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<span class="cat">Data Science</span>
<h1>Resource guide.</h1>
<span class="blog-date">5 October 2018</span>
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<p>
As I mentioned in <a href="blog-180913.html">Balancing the Boat</a>, I have been following the growth of Data Science for years. I never had the time to dive deep, but the desire was always there. Through the journey of learning about Data Science, I have gathered a number of resources. I hope that by gathering them together here in a one-stop-shop will not only help me, but maybe some others who are looking to learn more as well. My idea is that this post will evolve as my journey continues.
</p>
<b>Twitter:</b>
<ul>
<li><a href="https://twitter.com/gbkgwyneth/lists/data-science">My Twitter list</a></li>
<li><a href="https://twitter.com/kdnuggets">kdnuggets</a></li>
<li><a href="https://twitter.com/PyData">PyData</a></li>
<li><a href="https://twitter.com/AndrewYNg">AndrewYNg</a></li>
<li><a href="https://twitter.com/analyticbridge">analyticbridge</a> (Data Science Central)</li>
<li><a href="https://twitter.com/KirkDBorne">KirkDBourne</a></li>
<li><a href="https://twitter.com/kaggle">kaggle</a></li>
<li><a href="https://twitter.com/IBMDataScience">IBMDataScience</a></li>
<li><a href="https://twitter.com/thisismetis">thisismetis</a> (Metis)</li>
</ul>
<b>Podcasts:</b>
<ul>
<li><a href="http://lineardigressions.com/">Linear Digressions</a></li>
<li><a href="https://twimlai.com/">TWiML & AI</a></li>
<li><a href="http://nssdeviations.com/">Not So Standard Deviations</a></li>
<li><a href="https://open.spotify.com/episode/4a2JLGQjmoGdJQyvF8Oque?si=F70hXtkpSL-5I4ESDHGrPQ">Data Science Ethics</a></li>
</ul>
<b>Blogs:</b>
<ul>
<li><a href="http://www.storytellingwithdata.com/">Storytelling with Data</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/">Analytics Vidhya</a></li>
<li><a href="https://towardsdatascience.com/">Towards Data Science</a></li>
</ul>
<b>Books:</b>
<ul>
<li><a href="https://www.safaribooksonline.com/">Safari Books Online</a></li>
<li><a href="https://jakevdp.github.io/PythonDataScienceHandbook/">Python Data Science Handbook</a> by Jake VanderPlas</li>
<li><a href="http://www.storytellingwithdata.com/book/">Storytelling With Data</a> by Kole Nussbaumer Knaflic</li>
<li><a href="http://shop.oreilly.com/product/0636920033400.do">Data Science from Scratch: First Principles with Python</a> by Joel Grus</li>
<li><a href="https://weaponsofmathdestructionbook.com/">Weapons of Math Destruction</a> by Cathy O’Neil</li>
</ul>
<b>Online courses:</b>
<ul>
<li><a href="https://www.coursera.org/specializations/jhu-data-science">Coursera: Data Science</a></li>
<li><a href="https://www.datacamp.com/">DataCamp</a></li>
</ul>
<b>Meetups (these are local to Atlanta):</b>
<ul>
<li><a href="https://www.meetup.com/PyData-Atlanta/">PyData Atlanta</a></li>
<li><a href="https://www.meetup.com/python-atlanta/">PyAtl: Atlanta Python Programmers</a></li>
<li><a href="https://www.meetup.com/Data-Science-ATL/">Data Science ATL</li>
</ul>
<b>Skill development:</b>
<ul>
<li><a href="https://www.kaggle.com/">Kaggle</a></li>
</ul>
<b>Python libraries:</b>
<ul>
<li><a href="http://www.numpy.org/">NumPy</a></li>
<li><a href="https://pandas.pydata.org/">pandas</a> (Python Data Analysis Library)</li>
<li>matplotlib</li>
<li>altair</li>
<li>seaborn</li>
<li>bokeh</li>
<li>scikitlearn</li>
<li>tensorflow</li>
<li>keras</li>
<li>pytorch</li>
</ul>
<b>Tools:</b>
<ul>
<li><a href="http://jupyter.org/index.html">Jupyter notebooks</a></li>
<li><a href="https://atom.io/">Atom editor</a></li>
<li><a href="https://toolbox.google.com/datasetsearch">Google Dataset Search</a></li>
<li><a href="https://github.com/">GitHub</a></li>
</ul>
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