Apprenticeship Learner's Portal

In this module, learners will explore data storage fundamentals, including RDBMS such as: SQL Server, SQL Fundamentals, NoSQL databases, data modelling, distributed systems, and cloud implementation.

Hands-on sessions cover database design, access management, issue communication, and querying data with SQL.

This module covers:

• Data processing and transformation
• Python tools (NumPy, Pandas, SciPy)
• Data quality assessment
• Cleansing
• Integration
• Batch vs real-time processing
• Spark
• ETL/ELT
• Cloud platforms
• Azure data engineering
• Pipeline orchestration
• CI/CD
• Hands-on data cleaning with Python libraries.

This module explores the end-to-end process of developing data products. It begins with identifying business needs and evaluating requirements, then focuses on agile frameworks for user requirements and data product design. Best practices in software development are emphasised alongside gap analysis in existing tools.

Planning encompasses testing, data quality, automation, and risk analysis, with considerations for cost, organisational strategies, net-zero technologies, governance frameworks, compliance, security, and scalability.

Data product design, tools, and technologies are explored, including version control systems. Principles of sustainable data products, documentation, standardisation, SDLC, CI/CD pipelines, and data movement methods are covered within an organisational architecture context.

The workplace project involves ‘a significant and defined piece of work’ with real business impact. The business task agreed upon will be relevant for both organisational context and the apprentice’s role, and demonstrate the required skills and knowledge. The project will be completed whilst on programme and prior to End Point Assessment, typically at the place of work.

This module will cover all the necessary knowledge and skills needed to build and test data products.

Topics include structured data extraction, integration platforms, data ingestion optimisation, automation of pipelines, testing, security, user requirements, interfaces, prototype evaluation, and stakeholder collaboration, emphasising continuous improvement, sustainable product development, and version control.

This module introduces learners to Data Operations, covering:

• Technology service management
• Incident response
• Troubleshooting
• Release and deployment methods
• Data pipeline deployment and management
• Monitoring
• Continuous improvement
• Data product evaluation
• Quality assurance
• Root cause investigation
• Forecasting tools
• Machine learning model development
• Data governance
• Further development aligned with operational best practices.

This module will introduce learners to the future of data engineering, this module explores emerging technologies and strategies shaping data management and analysis, empowering personal development through innovative data-driven approaches and insights.