Student Tech Guide

Note: The following is a Markdown formatted readme file of this Google Document that should be sent

to students by local teams upon signing up for the course.

Before the course starts, you may want to familiarize yourself with the following technologies:

Anaconda - We will be using Anaconda as our primary development environment.

Python 2.7 - We will be using Python & its packages as our primary language.

Github - We’ll be using Github on a daily basis to store and share our code.

Git (mac) / Git Bash (pc) - You will also need to install Git command line tools for your OS.

Postgres - We’ll be using Postgres for local SQL-based data storage.

NLTK & Spacy: Used for natural language processing.

NumPy: Array processing tool.

Scikit-learn: Modules for machine learning & data modeling.

SciPy: Scientific library for python.

Seaborn: Statistical data visualizer.

Pip & Setuptools: package installer & version manager (Mac only).

PyMC: common stats tool for simulation and optimization.

Sqlite: Standalone, lightweight SQL database engine.

Statsmodels: Simple statistical computation (used with SciPy).

These tools aren't specifically required, but are highly recommended.

Chrome is Google's popular web browser, and comes with a complete set of developer tools built-in.

Follow the guidelines below to ensure your machine is fully prepared for Data Science:

Make sure your machine is running with administrator permissions and has at least 10+ GB of free disk

space. We also recommend that you use a laptop with a 13-inch screen or larger in order to do your best

work. In our experience, students with an 11-inch screen have a harder time in class.

General Assembly is a Mac-friendly organization. Our instructors will be teaching the course using Macs, so

we strongly recommend students use a Mac with OS X 10.11 (“El Capitan”) in order to run all of the

programs necessary for the course. This rules out some older MacBooks.

Check the following specs to make sure your machine can provide you with the performance you’ll need in

this course:

While you can be a data scientist with any machine, some students have found compatibility issues with

older versions of Windows. While you can be a data scientist with any machine, unfortunately, there are a

number of compatibility issues with Python libraries and older versions of Windows. For example, Python

and Anaconda users have identified multiple issues with Windows 7 x64 machines.

Therefore, we strongly recommend that PCs users adopt the latest version of Windows** (“Windows 10”).

PC users on older machines may consider installing a Virtual Machine like Oracle’s Virtualbox and running

Anaconda in a Linux environment via Ubuntu Desktop. See more information here.

Please note that our instructors will be conducting the course using Macs, and may not be able to help PC

A NOTE ABOUT TECHNOLOGY

System Requirements