AAQ IT Data Analytics Revision: The Complete Free Guide to F200, F201 and F202

AAQ Data Analytics is the OCR Level 3 Cambridge Advanced National in IT: Data Analytics, available as a Certificate (H019) and an Extended Certificate (H119), first taught from September 2025. It is assessed through written exams, F200 Fundamentals of data analytics and, on the Extended Certificate, F201 Big data and machine learning, plus coursework set by OCR and marked by your centre, starting with F202 Spreadsheet data modelling. This guide covers the whole qualification: what is in each unit, how the exams work, how the coursework is graded, and how to revise for a specification with very few past papers.

What is AAQ Data Analytics?

AAQ Data Analytics is a Level 3 Alternative Academic Qualification awarded by OCR, and it comes in two sizes: the Certificate (H019), which is 150 guided learning hours and the same size as an AS Level, and the Extended Certificate (H119), which is 360 guided learning hours and the same size as one full A Level. The full title is the Cambridge OCR Level 3 Alternative Academic Qualification Cambridge Advanced National in IT: Data Analytics. Schools and students shorten it to AAQ IT, AAQ Data Analytics or Cambridge Advanced National Data Analytics; they all mean the same qualification.

The letters AAQ matter. An Alternative Academic Qualification is designed to sit alongside A Levels in a sixth form or college study programme, not to replace them. It is built to meet the Department for Education's requirements for the Alternative Academic Qualifications category of the 16 to 19 performance tables, meets funding approval criteria, and is recognised in the UCAS tariff tables.

The subject is applied. You learn what data, information and knowledge are, how data is managed from creation to destruction, how it is stored, secured and shared, and the laws that control it. On the Extended Certificate you go on to big data, data mining, machine learning, and the ethical and environmental side of data use. The coursework then puts it into practice for a client: a spreadsheet data model in F202, then two optional units such as relational databases or dashboards.

There is no programming requirement. The practical work is in spreadsheets (and, in the optional units, databases, dashboards and marketing tools), and the exams test understanding and application rather than code. That is the main structural difference from A Level Computer Science.

Qualification Structure: H019 Certificate vs H119 Extended Certificate

The Certificate (H019) is 150 guided learning hours and needs two units. The Extended Certificate (H119) is 360 guided learning hours and needs five. The smaller qualification is nested inside the larger one, so F200 and F202 are common to both and nothing is wasted if a centre starts with the Certificate.

Both qualifications are offered in England only, are approved for ages 16 to 18, 18 plus and 19 plus, and have no formal entry requirements: the specification says there is no requirement to hold any specific qualification before starting. Both are graded Distinction*, Distinction, Merit and Pass.

If you are choosing between them, the practical difference is big data. The Extended Certificate adds F201 Big data and machine learning and two optional coursework units, which turns an introduction to data analytics into a full A Level sized qualification.

All Seven AAQ Data Analytics Units at a Glance

There are seven units in the suite: two examined and five non-examined. F200 and F202 are mandatory for both sizes, F201 is mandatory for the Extended Certificate, and the remaining four are optional coursework units from which Extended Certificate students take two.

Every unit is mapped to four performance objectives, and knowing which is which explains how questions are written. PO1 is showing knowledge and understanding and is assessed in the exams only. PO2 is applying knowledge and understanding. PO3 is analysing and evaluating. PO4 is demonstrating and applying skills and processes, and is assessed in the NEA units only. On the Certificate, PO1 is only 15 to 20 per cent of the qualification, while PO2 is 32.5 to 37.5 per cent and PO3 is 22.5 per cent (10 per cent in the exam and 12.5 per cent in the NEA). Recall alone is a minority of the marks: most of them are for applying and judging.

F200 Fundamentals of Data Analytics: Revision by Topic Area

F200 is a 75 guided learning hour unit examined by a 1 hour 15 minute paper worth 60 marks, and it is taken on both qualification sizes. It has five topic areas, and the paper has at least one question or part question on each. Unlike F201, there is no single scenario: since version 5 of the specification the paper uses a number of short contexts, and PO3 questions are answered in relation to the context given in the question. There are two six mark extended response questions marked with levels of response.

Topic Area 1: Understanding data

The foundations. Data, information and knowledge: what each is, where each comes from, and how data becomes information when it is processed and given context. OCR's sample mark scheme defines data as "raw facts and figures", and that phrase is worth learning exactly. Then the benefits and limitations of data to organisations and individuals, and its positive and negative effects on society. Big data is introduced here (concept, structure, uses, benefits and limitations), but its evolution, preparation, mining and infrastructure are F201 content.

