<commit_before>---
layout: full-width
title: Index Map 
# Note that this index page uses a full-width layout!
---

<h1 id="how-do-i-start-learning-data-science">How Do I Start Learning Data Science?</h1>
<p>This is a “hands on” or applied guide to getting started with <em>data science</em>. It begins with what exactly is data science, and how to get the required background and later goes into details of learning and practicing the data science approach to actional insights. After going over the basics of data analysis, it later goes into special topics such as dealing with big data, natural language processing, image classification and many more topics.</p>
<p>While books are great (and are recommended), a large reading lists are daunting and are not conductive to becoming a better data scientist, rather the way to get better is to learn a little, play a little, present results a little, get feedback a little, rinse and repeat! And this guide provides such a framework by explaining topics, providing sample code analysing data (through tutorials)and links to revelant resources along the way!</p>

<p>You can view the topics in the hierarchical tree/ mind map below (coming soon!) or <a href="http://datascienceguide.github.io/outline.html">outline</a> and get started by learning <a href="http://datascienceguide.github.io/what-is-data-science/">what is data science is</a>!</p>

<iframe src="http://bl.ocks.org/mrandrewandrade/raw/f50a0af562b8259ddc0f/" width="100%" height="1000" seamless frameBorder="0" scrolling="no"></iframe>
<commit_msg>made map scrollable<commit_after>---
layout: full-width
title: Index Map 
# Note that this index page uses a full-width layout!
---

<h1 id="how-do-i-start-learning-data-science">How Do I Start Learning Data Science?</h1>
<p>This is a “hands on” or applied guide to getting started with <em>data science</em>. It begins with what exactly is data science, and how to get the required background and later goes into details of learning and practicing the data science approach to actional insights. After going over the basics of data analysis, it later goes into special topics such as dealing with big data, natural language processing, image classification and many more topics.</p>
<p>While books are great (and are recommended), a large reading lists are daunting and are not conductive to becoming a better data scientist, rather the way to get better is to learn a little, play a little, present results a little, get feedback a little, rinse and repeat! And this guide provides such a framework by explaining topics, providing sample code analysing data (through tutorials)and links to revelant resources along the way!</p>

<p>You can view the topics in the hierarchical tree/ mind map below (coming soon!) or <a href="http://datascienceguide.github.io/outline.html">outline</a> and get started by learning <a href="http://datascienceguide.github.io/what-is-data-science/">what is data science is</a>!</p>

<iframe src="http://bl.ocks.org/mrandrewandrade/raw/f50a0af562b8259ddc0f/" width="100%" height="1000" seamless frameBorder="0" scrolling="yes"></iframe>

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