Data Science · 8-week live cohort

Data Analysis with Python

Move from spreadsheets to a repeatable analytical workflow in Python.

Learn to clean and combine datasets with pandas, explore patterns with visualisations, use SQL for data access and joins, and automate routine analysis.

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Next cohort details will be published when confirmed.

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The programme standard

Understand it. Practise it. Produce evidence.

ClarityKnow the concepts and reasoning behind the work.
PracticeUse tools and methods in realistic tasks.
EvidenceLeave with practical outputs you can inspect and explain.
How the work fits together

The analytical notebook

A useful Python analysis should be reproducible enough for another person to follow.

The code matters, but the portfolio becomes stronger when the notebook also explains the question, choices and result.
01Load

Bring data into a controlled notebook or script.

02Wrangle

Clean, merge and validate with pandas.

03Explore

Test questions with visualisation and structured analysis.

04Automate

Turn recurring work into reusable code.

Programme overview

Data Analysis with Python

Learn to clean and combine datasets with pandas, explore patterns with visualisations, use SQL for data access and joins, and automate routine analysis.

01
Work confidently in notebooksUse a clear Jupyter or Colab workflow for analysis and documentation.
02
Clean and combine messy dataUse pandas to reshape, merge and prepare realistic datasets.
03
Explore and explain patternsUse visualisation and exploratory analysis to investigate business questions.
04
Automate routine workTurn recurring analysis into scripts that can be rerun and improved.

What you will build

Practical work that makes progress visible.

01 · Output
Reusable pandas cleaning toolkit

Functions and patterns for common data-cleaning tasks.

02 · Output
Exploratory analysis notebook

A documented notebook that tests questions and explains findings.

03 · Output
Visualisation assets

Clear charts created with Matplotlib or Plotly for stakeholder use.

04 · Output
SQL-to-pandas pipeline

A simple workflow for querying, joining and analysing data.

05 · Output
Reporting script

Automation for a recurring analytical task.

06 · Output
Portfolio case study

A polished analysis with code, evidence and a concise narrative.

Programme journey

A clear progression through the work.

Week 1

Python setup and notebook workflow

Establish the environment, syntax and working habits needed for analysis.

Weeks 2–3

Data cleaning and wrangling

Use pandas to inspect, transform, combine and validate datasets.

Week 4

Exploratory analysis and visualisation

Investigate patterns and communicate them clearly.

Week 5

SQL and joins

Connect simple SQL querying to the analytical workflow.

Week 6

Simple modelling

Use practical predictive methods where they genuinely help.

Week 7

Automation and reproducibility

Turn recurring work into reusable scripts and documented workflows.

Week 8

Capstone

Ship a complete analysis and explain the evidence behind it.

Who this is for

Built for people who need the capability in practice.

Early-career analysts
Product and operations professionals
Finance and marketing teams
Graduates and career switchers

How you learn

A live learning rhythm built around practice.

The programme combines structured teaching, guided practice and regular review so the work develops alongside the knowledge.

01Three live sessions weekly
02Recordings available
03Weekly clinic and community support
04Datasets and starter notebooks provided
05Optional bring-your-data clinic
06Anaconda, JupyterLab or Google Colab

Next step

Interested in this programme?

Check the current application status and register your interest. We do not publish cohort dates until they are confirmed.

Next cohort

Next cohort details will be published when confirmed.

Register interest