Data and file formats is a fixed list: ASCII, AIFF, WAV, XML, JPEG, PNG, TIFF, JSON, numeric, and the plain text formats CSV, fixed width and TXT. You need each format's characteristics, uses, benefits and limitations, and the specification excludes every other format and lossy and lossless compression. Data types are Boolean, character, date, integer, real and string, and the four classifications are qualitative, quantitative, structured and unstructured.

Topic Area 2: Managing data

The largest topic area. It opens with two models that are easy to mix up. Data lifecycle management has five phases in a fixed order: creation, storage, usage, archival, destruction. The data analytics pipeline also has five: capture (data ingestion), process (data transformation), data storage (data stores), analysis, and use (visualisation). The lifecycle is the life of the data; the pipeline is how data becomes insight.

Then creation and capture: eight data assurance considerations (accuracy, quality, redundancy, reliability, timeliness, validation, validity, verification) and nine data gathering methods from focus groups to verbal histories, plus the three factors that make gathering effective: defining requirements, defining relevant data and avoiding bias. Storage covers the three data states (in transit, at rest, in use), data stores, block, file and object storage, on-site storage (file servers, HDD, SSD, NAS, portable devices, SAN) and the four cloud types. The topic ends with data wrangling and maintenance, the five types of data analytics (cognitive, descriptive, diagnostic, predictive, prescriptive), presentation and visualisation methods including six chart types, three archiving methods and five secure destruction methods.

Topic Area 3: How data can be accessed and managed across platforms

Three short sections that are mostly about matching. APIs: what they are, the five certifications (composite, internal, partner, private, public) and the four types with the data format each uses (JSON-RPC, REST, SOAP, XML-RPC). User access controls: ABAC, DAC, MAC, RBAC and RuBAC, and when each fits. Permissions: the user rights read, write, edit and delete, and the administrator rights of allocating access controls and privileges at user, user group, and file and folder level. The sample paper's answer for a government office holding highly sensitive data is MAC.

Topic Area 4: Legal considerations

Five pieces of legislation and one body: the Computer Misuse Act, the Data Protection Act 2018, the Freedom of Information Act 2000, the UK GDPR, the Privacy and Electronic Communications Regulations (PECR), and the role of the Information Commissioner's Office. For each you need the latest version, its main purpose and important aspects, what an organisation must do to comply, and the impact of not complying. The detailed content of each Act is excluded.

Topic Area 5: Job roles, skills and attributes

Seven roles: AI scientist, data analyst, data architect, data engineer, data scientist, database administrator and machine learning engineer. The examinable part is how each contributes to the pipeline and the lifecycle, not detailed job descriptions or the specific skills each needs. Then nine personal attributes (analytical skills to time management) and five communication skills: appropriate language for the audience, non-verbal, questioning techniques, verbal and written.

F201 Big Data and Machine Learning: Revision by Topic Area

F201 is a 70 guided learning hour unit examined by a 1 hour 30 minute paper worth 60 marks, and it is taken only on the Extended Certificate. It is built around one short scenario that develops through the paper, and it contains one six mark and one nine mark extended response question with levels of response mark schemes. Every topic area appears at least once.

Topic Area 1: The scope of managing big data

The 6Vs, written in the specification's full wording: volume of data, variety of data types, velocity of streamed data, value of data, veracity of data, and variability of data inconsistencies. OCR's sample mark scheme accepts "variety of data types" and rejects "variety" on its own, so learn the full phrases. Then the six steps for analysing big data in order (collection, storing, cleaning, mining, analysis, consumption), five developments in the evolution of big data, nine capture methods from satellites to transactional records, and nine areas of application from banking to retail.

Topic Area 2: The infrastructure challenges of big data

The biggest topic area in F201. Structured, unstructured and semi-structured data, each with the specification's own examples (continuous, discrete, relational database, spreadsheet and transactional data; social media and weather data; emails, XML, zipped files and web pages). Data preparation for numeric and textual data, data wrangling, and eight named cleaning techniques. The four data mining techniques (descriptive, diagnostic, predictive, prescriptive).

Then infrastructure: dedicated servers against distributed clusters; six software platforms including NoSQL and data integration platforms; and data storage areas, including public and private cloud, data lakes, data warehouses, SSDs and three emerging technologies (blockchain, DNA, quantum server). It closes with the difference between data science and data analytics and six analytic techniques: regression, Monte Carlo simulation, factor, cohort, cluster and time series analysis. The sample paper's nine mark question asked whether a retailer should use cluster analysis.

