Setting up your machine for data science in Python

So you're into snakecharming and crystal balls? Consider this the magic supply shop...

Time required: [~20 - 30 minutes]

This is a community-maintained set of instructions for installing the Python Data Science stack. If you'll be using the programming language Python and its related libraries for loading data, exploring what it contains, visualizing that data, and creating statistical models this is what you need. Here's how to get your machine setup properly.

1. Install Anaconda

Anaconda puts nearly all of the tools that we're going to need into a neat little package: the Python core language, an improved REPL environment called Jupyter, numeric computing libraries (NumPy, pandas), plotting libraries (seaborn, matplotlib), and statistics and machine learning libraries (SciPy, scikit-learn, statsmodels). We'll use Anaconda's installer to handle setting up the environment that we'll work in.

In order to keep the size of the download small, we actually use a minimum set of packages called Miniconda.

2. Download and install common packages for data science in Python

3. Run Jupyter notebook!

In this step, we'll make sure everything is working by running the Jupyter Notebook. Jupyter Notebook is a tool for doing interactive data science work in your browser. * In your command prompt with the tutorial environment activated (Note: you'll be able to tell because your command prompt will say (tutorial) at the start of it.) * Type the following command jupyter notebook . * A browser window will open, showing the Jupyer environment. By default, you will be in a file browser view. * In the file browser, find where you have a Jupyter notebook. If you don't have materials for a course or tutorial that you have downloaded, you can download this fun Jupyter notebook and then open it in the file browser. * Click on one of the notebook (*.ipynb) files to get started!

4. To stop Jupyter notebook:

5. To leave the tutorial environment (with all our fun packages) and go back to your normal environment: