hypotheses. It also covers current data protection legislation, including personal data, the purpose of the GDPR, the seven key principles, lawful bases for processing, and the purpose and elements of the Data Protection Act.

Unit 03 is practical. You identify internal and external sources of data, assess their reliability and quality, use data validation checks, and learn the characteristics of good data. You cover research methods including interviews, questionnaires, surveys, observations, focus groups, case studies, documents and records, and physical measurements, then transfer data into a spreadsheet and perform data cleansing.

Unit 04 covers forms of data, outliers, identifying and non-identifying relationships, primary keys and text analytics, together with statistical terms such as sum, count, mean, mode, median, range, variance and skew. You then work in a spreadsheet to filter and anonymise data, use VLOOKUP and HLOOKUP, apply statistical functions and build a pivot table.

Unit 05 covers communicating findings clearly to an intended audience, why data should be encrypted and how, and designing effective data visualisations including bar graphs, line graphs, dual axis charts, stacked bar graphs, pie charts, scatter plots and histograms.

Assessment is by an internally assessed and externally quality assured portfolio of evidence. There are no exams and grades are not awarded. Evidence could include a report, a presentation and evidence of research. Learners who are not successful can resubmit work within the registration period.

To complete the practical units you will need a digital device (desktop, laptop or tablet), internet connectivity, a web browser, a storage medium and generic spreadsheet software capable of performing data analysis functions, such as Microsoft Excel, Google Sheets or Open Office. There is no requirement to use any specific software.

Learners who achieve this qualification could progress to the NCFE Level 2 Certificate in Digital Skills for Work, the NCFE Level 2 Certificate in Understanding Data Protection and Data Security, or Level 2 and Level 3 qualifications in Data Analytics and Programming. It is also useful for learners studying in Business and Administration, IT, Retail, Marketing, Media, Engineering, Accounting and Finance, HR and Recruitment.

Delivery is 100% online with tutor support.

