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doc: re-order table of contents #15

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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -17,7 +17,7 @@ Remember - If you’re not prepared to be wrong, you’ll never come up with any
* [Start](#start)
* [Data Science Courses](#data-science-courses)

1. [Data Pipeline & Tools](#data-pipeline--tools)
2. [Data Pipeline & Tools](#data-pipeline--tools)
* [Python](#python)
* [Data Structures & CS topics](#data-structures--cs-topics)
* [Statistics](#statistics)
Expand All @@ -37,12 +37,12 @@ Remember - If you’re not prepared to be wrong, you’ll never come up with any
* [Data Sources](#data-sources)
* [New Data Tools](#new-data-tools)

1. [Product](#product)
3. [Product](#product)
* [Product Metrics](#product-metrics)
* [Team Communication & Business Tools](#team-communication--business-tools)
* [Best Practices](#best-practices)

1. [Career Resources](#career-resources)
4. [Career Resources](#career-resources)
* [Data Science Career Path](#data-science-career-path)
* [Types of Data Scientists](#types-of-data-scientists)
* [Data Science Applications/Use Cases](#data-science-applicationsuse-cases)
Expand All @@ -53,11 +53,11 @@ Remember - If you’re not prepared to be wrong, you’ll never come up with any
* [Data Science Presentations](#data-science-presentations)
* [Relevant Business Processes](#relevent-business-processes)

1. [Open Source Data Science Resources](#open-source-data-science-resources)
5. [Open Source Data Science Resources](#open-source-data-science-resources)
* [Additional Open Source Content](#other-open-source-data-science-content)
* [Auxiliary Content & Apps](#auxiliary-content--apps)

1. [About Me](#about-me)
6. [About Me](#about-me)

## Data Science Getting Started
Data Science is a multidisciplinary field covering at the very minimum - statistics, programming, machine learning [Drew Conway's venn diagram](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) or [Cheat Sheet of a Modern Data Scientist](http://www.marketingdistillery.com/2014/08/30/data-science-skill-set-explained/). These topics are covered throughout this repo. I personally find the best way to learn a topic is to get my hands dirty quickly - with that in mind I would get to work in python and then implement different tools or theory into my toolkit as they are understood. If you haven't used python before I would strongly urge you to use the codecademy course to familiarize yourself with the content and how to program. Good luck and have fun.
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