Computer Setup

Instructions to set up your data science environment.

Contents

Need a Computer?

We have a small collection of older Chromebooks on reserve in the library.

OSX

  1. First things first. Your terminal program allows you to type commands to control your computer. On a Mac, you can open the Terminal by going to your Applications screen and selecting Terminal (it might be in the folder named “Other”). Or, you can open Spotlight (Cmd + Space) and type “Terminal”.

  2. First, let’s install brew if you haven’t done that yet. Homebrew is a program that allows you to easily install other software on OSX. In your terminal, run:

     # This downloads the Ruby code of the installation script and runs it
 /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

    Verify your installation by making sure brew --version doesn’t error at your terminal.

  3. Download and install Anaconda:

     # Uses curl to download the installation script
 curl https://repo.continuum.io/miniconda/Miniconda3-4.3.31-MacOSX-x86_64.sh  > miniconda.sh

 # Run the miniconda installer (you will need to enter your password)
 bash miniconda.sh

  4. Close and restart your terminal.

    Ensure the installation worked by running conda --version.

You may remove the miniconda.sh script now if you’d like since it’s quite large.

Click here to continue to the next part of the setup.

Windows

Getting set up on Windows is especially prone to error if you aren’t careful about your configuration. If you’ve already had Anaconda or git installed and can’t get the other to work, try uninstalling everything and starting from scratch.

Installing Anaconda:

  1. Visit the Anaconda website and download the installer for Python 3.6. Download the 64-bit installer if your computer is 64-bit (more likely), the 32-bit installer if not. You can Google how to check whether your computer is 64 or 32 bit.

  2. Leave all the options as default (install for all users, in the default location). Make sure both of these checkboxes are checked:

    conda4

  3. Install.

  4. Verify that the installation is working by starting the Anaconda Prompt (you should be able to start it from the Start Menu) and typing python:

    conda6

    Notice how the python prompt shows that it is running from Anaconda. Now you have conda installed!

    From now on, when we talk about the “Terminal” or “Command Prompt”, we are referring to the Anaconda Prompt that you just installed.

Click here to continue to the next part of the setup.

Linux

These instructions assume you have apt-get (Ubuntu and Debian). For other distributions of Linux, substitute the available package manager.

  1. You likely already know this if you’re running Linux, but just in case: your terminal program allows you to type commands to control your computer. On Linux, you can open the Terminal by going to the Applications menu and clicking “Terminal”.

  2. Install wget. This is a command-line tool that lets you download files / webpages at the command line.

     sudo apt-get install wget

  3. Download the Anaconda installation script:

     wget -O install_anaconda.sh https://repo.continuum.io/miniconda/Miniconda3-4.3.31-Linux-x86_64.sh

  4. Install Anaconda:

     bash install_anaconda.sh

  5. Close and restart your terminal.

    Ensure the installation worked by running conda --version.

You may remove the install_anaconda.sh script now if you’d like since it’s quite large.

Click here to continue to the next part of the setup.

Creating your environment

These instructions are the same for OSX, Windows, and Linux.

  1. Download the data100 environment.yml from the course repository here or:

     # download via curl
 curl https://raw.githubusercontent.com/DS-100/sp18/gh-pages/environment.yml > environment.yml

 # OR download via wget
 wget -O environment.yml https://raw.githubusercontent.com/DS-100/sp18/gh-pages/environment.yml

This YAML file is what we use to specify the dependencies and packages (and their versions) we wish to install into the conda environment we will make for this class. This ensures everyone in the course is using the same package versions which prevents inconsistent behavior between different versions.

  1. In Terminal, navigate to the directory where you downloaded environment.yml. Run these commands to create a new conda environment. Each conda environment has its own package versions. This allows us to switch between package versions easily. For example, this class uses Python 3, but you might have another that uses Python 2. With a conda environment, you can switch between those at will.

     # sanity check on conda installation
 conda --version
    
 # Create a python 3.6 conda environment with the full set
 # of packages specified in environment.yml (jupyter, numpy, pandas, ...)
 conda env create -f environment.yml

 # Switch to the data100 environment
 source activate data100 # omit the 'source' part on Windows

From now on, you can switch to the data100 env with source activate data100, and switch back to the default env with source deactivate.

Note: If you are on Windows, omit the ‘source’ part and use activate data100 and deactivate.

Working on assignments locally

These instructions are the same for OSX, Windows, and Linux.

To work on assignments, you should download the assignment zipfile (looks like hw1.zip). Then you can unzip the files into a folder of your choosing. The staff recommend that you create a parent folder that holds all the assignments for this class for easier access.

Remember the location of your assignment files because you’ll need to navigate to the folder to open the notebook.

You’ll notice that all the assignments for this class have a folder structure that looks something like:

hw1/
  func.png
  ok_tests/
  images/
  hw1.ipynb
  hw1.ok
  something_cool.csv

The file containing the actual assignment ends in .ipynb (short for IPython notebook). The other files are used for the assignment but you don’t have to open them unless we ask you to.

Opening notebooks locally

To open Jupyter notebooks, you’ll navigate to parent directory of the assignment in your terminal and run:

source activate data100 # omit the `source` part on Windows
jupyter notebook

This will automatically open the notebook interface in your browser. You can then browse to a notebook and open it.

Make sure to always work in the data100 conda environment when you are using jupyter notebooks for this class. This ensures you have all the necessary packages required for the notebook to run.

Verifying Your Environment

You can tell if you are correct environment if your terminal looks something like:

conda-env

Additionally,

conda env list

outputs a list of all your conda environments, and data100 should appear with a * next to it (the active one).

Working on JupyterHub

You have the option of working on your assignments in our hosted JupyterHub environment. If you are working on a Chromebook, this is what you will use throughout the course entirely. The Data 100 JupyterHub can be accessed at http://data100.datahub.berkeley.edu.

From within the JupyterHub, you can edit and execute notebooks in the same environment that you installed above. If you choose to complete assignments locally (outside of JupyterHub), they will still need to be uploaded to JupyterHub for submission.