Data Analysis with Python

About Python

Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C or Java. The language provides constructs intended to enable clear programs on both a small and large scale. 

Python libraries exist for almost all computational tasks, which means that unlike specialised languages such as MATLAB and R, a Python program can cover almost all aspects of a computational project. On the other hand through specialised scientifically oriented libraries such as Numpy/Scipy, Pandas, and Matplotlib, Python is able to compete with specialised frameworks at performing numerically intensive tasks or producing publication quality graphs and visualisations.

Registration not yet available. 


This advanced course is aimed at people with prior experience with basic Python.

You should have attended the Introduction to Python course or have equivalent experience with Python. 

Workshop Aims

The course will cover some advanced features of Python, including comprehensions, context managers, and a basic introduction to classes. 

Data analysis with Python will be introduced via the Numpy/Scipy for numerical analysis and Matplotlib for plotting; optional material and exercises for using Pandas and Image Processing libraries will also be included. 

By the end of this course you should be able to write Python scripts that perform complex numerical processing and research-level plots. You will optionally also be able to use Pandas for R-like data analysis and/or Scikit-Image for Image Processing. 

The workshop is open to researchers across the University.


  • Python syntax
  • Data-types and concepts
  • Introduction to scientific libraries
  • Plotting / visualisation with Python
  • Optional "pick and choose" sections including
    • Image Processing
    • R-like data analysis with Pandas
    • Getting started with web interfaces

NOTE: At the end of each session, the workshop team will remain available in order that you can practise your newly learned skills. We recommend that you bring some of your own data so that you can practise the techniques on data that is relevant to your research.


Dec. 2017 (TBC)


1pm - 5pm

Instructor:  Dr Jeremy Metz
Location: Hatherly B12 (TBC)