Student Tech Guide

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DSI TECHNICAL 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.

REQUIRED TOOLS
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.
Slack - We’ll be using Slack on a daily basis to communicate with each other.

COMMON TOOLS
Anaconda bundles many of the Python packages we’ll be using, including:
Python 2.7: The most widely used, stable, enterprise version of Python.
Ipython / Jupyter & Pandas: Core tools for notebooks & data analysis.
Matplotlib: The king of all python plotting packages.
Gensim: Framework for vector modeling.
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).

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

Atom or Sublime are popular text editors for writing scripts to process data, perform analysis, and
create visualizations.
Chrome is Google's popular web browser, and comes with a complete set of developer tools built-in.

A NOTE ABOUT TECHNOLOGY
Follow the guidelines below to ensure your machine is fully prepared for Data Science:

System Requirements
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.

Mac Users
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:
1.6GHz dual-core Intel Core i5 processor
Turbo Boost up to 2.7GHz
Intel HD Graphics 6000
At least 8GB RAM
128GB flash storage
10+ GB of free disk space

PC Users
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

or Linux users troubleshoot any issues you might encounter. If you choose to use a PC or Linux
machine, you will need to provide your own IT support.
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