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 Introduction to data analysis (mandatory)
2 learning outcomes
  1. Understand what data is
    • 1.1 Describe what data is
    • 1.2 Explain the difference between data and information
    • 1.3 Identify units used to measure the size of data
    • 1.4 Describe what is meant by big data
  2. Understand what data analysis is
    • 2.1 Explain what data analysis is
    • 2.2 Explain why businesses or individuals might use data analysis
    • 2.3 Define the following types of data analytics: • diagnostic • descriptive • predictive • prescriptive.
    • 2.4 Give examples of the following types of data analytics: • diagnostic • descriptive • predictive • prescriptive.
    • 2.5 Explain what qualitative data is
    • 2.6 Explain what quantitative data is
    • 2.7 Explain what is meant by ethical data analysis
 L/617/3890 15
02 The role of a data analyst (mandatory)
2 learning outcomes
  1. Understand the role of a data analyst
    • 1.1 Explain the role of a data analyst
    • 1.2 Identify the technical skills required by a data analyst
    • 1.3 Identify some of the career progression routes a data analyst can follow
    • 1.4 Explain why the following skills are important for a data analyst: • communication skills • critical thinking skills • a journalistic approach.
    • 1.5 Explain exploratory data analysis
    • 1.6 Explain the importance of developing and testing a hypothesis
    • 1.7 Give examples of questions a data analyst might ask when presented with a brief
  2. Understand current data protection legislation
    • 2.1 Define what is meant by personal data
    • 2.2 Describe the purpose of the General Data Protection Regulation (GDPR)
    • 2.3 Describe the following key principles of the general data protection regime: • lawfulness, fairness and transparency • purpose limitation • data minimisation • accuracy • storage limitation • integrity and confidentiality • accountability.
    • 2.4 Explain what is meant by a lawful basis for processing personal data
    • 2.5 Explain the purpose of the Data Protection Act
    • 2.6 Describe the following elements of the Data Protection Act: • general data processing • law enforcement processing • intelligence services processing • regulation and enforcement.
    • 2.7 Explain what is meant by ethical use of data
 R/617/3891 20
03 Collection, processing and preparation of data (mandatory)
3 learning outcomes
  1. Be able to identify sources of data
    • 1.1 Identify internal sources of data
    • 1.2 Identify external sources of data
    • 1.3 Assess the reliability of different sources of data
    • 1.4 Assess the quality of a chosen source of data
    • 1.5 Use data validation checks to ensure source data is accurate
    • 1.6 Identify the characteristics of good data
  2. Understand different research methods for collecting data
    • 2.1 Describe each of the following research methods for collecting data: • interviews • questionnaires • surveys • observations • focus groups • case studies • documents and records • physical measurements.
    • 2.2 Explain the importance of using both qualitative and quantitative methods
    • 2.3 Explain what is meant by the ethical collection of data
  3. Be able to structure and prepare data
    • 3.1 Describe the process of data preparation
    • 3.2 Perform a data transfer from a suitable external source to a spreadsheet
    • 3.3 Perform the following data cleansing measures: • remove duplicates • length checks • type checks • sense checks.
    • 3.4 Identify methods of storing data
 Y/617/3892 25
04 Interpretation of data (mandatory)
3 learning outcomes
  1. Understand how to assess different forms of data
    • 1.1 Explain the different forms of data available
    • 1.2 Explain what is meant by data outliers
    • 1.3 Describe what is meant by: • identifying relationship • non-identifying relationship • primary key.
    • 1.4 Describe different text analytics techniques
  2. Understand statistical terms used in data analysis
    • 2.1 Describe what is meant by the following: • sum • count • mean • mode • median • range.
    • 2.2 Explain what is meant by the ‘variance’ of a data set
    • 2.3 Explain what is meant by the ‘skew’ of a data set
  3. Be able to manipulate data in a spreadsheet
    • 3.1 Perform filtering of data
    • 3.2 Perform anonymisation of data
    • 3.3 Perform the following: • VLOOKUP • HLOOKUP.
    • 3.4 Perform the following spreadsheet functions: • count • sum • mean • mode • median • range.
    • 3.5 Discuss actions which can be taken relating to outliers
    • 3.6 Demonstrate the use of a pivot table
 D/617/3893 30
05 Communication and presentation of data (mandatory)
2 learning outcomes
  1. Understand the importance of communication
    • 1.1 Explain the importance of clear communication
    • 1.2 Explain the importance of understanding the intended audience
    • 1.3 Explain how the following factors will have an impact on how you present your data: • level of detail (granularity) • time • level of knowledge • familiarity with the dataset.
    • 1.4 Explain why it is important to encrypt data
    • 1.5 Identify methods of encrypting data
  2. Be able to design effective data visualisations
    • 2.1 Demonstrate the following types of data visualisation: • bar graph • line graph • dual axis chart • stacked bar graph • pie chart • scatter plot chart • histogram.
    • 2.2 Explain how the following factors impact data visualisations: • layout • use of colour • labelling • ordering • scaling.
    • 2.3 Use appropriate visualisations to display data
    • 2.4 Explain what the data visualisations show
 H/617/3894 30
Total guided learning hours 120

Entry requirements

  • There are no specific recommended prior learning requirements for this qualification.
  • This qualification is suitable for learners aged pre-16 and above.
  • Learners may find it helpful if they have achieved an Entry Level 3 qualification in Maths and an Entry Level 3 qualification in ICT.
  • You will need a digital device, internet access and spreadsheet software such as Microsoft Excel, Google Sheets or Open Office for the practical units.
  • Registration is at the discretion of the centre, in accordance with equality legislation.
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 5 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 our Level 3 Certificate in Data, and for junior data analyst and reporting roles. This qualification does not carry UCAS Tariff points.

Common questions

Who awards NCFE Level 2 Certificate in Data Analysis, and how do I get my certificate?

NCFE Level 2 Certificate in Data Analysis is awarded by NCFE, an Ofqual-approved awarding organisation, and sits on the Regulated Qualifications Framework (RQF). The qualification number is 603/3916/0. 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.

How long does NCFE Level 2 Certificate in Data Analysis take?

This qualification carries 120 guided learning hours and a total qualification time of 135 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?

There are no specific recommended prior learning requirements for this qualification. 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 £395. 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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