Topic Area 3: Big data, machine learning and AI

What AI is, how it is developed and used in data science, and its benefits and limitations. What machine learning is, and three named algorithms: decision tree, random forest and K-Means, with the purpose, characteristics, benefits, limitations and use of each in digital analysis. Then how big data, machine learning and AI interact. The historical development of AI is excluded.

Topic Area 4: Legal and ethical issues in data management

The legal half is the UK GDPR in more depth than F200: features, principles, rights of data subjects and marketing consent, how to comply, why compliance matters, the impact of non-compliance, and how organisational policies reduce the risk. The ethical half covers automated decision making and how it creates bias, the collection, storage, ownership and sharing of data, emerging ethical debates, and two frameworks: the data ethics framework and the inclusive data principles. Two numbers are worth memorising: 72 hours to report a notifiable breach to the ICO, and fines of up to £17.5 million or 4 per cent of annual global turnover, whichever is higher.

Topic Area 5: Environment and society

Six environmental areas: accuracy of weather forecasting, natural disaster management, energy efficiency, environmental management, platforms to combat climate change, and emerging environmental developments. For each, how big data is used and its benefits and limitations. The society half is the smart city (its purpose, how data from many sources is exchanged to run it, and its benefits and limitations) and four emerging social developments: personalised healthcare, smart homes, traffic management, and urban and community planning.

F202 Spreadsheet Data Modelling: The NEA Explained

F202 is the coursework unit every AAQ Data Analytics student takes, on both H019 and H119. It is 75 guided learning hours, assessed against 24 criteria, and worth 60 uniform marks, the same as an exam paper. OCR sets the assignment, your centre marks it, and OCR moderates the marking. The set assignment has four practical tasks and takes about 15 hours of supervised time.

The assignment gives you a client, a problem a spreadsheet model must solve, a list of requirements, fixed rules for the calculations, and data files. In OCR's sample assignment the work splits like this:

• Task 1: plan and design the model. SMART success criteria, project constraints, the structure, the formulae (written as headings, not cell references), formatting, validation, comments and protection, HCI features, two test plans and a test strategy.
• Task 2: build the model from the plans, using formatting, simple formulae, linked worksheets and functions, validation, protection and data manipulation tools, then carry out end testing with screenshots.
• Task 3: produce outputs, analyse the trends and patterns they show, evaluate the outcomes with recommendations, and write the parts of the technical and user documentation the task names.
• Task 4: compare the model with the user requirements, assess the HCI features, and evaluate the whole model with justified improvements.

F202 draws directly on F200: data types, file formats such as CSV, data assurance and validation, presenting and visualising data, and permissions all reappear in the criteria. The assessment guidance contains several requirements that are easy to miss, such as at least one advanced function for D1, normal, extreme and erroneous test data for both test plans, and a test strategy that is a short statement, not a lengthy document.

The Optional NEA Units: F203 to F206, and How to Choose

Extended Certificate students take two optional coursework units alongside F202. Each is 70 guided learning hours, each has 24 assessment criteria and is worth 60 uniform marks, and each is set by OCR and moderated in the same way. Every one asks you to plan, build or present, and review a solution for a specific client. Your centre normally decides which two it offers, so you may not get a free choice.

The specification's synoptic grids link all five NEA units to F200, and F203 to F206 to F201 as well. In plain terms, the coursework is where you use what the exams taught you, so revising the exam content properly makes the coursework easier, and the coursework makes the exam content stick.

Exam Technique for F200 and F201

F200 is 75 minutes for 60 marks and F201 is 90 minutes for 60 marks. Every question is compulsory on both. The two papers are built differently, and knowing how is worth marks on its own.

Short contexts in F200, one scenario in F201

F200 uses a number of short contexts through the paper: a pizza shop in one question, a college or a government office in the next. F201 uses one scenario that develops through the paper, so later questions add detail to the same organisation. Either way, application marks need you to use the context. OCR's sample mark schemes give one mark for a point and a second for applying it, for example "stock levels can be monitored (1) so the retailer can order more of its popular products (1)". The same point with no link to the organisation gets one mark or none.

The question types you will meet

• Forced choice and controlled response, such as multiple choice, matching or putting phases in order. The sample F200 paper asks for the five lifecycle phases in order.
• Short answer, closed response. Usually one or two marks: state, identify, name. One point per mark.
• Extended response with points-based mark schemes. Each creditworthy point earns a mark, usually a point and its expansion.
• Extended response with levels of response mark schemes: two six mark questions in F200, and one six mark and one nine mark question in F201. These are marked on the quality of the whole answer, not by counting points.

How to approach the six and nine mark questions

They are worth 12 of the 60 marks in F200 and 15 in F201, and they are where the PO3 analysis and evaluation marks sit. In the sample mark schemes the top band needs benefits and limitations applied to the context, then a clear conclusion with reasons. The sample F200 six mark question asked whether a small company should use a NAS; the F201 nine mark question asked whether a retailer should use cluster analysis.

A reliable structure: make a point, explain why it matters, apply it to the organisation in the question, then weigh it against a limitation. Do that two or three times and finish with a conclusion that actually decides. If the command word is discuss, an answer with no judgement at the end cannot reach the top level, however accurate the content.

Command words

Appendix A of the specification defines every command word used in the exams. These are the ones that decide the depth of an answer:

The most common way to lose marks is answering a level below the command word: describing when asked to explain, or listing points when asked to discuss. Underline the command word before you write.

Sample Assessment Materials and Practising With Few Past Papers

AAQ Data Analytics was first taught in September 2025, so only a handful of live exam series exist. The free material everyone can use is OCR's sample assessment paper for F200 and F201 with its mark scheme, an annotated version that explains the paper question by question, and the F202 sample assignment. Ask your teacher about papers and examiner reports from the first live series.

Use the sample papers in this order:

1. Sit the paper properly, timed and closed book, before you feel ready. The point is to find the gaps.
2. Mark it against the mark scheme without being generous. Look at the "do not accept" notes as well as the answers: "variety" alone is rejected, and so is an item of information that just repeats a column heading.
3. Read the annotated version, which shows what each question is testing and what the extended answers need.
4. Rewrite your long answers using what it told you. One six mark answer rewritten well is worth more than five new questions done badly.

The specification is a practice tool too. The breadth and depth column next to each piece of teaching content tells you what can be asked. A line that says "the benefits and limitations of each data storage type" means a question can ask for the benefits and limitations of any item in that list. Turn each line into a question and you have written your own paper.

The sister qualifications help with format. AAQ Computing (F160 and F161) and AAQ Cyber Security (F193 and F194) use the same question types and mark scheme structure, so their sample papers are useful practice for exam technique even though the content is different.

Grading: Distinction*, UMS and What Compensatory Marking Means

The qualification grades are Distinction* (D*), Distinction (D), Merit (M) and Pass (P). Units are graded pass, merit or distinction, and a unit below a pass is reported as unclassified. The marks from an unclassified unit still count towards your final grade: the specification's own phrase is "every mark counts".

Compensatory grading, and why it changes how you work

Cambridge Advanced Nationals use a compensatory model, not the hurdles model of Cambridge Technicals. For an NEA unit, your grade comes from the total number of criteria achieved, in any combination of pass, merit and distinction criteria. You do not need every pass criterion before a distinction criterion counts.

Those numbers change the strategy. A distinction in F202 needs 20 of the 24 criteria, and there are only 12 pass criteria, so you cannot get there without merit and distinction criteria. There is no penalty for attempting one and missing it. The worst approach is polishing the pass criteria and running out of time before the distinctions.

How the qualification grade is calculated

Each unit's raw result, the exam mark out of 60 or the number of criteria out of 24, is converted to the Uniform Mark Scale. Every unit is worth a maximum of 60 uniform marks, so an NEA unit counts as much as an exam. The unit totals are added, and the total decides the grade. On results day OCR also publishes a marks calculator for that series.

At unit level, the uniform mark boundaries are 24 for a pass, 36 for a merit and 48 for a distinction, out of 60. There is no Distinction* at unit level; it exists only as a qualification grade, which is why consistency across every unit matters more than one standout result.

Resits and resubmission

You can resit each examined unit twice, giving three attempts in total. For F200 that total includes attempts towards both the Certificate and the Extended Certificate. For NEA units there is one resubmission opportunity per assignment, with your teacher's agreement, while the assignment is still live. Each assignment is live for two years, which is why centres are told to use the one released in the year a cohort starts.

AAQ Data Analytics vs BTEC, Cambridge Technicals, A Level and T Level

Several Level 3 qualifications cover IT and data, and search results mix them together. Here is what is actually different.

Should you take AAQ Data Analytics or A Level Computer Science?

They are the same size and the same level, so the answer depends on what you want to do. A Level Computer Science is programming-based and abstract: algorithms, data structures, Boolean logic, architecture and a coded project. AAQ Data Analytics has no programming and is applied: how data is collected, stored, secured, analysed and presented, assessed partly through coursework done over time.

