Discovering your passion

Data Analytics

Year 12 – 13

Data Analytics

Key Stage: KS5

Exam Board: OCR

Qualification Gained: Level 3 Alternative Academic Qualification Cambridge Advanced National in IT: Data Analytics

Assessment Breakdown:

Entry Requirements: Eight or more GCSEs at grades 9-4, including Grade 5 in English and Maths. If you have studied GCSE or BTEC Level 2 ICT or Computer Science, you should have achieved a minimum Grade 5 or Merit. Any prior ICT skills or knowledge will be beneficial.

To develop confident, data-literate learners who can analyse information, solve problems and communicate insights using digital tools. The course equips students with the analytical thinking and technical skills needed to interpret data and make informed decisions in an increasingly data-driven world.

The Data Analytics curriculum at Hammersmith Academy focuses on helping students understand how data is collected, analysed and used to support decision-making across a wide range of industries. Through practical and applied learning, students develop the ability to work confidently with datasets, identify patterns and present insights using professional digital tools.

Students gain hands-on experience in spreadsheet modelling, data visualisation and the data lifecycle, learning how data moves from collection and storage through to analysis and communication. The course also introduces emerging technologies such as big data and machine learning, helping students understand how modern organisations use large datasets to drive innovation and strategic decision-making.

At Key Stage 5, students follow the OCR Level 3 Advanced National in IT: Data Analytics, which combines externally assessed examinations with internally assessed project work. This structure enables students to demonstrate both theoretical understanding and practical technical skills while developing confidence in using industry-standard tools such as Excel for modelling and data analysis.

In Year 12, students build the core foundations of data analytics. They explore how data is collected, processed and interpreted while developing practical skills in spreadsheet modelling and data visualisation. Students are introduced to concepts such as big data and machine learning, learning how organisations use advanced data systems to identify patterns and trends in complex datasets.

In Year 13, students extend these skills through more independent work and deeper analysis of real-world datasets. They refine their ability to model data, interpret results and communicate insights through dashboards, reports and visualisations. This progression allows students to apply their knowledge to more complex data problems and prepares them for further study, apprenticeships or careers in the rapidly growing digital and data sector.

Throughout the course, the curriculum emphasises transferable digital skills including logical thinking, precision, ethical data use and problem-solving, ensuring students develop the confidence and capability to work with data in a variety of professional contexts.

Year-by-Year Curriculum

Year 12

Module 1-2: Fundamentals of Data Analytics and Spreadsheet Data Modelling
Students develop an understanding of how data is collected, stored and analysed while building practical spreadsheet modelling skills used to interpret datasets.

Module 3-4: Big Data and Machine Learning, Spreadsheet Data Modelling and Data Visualisation
Students explore how organisations use large datasets and emerging technologies while developing techniques for modelling and visualising data effectively.

Module 5: Big Data and Machine Learning and Data Visualisation
Students deepen their understanding of how complex datasets are analysed and presented using visual tools to communicate trends and patterns.

Module 6: Data Visualisation
Students focus on designing dashboards and visual representations that clearly communicate insights from data.

Year 13

Skills Gained

Students develop the ability to:

Create and manage databases and digital data systems
Analyse how organisations use data and digital technologies
Model and interpret datasets using spreadsheet tools
Understand hardware, software and emerging technologies such as AI and robotics
Apply analytical thinking and problem-solving to real digital scenarios

Partnerships & Enrichment

Students benefit from enrichment opportunities that connect digital learning with real-world industry insight. Key enrichment opportunities include:

WCIT Dinner Debate

These experiences provide students with exposure to technology professionals, current digital issues and discussions around the future of technology and data.