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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Frutiger 45 Light";color:#1F497D">The following message is being forwarded to you at the request of Dr. Lonnie Welch.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Frutiger 45 Light";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black">DATA SCIENCE: Models, Processes and Applications<o:p></o:p></span></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black">Computer Science 4900/5900 (Class Number: 2299/13370)<o:p></o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p><span style="text-decoration:none"> </span></o:p></span></u></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black">Professor L. R. Welch<o:p></o:p></span></i></p>
<p class="MsoNormal" align="center" style="text-align:center"><i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><a href="mailto:welch@ohio.edu">welch@ohio.edu</a><o:p></o:p></span></i></p>
<p class="MsoNormal"><b><u><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p><span style="text-decoration:none"> </span></o:p></span></u></b></p>
<p class="MsoNormal"><b><u><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p><span style="text-decoration:none"> </span></o:p></span></u></b></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">A Harvard Business Review article convincingly argues that
</span><i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black">Data Scientist</span></i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"> is one of the most exciting jobs of the 21st Century:<a style="mso-footnote-id:ftn1" href="#_ftn1" name="_ftnref1" title=""><span class="MsoFootnoteReference">[1]</span></a>
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal" style="margin-left:.5in"><i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">“A data scientist is a high-ranking professional with the training and curiosity to make discoveries in the world of
big data. More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless
data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists
help decision makers shift from ad hoc analysis to an ongoing conversation with data.
</span></i><i><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black">As they make discoveries, they communicate what they’ve learned and suggest its implications for new strategies and directions. Often they are creative in displaying information
visually and making the patterns they find clear and compelling. They advise on the implications of the data for products, processes, and decisions.
<span style="background:white">What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.”</span><o:p></o:p></span></i></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">Students who complete this course will gain a thorough understanding of computational techniques and processes for data science by implementing them
and by applying them to solve real world problems. Students will gain not only an understanding of data science models and methods, but they also will learn how to perform analyses to answer domain research questions and to effectively communicate insights
that result from their analyses. Participants will learn to perform the following specific aspects of the data science process:
<o:p></o:p></span></p>
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<![if !supportLists]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><span style="mso-list:Ignore">(1)<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">computational modeling (including graph analytics and classification trees/forests);
<o:p></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left:.5in;mso-add-space:auto;text-indent:-.25in;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><span style="mso-list:Ignore">(2)<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">statistical significance testing;
<o:p></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left:.5in;mso-add-space:auto;text-indent:-.25in;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><span style="mso-list:Ignore">(3)<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">data cleaning, preprocessing, and exploration;
<o:p></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left:.5in;mso-add-space:auto;text-indent:-.25in;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><span style="mso-list:Ignore">(4)<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">visualization; and
<o:p></o:p></span></p>
<p class="MsoNormalCxSpMiddle" style="margin-left:.5in;mso-add-space:auto;text-indent:-.25in;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><span style="mso-list:Ignore">(5)<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">interpretation.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black;background:white">The Bureau of Labor Statistics (BLS) classifies
<i>data scientists </i>as Computer and Information Science Research Scientists. The job growth rate is projected as 16% from the present through 2028. The current median pay is $118,370 per year. The BLS describes the specialty of
<i>data science<b> </b></i>as follows. Computer and information research scientists write algorithms that are used to detect and analyze patterns in very large datasets. They improve ways to sort, manage, and display data. Computer scientists build algorithms
into software packages that make the data easier for analysts to use. For example, they may create an algorithm to analyze a very large set of medical data in order to find new ways to treat diseases. They may also look for patterns in traffic data to help
clear accidents faster.</span><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><a style="mso-footnote-id:ftn1" href="#_ftnref1" name="_ftn1" title=""><span class="MsoFootnoteReference"><span style="font-size:8.0pt;color:black">[1]</span></span></a><span style="font-size:8.0pt;color:black">
</span><a href="https://nam03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhbr.org%2F2012%2F10%2Fdata-scientist-the-sexiest-job-of-the-21st-century&data=02%7C01%7C%7C6c6838a552024590b56908d75ec5fd51%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C637082076752915457&sdata=uoY1%2FjpERkiOKLio9LaodUYDGAwBcP4WzoFB8tQs664%3D&reserved=0" originalsrc="https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century" shash="cQ7TkQn64ZTRDmtumWlJEG1PocmztecEbF6bMm2eGI6DSpDJl/utP4PbL043ckkxavMb47CzylB3Z+IcO4eK68IvpW1q0yEq/aenmuSS9mo4JK3ESo0qgdtF29amWiGnOh2EAtWZBV860Erk/dKDkoLZH+uTNY4Sed9qK1eLbTw="><span style="font-size:8.0pt">https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century</span></a><u><span style="font-size:8.0pt;color:black">.</span></u><o:p></o:p></p>
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