OCR · Level 3 (Years 12–13)
OCR Cambridge Advanced National in IT: Data Analytics H019/H119 Homework and Practice
The OCR Level 3 Cambridge Advanced National in IT is a sixth-form vocational qualification, offered as the Certificate (H019) and Extended Certificate (H119). The Data Analytics pathway covers how organisations collect, manage and analyse data, including big data and machine learning.
CS Ninja supports the knowledge content of the Data Analytics units, including F200 Fundamentals of data analytics and F201 Big data and machine learning. Teachers can set homework on data types and levels of measurement, data quality and cleaning, warehouses and data lakes, UK GDPR, the five Vs of big data, types of machine learning and ethical issues in AI, keeping students’ theory knowledge secure alongside practical work.
OCR H019/H119 Data Analytics specification coverage
Topics are grouped by unit and teaching area, matching the OCR H019/H119 specification.
F200: Fundamentals of data analytics
TA1 Understanding data
Data types, structures, levels of measurement, the analytics lifecycle and types of analytics
Skills practised: Big Data & Analytics; Data Collection & Organisation; Data Ethics & Society; Data Fundamentals; Data Representation & Classification
TA2 Managing data
Sources, collection, sampling, data quality and cleaning
Skills practised: Data Analysis & Presentation; Data Collection & Sourcing; Data Processing & Prep; Data Quality & Integrity; Data Storage & Management
TA3 How data can be accessed and managed across platforms
Warehouses, lakes, operational databases, file formats and cloud platforms
Skills practised: Access Control & Security; API Fundamentals; Cloud Computing Concepts; Cross-Platform Access; Data Storage Solutions
TA4 Legal considerations
UK GDPR, Data Protection Act, Computer Misuse Act and protecting personal data
Skills practised: Computer Misuse Act; Data Protection Enforcement; Data Protection Governance; Data Security & Compliance; Specific Legislation & Practice; UK GDPR Principles & Rights
TA5 Job roles, skills and attributes in data analytics
Data analyst, data engineer, data scientist and the skills each role needs
Skills practised: Career Pathways; Data Analytics Fundamentals; Data Analytics Technical; Professional Skills
F201: Big data and machine learning
TA1 The scope of managing big data
Volume, velocity, variety, veracity and value, and where big data comes from
Skills practised: Benefits of big data to organisations; Challenges in the scope of big data; Characteristics of big data; Limitations and risks of big data; Sources of big data; The five Vs of big data
TA2 The infrastructure challenges of big data
Distributed storage and processing, scaling, cloud services and pipelines
Skills practised: Batch vs real-time processing; Big data infrastructure; Cloud infrastructure for big data; Data cleaning techniques; Data pipelines; Distributed storage and processing; Infrastructure challenges; Storing big data
TA3 Big data, machine learning and artificial intelligence
Supervised, unsupervised and reinforcement learning, training data and model evaluation
Skills practised: AI concepts linked to big data; Classification, regression and clustering; Data analytic techniques; Evaluating a machine learning model; Machine learning algorithms; Training, validation and test data; Types of machine learning
TA4 Legal and ethical issues in data management
Bias, transparency, consent, accountability and regulation of AI
Skills practised: Automated decision-making; Bias and ethics in analysis; Bias in AI and data; Ethical principles for AI and big data; Legal and ethical issues in AI; Regulation of AI and data use
TA5 Environment and society
Energy use of data centres, e-waste, employment impacts and the digital divide
Skills practised: Environment and society; Environmental impact of big data; Reducing the impact of big data; Social impact of big data and automation; The digital divide
F202: Spreadsheet data modelling
TA1 Principles of spreadsheet modelling
Purpose of models, what-if analysis, assumptions, inputs, processes and outputs
Skills practised: Challenges of spreadsheet modelling; Principles of spreadsheet modelling; Purposes of spreadsheet modelling; Spreadsheet modelling development cycle; Technological developments in spreadsheet modelling; The decision-making process; Types of spreadsheet model
TA2 Planning the design of a spreadsheet model
Client requirements, structure charts, test plans and success criteria
Skills practised: Design tools for spreadsheet models; HCI features in data modelling; Planning a spreadsheet model; Planning testing; Project constraints; Structure of a spreadsheet model; When to test a spreadsheet model
TA3 Creating the spreadsheet model
