Gateway Preparation runs across all semesters
Semester 1
This module introduces apprentices to data analysis and helps them develop the core knowledge, workplace behaviours and practical confidence needed for the course. It establishes what data analysis is for (answering questions, solving problems, supporting decisions), how it fits within different kinds of organisations (commercial, public), and how data analysts work with stakeholders to frame the right questions before analysis begins. It also introduces the modern data analyst's working context, where AI-enabled tools increasingly support query writing, exploratory analysis and reporting. Apprentices begin developing the skills to evaluate and integrate these tools appropriately into their own practice, while retaining professional accountability for the outputs produced. As the opening module of the apprenticeship, it introduces a number of topics that subsequent modules will develop in depth: databases, data preparation, statistical analysis, and visualisation.
This module gives apprentices the database and data-acquisition skills that underpin every other activity a data analyst performs. Real data analysis depends on being able to access, query, combine and extract data confidently and reliably, and most of that work happens in databases. This module covers relational database fundamentals, Structured Query Language (SQL) as the data analyst's primary technical skill, and the practical work of acquiring data from organisational systems via APIs, exports, scripts and queries, alongside AI-assisted approaches such as natural language querying. It also introduces the distinction between an ad hoc AI-generated query and a deterministic tool built with AI assistance, and the trade-offs between them in terms of reliability, auditability and reproducibility. The emphasis is on the data analyst's fluency with databases: not as a database administrator, but as someone who can navigate organisational data architecture, write the queries they need, judge when and how to use AI-assisted acquisition appropriately, and acquire the data their analysis requires. This is the technical heart of the early apprenticeship and sets up the remaining modules of the course.
Semester 2
This module covers a core craft in data analysis: the disciplined, ethical, and secure handling of real-world data as it is prepared for analysis. Most data analysis projects fail not because the analysis itself is wrong, but because the data feeding it is messy, ungoverned, poorly understood, or its limitations and confounders go unrecognised, including when that data has been generated or shaped by AI-assisted tools. The module teaches the practical skills of data profiling, quality assessment, cleansing, transformation and classification (approached with the curiosity to notice what doesn't look right and ask why) alongside the legal and ethical responsibilities, including data protection and security, that apply when handling organisational data, and the additional scrutiny required when working with AI-generated or AI-assisted data workflows. It is the most ethically substantive module in the course — K15 (ethical aspects of data use) is owned here, alongside K1 (legislation) and K2 (security policies), because this is where apprentices first take responsibility for handling real data. The module sets up the analytical work of Modules 4 and 5 (Statistical Analysis and Modelling and Data Visualisation, Dashboarding and Insight Communication) by ensuring apprentices arrive with clean, fit-for-purpose data and the disciplined habits to keep it that way.
This module gives apprentices the statistical and analytical foundations that distinguish a data analyst from someone who simply queries databases. It covers descriptive statistics for understanding data, inferential statistics for testing hypotheses, predictive analytics for forecasting and modelling, and the broader landscape of descriptive, predictive and prescriptive analytics. AI tools can apply most of these techniques quickly, but they are far less reliable at choosing the right technique for a given question or interpreting what the result actually means, so the module puts real weight on critically evaluating both AI-assisted and traditional analysis. Apprentices practise spotting analyses that look sound on the surface but have real methodological problems underneath, and learn to ask what technique fits the question, not just how to run it. The emphasis throughout is on practitioner-level statistical thinking, not a maths module, that prepares apprentices to apply analytical techniques to real organisational questions, interpret results critically, and recognise the limits of what their analyses can support. The module is the analytical heart of the course and sets up Module 5 (Data Visualisation, Dashboarding and Insight Communication) where these insights are communicated.
Semester 3
This module covers the data analyst's most visible professional output: visualisations, dashboards, reports and insight communications that turn analysis into business or service value. Effective data analysts produce findings; effective data analysts who get promoted communicate findings in ways that decision-makers can act on. The module teaches visualisation principles, dashboard design, report writing, tool fluency in contemporary BI platforms (Power BI, Tableau), and the communication craft of turning analysis into insight for different audiences. It also covers AI-assisted visualisation and insight generation as an emerging professional practice, including how to use these tools critically rather than accept their output uncritically, and the user experience and domain context considerations that distinguish good analytical outputs from ones that look polished but miss the point. The module is the natural culmination of the taught course - by the end, apprentices can take a workplace question through to a delivered insight that lands with stakeholders.
This is the synoptic project module for the apprenticeship. Apprentices consolidate learning from all five modules into a substantial work-based project demonstrating competence across the full ST0118 standard, and prepare for End Point Assessment under the standard's published EPA plan. The module is the principal vehicle for EPA preparation and contributes 20 credits to the LSBU Certificate of Higher Education in Data Analytics
This is primarily a consolidation and EPA-preparation module rather than a teaching module: the substantive knowledge, skills and behaviours were developed in the five preceding modules. It does, however, include a short series of targeted workshops on project management, synthesising the end-to-end analytical pipeline, professional report writing, and portfolio construction: skills needed at the scale of a substantial synoptic project that go beyond what any single earlier module required. The module is a structured 3-month period in which the apprentice produces, refines and presents the workplace project that forms the basis of EPA, supported by these workshops, individual tutorial supervision, structured mock assessments, and Skills Reviewer engagement.
