Guide to getting started! What tools to use.
NB You can easily follow the practicals on GitHub and view the code and outputs without downloading and running any code on your machine. However to get the most of the exercises we recommend that you follow this guide.
Initially we will be looking at Python and using some popular powerful modules like Numpy (to handle multi-dimensional arrays), Pandas to handle and explore datasets, matplotlib (so we can plot our results) and SciKit-Learn (Machine Learning).
Want an easy way to install of these? Then use Anaconda - this is a very popular Python Data Science Platform. Download the distribution here and looking for a guide? View here. We recommend using the Python 3.6 distribution. We have got this working on both Windows and Mac OS without any issues.
We also have our very brief 15 minute guide slides that looks at how to install Anaconda, get the examples and open them in Jupyter.
Jupyter Notebooks are a great way to prototype, play and share code. It is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Its ideal for us to start and share ideas, codes and concepts in the group.
Anaconda installs Jupyter for you. To understand Jupyter a little more check out this guide.
To start it just type the following in the command line:
jupyter notebook
or simply launch it from Anaconda Navigator.
This will start the Jupyter application and you will see it in your browser. The initial page will look very much like a file manager. Just click on an existing notebook to open it or create a new one to get started - notebooks have the .ipynb extension.
A notebook consists a number of cells where you can add code or markdown notes, and running that cell will run the code (and show any outputs) or render your markdown notes.
Useful shortcuts:
- ctrl-Enter to run a cell
- alt-Enter to insert a new cell below
Hop on over to one of the repositories with the examples in GitHub and download it - this will give you copty of the notebooks and any data and support files in a .zip. Unzip and open a notebook it through Jupyter and run some cells!
We recommend starting with The Sigmoid Function which is a gently introduction to running Python in Jupyter to model a common activation function; or Week 1 Poll Data to see how you can load and display data within a notebook.