Formulae, functions, lookups, validation, formatting and automation
Skills practised: Categories of spreadsheet function; Chart components; Designing data tables; Importing data into a spreadsheet; Preparing data in a spreadsheet; Relative and absolute cell references; Spreadsheet functions
TA4 Delivering the outcomes
Testing, user documentation, handover and presenting results to the client
Skills practised: Analysing processed data; Dashboard outputs; Delivering the outcomes; Making recommendations from analysis; Technical documentation; Technical vs user documentation; User documentation
TA5 Evaluation
Judging the model against requirements, strengths, weaknesses and improvements
Skills practised: Ease of use and accessibility; Effectiveness of a spreadsheet model; Evaluating a model; Evaluating HCI effectiveness; Meeting user and client requirements; SMART success criteria; Strengths, weaknesses and improvements
F203: Relational database design
TA1 Relational database concepts
Entities, attributes, keys, relationships, normalisation and referential integrity
Skills practised: ACID properties; Relational and non-relational databases; Relational database concepts; Relationship types
TA2 Plan relational database solutions
Client requirements, entity relationship diagrams, data dictionaries and test plans
Skills practised: Data dictionaries and naming conventions; Planning a database solution
TA3 Create relational databases
Tables, relationships, SQL queries, forms and reports
Skills practised: SQL for analysis
TA4 Testing relational database solutions
Test data, validation, verification and correcting faults
Skills practised: Testing a database
TA5 Evaluate database solutions
Measuring the solution against the client brief and suggesting improvements
Skills practised: Evaluating a database solution
F204: Data and the Internet of Everything (IoE)
TA1 IoE ecosystem
People, process, data and things, and examples of IoE in use
Skills practised: Benefits of IoE examples; How the pillars work together; IoE in cities and industry; Sectors that use the IoE; The four pillars of the IoE; The IoE ecosystem; What is the IoE
TA2 Data collection, processing and storage methods and devices
Sensors, actuators, embedded devices, edge and cloud processing
Skills practised: Cloud & Distributed Systems; Data Collection Methods; Data Processing Concepts; Data Storage Concepts; IoE Devices & Processing; Power & Efficiency
TA3 Connectivity and data transmission
Wi-Fi, Bluetooth, Zigbee, Z-Wave, NFC, mobile, fibre and copper
Skills practised: Connectivity Principles; Connectivity Types & Comparison; Data & Transmission; IoE Connectivity; Selecting Connectivity
TA4 Human computer interfaces (HCIs)
Interface types, accessibility and designing for the user
Skills practised: HCI Design & Selection; HCI Design Principles; HCI Fundamentals; HCI Output Modalities; IoE HCI Considerations
TA5 Securing IoE devices
Threats to devices and data in transit, mitigations, and legal and ethical considerations
Skills practised: Legal, Ethical & Privacy; Mitigation Strategies; Security Concepts & Scope; Security Design & Architecture; Threat Identification
TA6 Documentation and audience communication
Presenting an IoE solution to technical and non-technical audiences
Skills practised: Audience Communication; Documentation & Communication; Feedback Management; IoE Solution Design; Solution Presentation
F205: Data visualisation
TA1 The value and importance of data visualisation
Why visualise, audience needs and the benefits of visual reporting
Skills practised: Big Data Concepts; Data Dashboards; Data Selection & Sourcing; Value & Importance of Data
TA2 Planning for data dashboards
Requirements, KPIs, layout, filters and data sources
Skills practised: Dashboard Design & Purpose; Data Preparation & Processing; Legal & Ethical Considerations; Planning Tools & Strategies
TA3 Techniques for creating a data dashboard
Chart selection, colour, labelling, interactivity and accessibility
Skills practised: Choosing a chart; Dashboard creation features; Dashboard security and access; Data manipulation methods; Data preparation software tools; Importing data for visualisation; Visualisation software tools
TA4 Communicating information and interpreting data
Descriptive statistics, correlation, trends, misleading visuals and conclusions
Skills practised: Communication Principles; Dashboard Communication; Data Analysis Techniques; Data Interpretation; Data Representation & Ethics
TA5 Evaluating the effectiveness of visualisation solutions
Testing with users, measuring against requirements and improvements
Skills practised: Comparing against client requirements; Evaluating a visualisation; Evaluating dashboard effectiveness; Evaluating data preparation; Evaluating HCI and layout; Future development of a dashboard; Testing with users
