This is material used for doing a course introducing how to do data science with Python and Jupyter. The material has been presented at several occasions, and will be expanded on whenever I have the time or need (but please feel free to contribute if you are interested, see License below).
So far I have given courses at
The links are to tags showing the material as it was at the time of the presentation.
Install Anaconda, Python 3.x version. Anaconda is an open data science platform which includes Python and most packages necessary for doing data science. Anaconda can be installed even without root/administrator privileges.
The course is made available as a series of Jupyter Notebooks. The notebooks can be read directly here at Github, but you will have a better, more interactive experience by downloading them and running the locally. How to do this is explained below.
This short course is not a course in Python, the programming language. Instead we aim to show you how Python has become a full-blown platform for doing data science.
The following short lessons are available:
- Anaconda
- Jupyter Notebooks
- Working with JSON-data
- Reading files
- Numerical data in numpy
- Pandas and time series
- Plotting data
- Geospatial data
- Space-geodetic data
- Distributing Jupyter notebooks
- Further resources
The notebooks can be downloaded from this github-page, by scrolling to the top,
click the green Clone or download-button and then clicking the Download ZIP
link.
After downloading the zip-file, unpack it on your computer. Then open a terminal (on Windows you should open a Anaconda terminal) and type
jupyter notebook
This will start a local webserver, so the terminal will print some messages to that effect. You do not need to worry about these message. Furthermore, your default web browser will open up with a new window showing a file browser. You can then navigate to the folder you just downloaded, and click on any of the files to see the content.
If you want to reuse this material, feel free to fork it. If you find any errors or have suggestions for improvements raising an issue or sending a pull request would be very welcome. Thank you for your interest.