This course follows on directly from Introduction to Python, aiming to bridge the gap between a beginner-level course and the specific computing resources that you need for your research.
We have three sessions to work together, with the following aims:
- Improve your confidence in writing python code.
- Introduce three essential packages for scientific computing in python:
numpy,matplotlibandpandas. - Practice working with a data set to carry out some basic analysis and visualisation tasks.
At the end of these sessions, we hope that you will feel better prepared for further training in scientific computing (e.g. machine learning, statistical modelling, simulation etc.)
This is a new course, and we are very grateful for your questions and feedback on the content and delivery so that we can continue to improve the training that we offer. Please email j.pinney@imperial.ac.uk with your comments and suggestions.
We will be working with python using jupyter notebooks. The easiest way to access jupyter is via the Anaconda platform.
Please install Anaconda from https://www.anaconda.com in advance of the first session.
Download this repository to your computer as a ZIP file and unpack it.
Open JupyterLab (within Anaconda) and navigate to the unpacked directory to work with the .ipynb notebooks.
Alternatively, you can run the notebooks online using Binder:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.