F206: Data and digital marketing
TA1 Digital marketing fundamentals
Channels, campaigns, audiences and marketing objectives
Skills practised: B2B and B2C approaches; Digital marketing channels; Digital marketing tools; Identifying customers and markets; Marketing approaches and purposes; Purposes of digital marketing; Short term and long term marketing goals; The digital marketing lifecycle
TA2 Data driven digital marketing
Analytics metrics, segmentation, personalisation and A/B testing
Skills practised: Analysing and presenting marketing data; Data collection sources for marketing; Data driven marketing metrics; Interpreting marketing metrics; Presenting marketing analysis results; Segmentation and testing; Social media data aggregation; Using data for targeted campaigns
TA3 Planning digital marketing content
Briefs, content plans, schedules and legal requirements
Skills practised: Campaign Planning; Campaign Strategy; Content Development; Marketing Fundamentals
TA4 Creating content for digital marketing campaigns
Assets, tone, branding, SEO and accessibility
Skills practised: Content formats; Content purposes; Content style and keywords; Content style and language; Creating campaign content; Matching content format to channel; Prototyping content; The value of keywords
TA5 Communicating to stakeholders
Reporting campaign performance to clients and colleagues
Skills practised: Clear presentation and communication; Communicating to stakeholders; Executive summary features; Gathering and using stakeholder feedback; Impact on stakeholders; Reporting campaign metrics to stakeholders; Structuring a pitch to stakeholders
TA6 Reflection and evaluation of working processes
Reviewing decisions, time management and improvements for next time
Skills practised: Boud, Keogh and Walker's reflective model; Evaluating the working process; Evaluating tools and techniques used; Judging overall effectiveness; Lessons learnt and improvements; Reflection and evaluation; Time management in the working process
How CS Ninja supports OCR H019/H119 Data Analytics
- Set homework on a unit or a single teaching area, such as F201 TA3 Big data, machine learning and artificial intelligence.
- Multiple-choice and short-answer questions are marked instantly with explanations.
- Ninja Plus adds exam-style questions with mark schemes for Data Analytics topics; Ninja AI marks the written answers.
- Schedule homework across the course and view results by student and skill.
For teachers
Create a class for your Data Analytics group and choose the F200 or F201 teaching areas you are covering. Short homework sets are a quick way to check theory knowledge between practical lessons, and exam-style questions help prepare for the examined units.
For students
Students practise data analytics theory with generated questions, get instant feedback and meet missed skills again later in the set. Independent study lets them revise any teaching area in their own time.
Tracking progress across 37 topics
Skill-level results show which teaching areas each student understands, helping you plan revision before assessments.
Example OCR H019/H119 Data Analytics questions
Taken from the CS Ninja question bank. Generated questions use different values each time they are set.
Multiple choice · Data Collection & Organisation
How would you classify this data: the number of items in each order?
- Qualitative
- Quantitative
Answer: Quantitative
Multiple choice · Data Collection & Sourcing
Is this primary or secondary data: a survey the analyst runs with the company's own customers?
- Secondary
- Primary
Answer: Primary
Exam-style (Describe) · 4 marks · Ninja Plus and Ninja AI
A retailer holds data about customer transactions. Describe the difference between quantitative and qualitative data, using an example from the retailer's transaction data for each. [4]
Mark scheme includes:
- Quantitative data is numerical and can be measured or counted, e.g. the price of an item or the quantity purchased.
- It can be used in calculations and statistical analysis such as averages.
- Qualitative data is descriptive and non-numerical, e.g. a customer's written feedback comment or product category name.
Related courses
- OCR Cambridge National in IT J836
The OCR Level 1/2 IT course many students take before H019/H119.
- OCR H446 A Level Computer Science
The OCR A Level with related databases, SQL and algorithms content.
OCR H019/H119 Data Analytics is one part of CS Ninja’s wider Computer Science homework platform, which lets one department set auto-marked homework from Year 7 through to A Level.
See all Computer Science and IT courses supported by CS Ninja, read about CS Ninja, or compare school pricing plans.
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