Python for Data Analytics
Learn Python for data analytics using real NHS emergency department data — from Google Colab setup to AI-augmented analysis with Gemini.
Python is the most in-demand skill in data analytics. This module teaches you to use it the way analysts actually do — not through abstract exercises, but through a real-world project analysing NHS England emergency department performance data across 198 hospitals and 25 months. You will work in Google Colab, use pandas to clean and join real datasets, build visualisations, and deliver a stakeholder report in Excel and PowerPoint. Gemini AI is integrated throughout as a professional copilot. And by the final lesson, you will know how to present your work to employers — with a polished LinkedIn profile, a GitHub portfolio, and the language to talk about your project in interviews. No prior programming experience required.
What does this module teach?
Every lesson in this module is built around four pillars. Technical skills alone do not make a data analyst job-ready — you need to think in business terms, work alongside AI tools intelligently, and be able to present yourself and your work with confidence.
| Pillar | What it means in this module |
|---|---|
| Technical Skills | Python, pandas, Google Colab, data wrangling, joins, aggregation, visualisation, Excel reporting — the hands-on skills employers test in interviews |
| Business Acumen | Every lesson opens with a real NHS business scenario. You learn to frame analysis as answers to business questions — not just code that runs |
| AI Literacy | Gemini AI is your copilot from Lesson 2. You learn to use it to accelerate analysis, generate and verify code, and communicate findings — critically, not blindly |
| Personal Branding | Your NHS project becomes a portfolio piece. You learn to publish it on GitHub, write about it on LinkedIn, and talk about it in interviews — turning coursework into career capital |
What's in this module?
This module contains 13 lessons covering Python setup in Google Colab, pandas fundamentals, data wrangling, consistency checks, combining datasets, deriving variables, groupby aggregation, data visualisation, Excel reporting, and AI-augmented analysis with Gemini. Students work on a single real-world project throughout — analysing NHS A&E quality indicators — and graduate with a complete portfolio piece including annotated notebooks, an Excel report, and a stakeholder PowerPoint. The final two lessons focus on personal branding and career positioning: building a GitHub portfolio, writing a compelling LinkedIn profile, and presenting analytical work to employers.
Lessons
| # | Lesson | What you learn |
|---|---|---|
| 1 | Intro to Programming for Data Analysts | Why Python, how it fits alongside Excel and SQL, setting up Google Colab |
| 2 | Google Colab & Python Data Types | Libraries, integers, floats, strings, booleans — your project brief introduced |
| 3 | Intro to Pandas | Loading NHS data, DataFrames, descriptive statistics with real A&E data |
| 4 | Data Wrangling & Subsetting | Renaming columns, converting data types, filtering, creating subsets, exporting CSV |
| 5 | Data Consistency Checks | Missing values, suppressed data, duplicates — what they mean in an NHS context |
| 6 | Combining & Exporting Data | Merging three NHS files with left and inner joins, understanding unmatched rows, pickle format |
| 7 | Deriving New Variables | If-statements, for-loops, loc() — creating NHS performance flags and clinical thresholds |
| 8 | Grouping & Aggregating | groupby(), agg(), transform() — ranking and benchmarking NHS Trusts by region |
| 9 | Intro to Data Visualisation | Bar charts, line charts, histograms, scatter plots — built around real NHS business questions |
| 10 | Coding Etiquette & Excel Reporting | Clean code practices, population flow diagram, 7-sheet stakeholder Excel report |
| 11 | AI-Augmented Analytics with Gemini | Prompt engineering for analysis, AI-generated code verification, narrative writing with Gemini |
| 12 | Portfolio Project | End-to-end NHS analytics project, polished notebooks, GitHub, stakeholder presentation |
| 13 | Personal Branding for Data Analysts | LinkedIn profile, GitHub presence, interview preparation, career positioning |
What will you build?
Every lesson builds one piece of your final project. By the end of the module you will have produced three deliverables — the same outputs a professional data analyst would hand to a client:
| Deliverable | What it contains |
|---|---|
| Google Colab Notebooks | Annotated, portfolio-ready notebooks covering data cleaning, joining, analysis, and visualisation |
| Excel Report (7 sheets) | Title page, population flow, consistency checks, wrangling steps, column derivations, visualisations, recommendations |
| PowerPoint Presentation | 5–7 slide stakeholder deck presenting key findings and recommendations from your NHS analysis |
Frequently asked questions
Do I need programming experience to take this module?
No. This module is designed for beginners and professionals upskilling from Excel and SQL. Prior completion of Module 3 (SQL) or equivalent analytical foundations is recommended but not required.
What tool do I use for Python in this module?
Google Colab — a free, browser-based Python environment. No installation required. You access it at colab.research.google.com using a standard Google account.
What dataset is used in this module?
Real NHS England A&E Quality Indicators data — 94,720 rows covering emergency department performance across 198 organisations from December 2023 to December 2025. The dataset is freely available on the Lumen GitHub repository.
What will I produce by the end of this module?
A complete analytics project including annotated Google Colab notebooks, a 7-sheet Excel report, and a PowerPoint presentation suitable for a stakeholder audience — all based on real NHS data and ready to share on GitHub or LinkedIn as a portfolio piece.
Is AI taught in this module?
Yes. Gemini AI is integrated throughout the module as a professional copilot from Lesson 2 onwards, and Lesson 11 is dedicated to AI-augmented analytics — covering prompt engineering, code generation, and using AI to interpret and communicate data findings. The focus is on using AI critically, not blindly.
Does this module help with job searching and personal branding?
Yes. Lesson 12 focuses on building a portfolio project and publishing it on GitHub. Lesson 13 covers personal branding for data analysts — LinkedIn profile optimisation, presenting your project in interviews, and career positioning. Every technical project in this module is designed to be portfolio-ready from day one.
How long does this module take?
Each of the 13 lessons comprises approximately 1 hour of reading and 2 hours of exercises — around 39 hours of learning in total. Most students complete the module over 4–6 weeks alongside other commitments.
Why NHS data?
Healthcare is one of the largest and fastest-growing sectors for data analytics. NHS England publishes high-quality, real operational data under an open licence — making it ideal for teaching. The business questions it raises (wait times, reattendance rates, regional performance gaps) are immediately understandable and professionally relevant, whether you plan to work in healthcare or not.
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