This guide covers the following steps that you need to go through to prepare for the class.

• Setting up your operating system
• Setting up the version control system
• Pre-Reading Material
• Community and Meetups

The material in the Pre-Reading Material section is optional, but highly recommended.

• Minimum system requirements for the laptop to be used during the course - 64 bit system, minimum 4 GB RAM (8GB preferred), i3 and above processor

• You can use either the Windows/ Mac OS/ Ubuntu 16.04 while working through the program.

• A note on hardware: your machine should have the amd64 architecture. If you are not using an ancient machine, you should be fine.

We'll be using Homebrew to install and manage packages.

• Install xcode using this guide
• Install brew using this guide

You are already using Ubuntu? Just make sure you are using 16.04 LTS and you are all set.

In case you are new to Ubuntu, here is a guide to get started.Installation related help available on Ubuntu Forums and StackOverflow

You can use any given Windows version: Win7, Win8/8.1 or Win10.

Using VMWare to run Ubuntu 16.04 from inside of Windows is not recommended.

• Install Git and create a GitHub account.
• Hands on Introduction to using Git and GitHub, video series (27 minutes)

Two companion blog posts:

• Simple guide to forks
• Git quick reference for beginners.

Having trained more than 700+ professionals into various aspects of data science, our first hand experience says that people who were able to do this pre-work before coming to the classroom had very different outcomes versus people who did’nt.

Here are few resources that help you stand out from the rest

• Basics of Statistics & Probability - 10 Hours (Tip - watch the videos on 1.5x speed)

• Python Code Academy Course An interactive, in-browser tutorial from Codecademy with 20+ modules and 200+ exercises. It is geared toward novice programmers

• A Friendly Introduction to Machine Learning - 30 Minutes
• Introduction to Machine Learning - Udacity
• A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library - 15 Minutes

We will share many more, when in the course, but here are some very good starting points

• KDnuggets
• The Guardian
• yhat

Keep up with the latest in Data Science. Join & Attend the DataGiri Meetup

www.meetup.com/datagiri

We've all got plenty of neuroplasticity left in us! No matter your background, you can learn what you want to learn. Believe that you can, and believe that those around you can too.

Reading is one way of getting you to think about things that you wouldn't otherwise. Writing encourages thinking. Discussion encourages thinking. Being active encourages thinking. Do things! Try things! Think about things!

There's so much to know! We all learn from one another. Embrace opportunities to learn from others, and opportunities to share what you know.

We want to know what you're thinking