Data Career Programme: Data Analytics, Data Science & AI

What you will learn

1. 01

   Data Foundations & Excel (Month 1)

   Master data literacy, spreadsheet calculations and analytical question framing.

   • Core formulas, cell references, lookups (XLOOKUP, VLOOKUP) and data cleaning.
   • Descriptive statistics, outlier detection, data distributions and KPI dashboard architecture.
   • Multi-sheet Power Query automation and GitHub portfolio setup.
   • Project Deliverable: Retail sales and store performance KPI dashboard.
2. 02

   SQL for Analysts (Month 2)

   Query, filter and aggregate data from real-world relational databases.

   • Relational database fundamentals, schemas, MySQL Workbench and query filtering.
   • Multi-table INNER, LEFT, RIGHT joins, GROUP BY aggregations and CASE logic.
   • Common Table Expressions (CTEs), subqueries and window functions (ROW_NUMBER, RANK, LAG, LEAD).
   • Project Deliverable: Healthcare and NHS service demand investigation.
3. 03

   Python & Version Control (Month 3)

   Programmatic data manipulation, automation and professional developer workflows.

   • VS Code setup, Git workflow, repositories, branches and pull requests.
   • Python fundamentals, data structures, control flow, functions and error handling.
   • End-to-end data manipulation with pandas, NumPy, and visualisations with Matplotlib and Seaborn.
   • Project Deliverable: E-commerce and digital marketing multi-channel funnel analysis.
4. 04

   Power BI & Data Storytelling (Month 4)

   Business intelligence data modelling, DAX measures and executive dashboard design.

   • Star schema data modelling, fact and dimension tables, relationships and cardinality.
   • DAX calculations, row and filter context mastery, and time intelligence measures (YTD, YoY).
   • Executive dashboard design, drill-throughs, tooltips and scheduled refresh in Power BI Service.
   • Project Deliverable: Financial services customer and product performance dashboard.
5. 05

   Data Science Foundations (Month 4-5)

   Statistical inference and machine learning models for predictive analysis.

   • Statistical inference, hypothesis testing, confidence intervals and A/B test analysis.
   • Supervised machine learning: linear regression, logistic regression, decision trees and random forests.
   • Model evaluation, confusion matrix trade-offs, k-means clustering and feature engineering.
   • Project Deliverable: Telecoms customer churn prediction and retention model.
6. 06

   Applied AI for Data (Month 5)

   Modern generative AI, prompt engineering, vector search and data applications.

   • Large Language Model (LLM) fundamentals, prompt engineering and structured JSON outputs.
   • Calling LLM APIs in Python, vector embeddings, and Retrieval-Augmented Generation (RAG) tools.
   • Building and deploying interactive data web apps with Streamlit.
   • Project Deliverable: AI-powered document intelligence application.
7. 07

   Capstone Job Simulation (Month 6)

   A multi-source industry simulation delivered as a real first month in the job.

   • Scoping ambiguous business requests from multi-source messy data.
   • End-to-end data pipeline creation combining SQL, Python, Power BI and an AI layer.
   • Professional stakeholder presentation and documentation.
8. 08

   Career Launch (Month 6)

   Comprehensive career coaching, technical interview prep and application strategy.

   • ATS-optimised CV building tailored to target data roles.
   • LinkedIn and portfolio optimisation to attract recruiter inbound.
   • Technical SQL tests, case study practice and STAR behavioural mock interviews.