A practical introduction to data analysis and visualisation using the Python programming language.

Python is one of the leading programming languages in data science and is widely used for data analysis, visualisation, statistics and machine learning. This course introduces the core skills needed to work effectively with scientific datasets in Python, with an emphasis on exploratory data analysis, data manipulation and visualisation.

The course is entirely based on open-source software, and all tools used during the training are freely available.

By the end of the course, participants should be able to:

• work confidently with Python and interactive coding environments
• import, inspect and perform quality control checks on tabular datasets
• manipulate and reshape datasets for downstream analysis
• summarise and analyse grouped data
• create and customise data visualisations suitable for scientific communication
• combine datasets using shared identifiers and resolve common data-cleaning issues

Teaching is primarily hands-on, combining short presentations with live coding demonstrations and guided practical exercises.

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== Intended audience ==

This course is suitable for:

• researchers and students who are new to data analysis in Python
• participants interested in practical data science skills for research applications
• researchers working with biological, biomedical or experimental datasets
• participants with some prior programming experience who want to strengthen their Python data analysis skills

== Course fees ==

All fees are per full training day.

{| class="wikitable"
|-
! Category
! Fee
|-
| Industry full charge
| £130.00
|-
| Academic / Government / charity concessionary
| £65.00
|-
| Cambridge University staff members / postdocs / visitors
| £65.00
|-
| Cambridge University registered students
| Free
|-
| Cambridge University registered students non-attendance
| £22.00
|-
| Special events
| Per event
|}

Payment options will be provided in booking confirmation emails sent after registration.

More details…

== General information ==

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