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	<title>Data Science Specialization Course Notes by Xing Su</title>
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				<h1>Data Science Specialization Course Notes</h1>
				<h2>
					Notes for all 9 courses in the <a href="https://www.coursera.org/specialization/jhudatascience/1">Coursera Data Science Specialization</a> from Johns Hopkins University
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					Happy Learning
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				<p>
					All notes are written in <strong>R Markdown</strong> format and encompass all concepts covered in the <a href="https://www.coursera.org/specialization/jhudatascience/1">Data Science Specialization</a>, as well as additional examples and materials I compiled from lecture, my own exploration, <em>StackOverflow</em>, and <em>Khan Academy</em>.
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					They are <em>by no means perfect</em>, but feel free to <a href="https://github.com/sux13"><span class="fa fa-user-plus"></span> follow</a>, <a href="https://github.com/sux13/DataScienceSpCourseNotes/fork"><span class="fa fa-code-fork"></span> fork</a> and/or <a href="https://github.com/sux13/DataScienceSpCourseNotes/pulls"><span class="fa fa-github"></span>  contribute</a>. Please reach out to <a href="mailto:s.xing@me.com">s.xing@me.com</a> if you have any questions.
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					All files are hosted in <a href="https://github.com/sux13/DataScienceSpCourseNotes">this</a> repository.
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							<th align="center">Course</th>
							<th align="center">HTML</th>
							<th align="center">PDF</th>
							<th align="center">Rmd</th>
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							<td align="center">The Data Scientist’s Toolbox</td>
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								<a href="1_DATASCITOOLBOX/Data_Scientists_Toolbox_Course_Notes.html">
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								<a href="1_DATASCITOOLBOX/Data%20Scientists%20Toolbox%20Course%20Notes.Rmd">
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							<td align="center">Getting and Cleaning Data</td>
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								<a href="3_GETDATA/Getting_and_Cleaning_Data_Course_Notes.html">
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							<td align="center">Exploratory Data Analysis</td>
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								<a href="4_EXDATA/Exploratory_Data_Analysis_Course_Notes.pdf">
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							<td align="center">Reproducible Research</td>
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							<td align="center">Statistical Inference</td>
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							<td align="center">Regression Models</td>
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								<a href="7_REGMODS/Regression_Models_Course_Notes.pdf">
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							<td align="center">Practical Machine Learning</td>
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							<td align="center">Developing Data Products</td>
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						<img class="portrait" alt="Xing Su" src="https://media.licdn.com/mpr/mpr/shrink_100_100/p/4/000/172/23d/12d2ade.jpg" width="100" height="100"></a>
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						Xing graduated from Duke University in 2013, worked in consulting in NYC for 16 months, moved to SF to learn data science, and will be launching new cities for Uber in China. You can find him on <a href="https://www.linkedin.com/in/suxing/en">LinkedIn</a>, <a href="https://github.com/sux13">Github</a>, or through <a href="mailto:s.xing@me.com">s.xing@me.com</a>.
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