Welcome! Congratulations on your acceptance to GreyAtom's Data Science Program. We hope you are really excited!! We want you to get ready for the program and have a smooth experience.

In this module we will take you through all the steps that you should complete before you start your program.

This guide covers the following steps that you need to go through to prepare for the class. Review and complete all the steps. Some people may find some tasks very challenging, but go slowly through all of them and you shall be easily able to do it.

• Pre-readings
  • Overview of Linear algebra
  • Overview of Statistics
  • Overview of Python
  • Overview of Machine Learning
• Setting up your local system
  • Setting up your operating system
  • Install python using Anaconda
  • Setting up the version control system

Having trained more than 700+ professionals into various aspects of data science, our first hand experience says that people who were able to complete pre-readings 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. We strongly recommend everyone to go through the mentioned material before coming to the class. For many these would be just revisiting the concepts learned earlier, while for some they would be completely new concepts. We would like to re-emphasize that learners who are visiting these concepts for the first time should dedicate extra time to pre work and get thorough with it.

• Statistics & Probability (Estimated time :10 Hours | Tip - watch the videos on 1.5x speed)
• University of Amsterdam, Basic Statistics

• Basic Linear Algebra for Machine Learning (Estimated time : 4 hours)

• Intro to Python for Data Science (An absolute essential for non programmers)
• Practice Examples in Python

• Udacity, Introduction to Machine Learning
• A Friendly Introduction to Machine Learning - 30 Minutes

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

We recommend you to go through the following content

• Basics of Calculus - Visual Introduction
• Basics of Linear Algebra - Visual Introduction
• More hands on practice with Python
• Practice exercises in probability

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

• Towards Data Science
• KDnuggets

• 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 Windows/ Mac OS/ Ubuntu 16.04 while working through the program. However, we strongly recommend you to use Ubuntu / Mac OS.

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 or higher 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.

• If you are on Windows and want to run Ubuntu you can create a dual boot system. Here is a beginners guide for the same.
• Using VMWare to run Ubuntu 16.04 from inside of Windows is not recommended

Anaconda distribution is the easiest way to install Python along with most popular machine learning libraries that you will be needing for the program. You can download Anaconda 3.6 or higher for you operating system from here. Follow this step by step guide to install the same.

• 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.