If you want to write software, take Computer Science. If you want to work with data in business, research or the public sector, or you do better across sustained coursework than in long exams, the AAQ is an academic route that fits beside other A Levels. The specification lists A Level Business, Computing, Geography, Maths and Psychology as subjects that complement it.

Should you take AAQ Data Analytics or a T Level?

Size decides it. A T Level is roughly three A Levels in one qualification with an industry placement, so it is a full-time programme in itself. The AAQ is at most one A Level in size and is designed to be taken alongside other subjects.

Entry Requirements, University and Careers

Entry requirements

There is no formal entry requirement. The specification states that students do not need any specific qualification before starting. Centres usually set the same GCSE requirements they use across their Level 3 courses, so ask your school or college what they expect.

Progression to university

The qualification is recognised in the UCAS tariff tables, and the Extended Certificate is the same size as one A Level. The specification names business analytics, information technology and digital marketing as the higher education courses it prepares you for.

Be realistic about competitive courses. Some computer science and data science degrees specify A Level Mathematics or Computer Science, and an AAQ will not replace a named subject requirement. Because the qualification is new, some universities have not yet updated their published requirements, so contact admissions directly rather than assuming a course that does not list it will reject it.

Careers this leads to

F200 Topic Area 5 names seven roles: artificial intelligence scientist, data analyst, data architect, data engineer, data scientist, database administrator and machine learning engineer. Most start from an entry-level analyst or data technician job, an apprenticeship, or a degree. The skills the qualification builds, spreadsheet modelling, database design, dashboards, data protection and presenting findings to a client, are the ones entry-level data jobs ask for.

How to Revise AAQ Data Analytics on a New Specification

A new specification has few past questions, few examiner reports and little third party material. Here is what works.

Revise from the breadth and depth column

The specification has two columns for every unit: teaching content, and breadth and depth. The second tells you what will be asked. A line that starts with "know" is recall only. Everything else must be understood: how, why, when, purpose, benefits and limitations, and suitability in different contexts. "The benefits and limitations of each data gathering method" is a question waiting to happen for any of the nine methods.

Learn the lists, because the lists are the spec

These units are list-heavy: twelve file formats, six data types, five lifecycle phases, five pipeline phases, eight assurance considerations, nine gathering methods, five access controls, the 6Vs, eight cleaning techniques, six analytic techniques. Questions often ask you to name several, put them in order, or choose the right one for a context, so the complete list beats three items in depth. Active recall works far better than re-reading, which is what flashcards are for.

Use the wording OCR credits

The sample mark schemes are strict about wording in places. Data is "raw facts and figures". The 6Vs need their full names. A Partner API "provides restricted access, intended for specific purposes"; a Public API "allows external users to access data or services". Learn these phrases exactly, because a close paraphrase can lose the mark.

Learn the exclusions

The specification says what will not be assessed. F200 excludes file formats not listed, lossy and lossless compression, specific wrangling and maintenance activities, non-secure destruction, how the API types work in detail, the detailed content of each Act, and the specific skills for each job role. F201 excludes the technical details of cleaning and analytic techniques, coding, the costs of server configurations, named providers of data lakes and warehouses, and the history of AI. Knowing these saves revision time and stops you giving an excluded answer.

A workable timeline

• Through the course: keep a running glossary in mark scheme wording. It is far easier to build weekly than to reconstruct in April.
• Eight weeks out: first pass through the notes, topic area by topic area, then flashcards daily on your weakest topics.
• Six weeks out: sit the sample paper properly, timed, then work through the annotated version.
• Four weeks out: extended answers only. The levels-marked questions are the hardest to improve at the last minute.
• Final fortnight: lists, orders, exclusions and the two UK GDPR numbers, in short recall sessions several times a day.

AAQ Data Analytics: Frequently Asked Questions

These are the questions students and parents search for about this qualification. Every answer is taken from the OCR specification.

The Best Free AAQ Data Analytics Revision Resources (2026)

When these notes were written, there was very little free written revision material for F200 and F201. Searches for AAQ IT data analytics revision mostly returned college course pages and material for different qualifications. Everything below is free, and the official OCR material is listed alongside, because you should be revising from the specification as well.

Being in one of the first cohorts of a specification is harder: few past papers, no seniors to borrow notes from, and few established resources. It also means nobody has a bank of predictable questions, so a student who has read the breadth and depth column is at less of a disadvantage than they feel. Everything on this site is free, and it always will be.