Data Analytics & AI

Develop the skills needed to turn complex data into clear, actionable insights that support business decision-making.

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To the programme

Duration

~15 months

Standard

Level 4 Data Analyst

Funding Band

£15,000

What you’ll be able to do

Data analysis using industry-standard tools

Analyse datasets using Excel or Google Sheets, SQL and Python to identify patterns, trends and insights.

Data preparation, cleaning and validation

Collect, combine, cleanse and validate data from multiple sources to ensure accuracy, consistency and data quality.

Statistical and analytical techniques

Apply statistical techniques such as A/B testing to compare results, identify meaningful differences and support evidence-based decision making.

Data visualisation and reporting

Create dashboards, charts and reports using BI tools and Python to present insights clearly for review and decision-making.

Insight-to-action translation

Interpret analysis results and develop clear, evidence-based recommendations aligned to business needs.

Secure and compliant data handling

Store, manage and present data responsibly, following organisational policies and relevant legislation.

Tools you'll learn

ExcelGoogle SheetsSQLBigQueryAWSAzureTableauPower BIPowerPointGoogle SlidesPythonPandasMatplotlibSeabornScikit-learnGit

Curriculum

6 Modules · 15 Sprints

01Module 1: Data Foundations3 sprints

Sprint 1: Structured Approach to Data Analytics

Build a strong foundation in how data analytics works by exploring key principles, analytical workflows, essential tools, AI applications and introductory statistical concepts.

Sprint 2: Utilising Spreadsheets for Data Analytics

Develop practical skills in Google Sheets or Excel by creating charts and tables, cleaning data and applying analytical techniques to uncover insights.

Sprint 3: SQL and Databases

Get introduced to SQL across platforms such as BigQuery, AWS or Azure, gaining confidence with SQL fundamentals, core logic and relational database structures.

02Module 2: Programming & Data Processing2 sprints

Sprint 1: First Steps in Programming

Develop essential programming skills by working with variables, functions, loops, conditionals and data structures, and combining these elements into structured programmes.

Sprint 2: Data Processing with Pandas

Apply Python to real datasets by working with files, using Pandas for exploratory analysis and completing a project that reinforces practical data-processing skills.

03Module 3: Data Visualisation2 sprints

Sprint 1: Visualising Data Using BI Tools

Create dashboards in Tableau or Power BI by connecting datasets, adding interactive elements and using visual storytelling to communicate insights effectively.

Sprint 2: Data Visualisation with Python

Create visualisations using Python, progressing from basic to intermediate charting, applying data cleaning and completing a practical analysis project.

04Module 4: Statistics & Machine Learning2 sprints

Sprint 1: Statistical Inference and A/B Testing

Apply core and intermediate statistical techniques by designing and evaluating A/B tests to support evidence-based decision making.

Sprint 2: Machine Learning (Python)

Explore introductory machine-learning approaches by building regression and classification models that support predictive analysis.

05Optional ModulesOptional · 3 sprints

The optional modules give learners the chance to extend their analytical skills beyond the core programme, covering areas such as cohort analysis, retention and churn, funnel analysis, customer segmentation and Customer Lifetime Value. These modules allow organisations to carry out more detailed analysis and explore their data in greater depth.

Retention, Cohorts and Churn

Examine customer behaviour patterns by performing cohort analysis and applying different approaches to measuring retention and churn.

Funnels

Explore funnel analysis to identify conversion patterns, drop-off points and optimisation opportunities.

CLV, Customer Segmentation and RFM

Apply segmentation methods, RFM analysis and Customer Lifetime Value modelling to understand long-term customer performance.

06End Point Assessment (EPA)3 months · 3 topics

When the employer, the learner and the Personal Tutor all agree that the learner is ready, they pass through ‘Gateway’. This means the training is completed and the learner registers for their End Point Assessment (EPA). The EPA consists of: - A professional discussion, underpinned by a portfolio of evidence - Scenario Demonstrations with questioning - Learners will be observed by an independent end point assessor completing two scenario demonstrations which will be supplemented by questioning.

Professional discussion with portfolio of evidence
Scenario Demonstrations with questioning
Level 4 Data Analyst Apprenticeship Certificate