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Beecher.html
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<!DOCTYPE HTML>
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<h2>Archaelogical Analysis</h2>
<p>A descriptive analysis of animal bones from a Historic Archaeological site in Louisville, Kentucky.</p>
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<h3 class="major">Beecher Faunal Site</h3>
<p>This analysis was conducted on an archaeological faunal assemblage recovered from a historic site in
Louisville, Kentucky. Reflecting the cultural diversity of its inhabitants, including African-American,
Western European American, and Eastern European Immigrants, the assemblage offers insights into past meat
consumption practices. Composed predominantly of waste food remains, with a substantial number of mammal,
bird, and fish specimens, the assemblage provides a rich source for historical interpretation.
</p>
<h3 class="major">Python and Tableau Expressions</h3>
<p>The first task was to use python to create the appropriate grouping classes for the different populations.
The groups are <li>African Professionals</li><li>African Working Class</li><li>European Professionals</li>
<li>Immigrant Working Class</li></p>
<img src="images/Beecher_Python.png" style="width: 850px; height: auto; display: block; margin: 0 auto;">
<p></p>
<p>Then, getting appropriate proportions in a dynamic way required Fixed Level-Of-Detail (LOD) Expressions
in Tableau's convenient table calculations. </p>
<img src="images/Beecher_LOD_Exp copy.png" style="width: 850px; height: auto; display: block; margin: 0 auto;">
<p></p>
<h3 class="major">Tableau Dashboard</h3>
<p>Below is the two page dashboard, showing the required analysis. Food Utility Index (FUI) scores are High,
Medium, Low (H/M/L) and indicate value of the animal and bone portion. Cut Portions are shown in the second dashboard,
and provide higher grain detail into the differences found in each site.
</p>
<div class='tableauPlaceholder' id='viz1707420707729' style='position: relative'><noscript><a href='#'><img alt='Beecher Faunal Analysis ' src='https://public.tableau.com/static/images/Be/Beecher_jan_24/BeecherFaunalAnalysis/1_rss.png' style='border: none' /></a></noscript><object class='tableauViz' style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='Beecher_jan_24/BeecherFaunalAnalysis' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https://public.tableau.com/static/images/Be/Beecher_jan_24/BeecherFaunalAnalysis/1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='en-US' /><param name='filter' value='publish=yes' /></object></div> <script type='text/javascript'> var divElement = document.getElementById('viz1707420707729'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='1016px';vizElement.style.height='991px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); </script>
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<h2 style="font-size: 30px; display: block; margin: 0 auto; text-align: center; white-space: nowrap;">Manzano Analytics: Archaeological Analysis</h2>
<p style="font-size: 30px; font-weight: 100; text-align: center;">Lessons Learned:</p>
</div>
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<div class="alternate">
<h4 style="font-size: 18px;">Domain Experts</h4>
<ul style="font-size: 20px;">
<li>Timely and comprehensive communication is key to fostering a collaborative environment. This project was the
result of multiple meetings with the lead archaeologist to identify and discuss the archaeological assemblage.</li>
<li>Data organization and manipulation are <b>my</b> areas expertise, and proper application
of this expertise can better illustrate what is possible with the data to the domain experts. Due to the project's
scope and focus, we opted to analyze only a portion of the entire assemblage, making descriptive
analysis the most suitable approach.</li>
<li>Adaptability is key for providing relevant data context. I developed a few interactive
visualizations to show what is possible and what makes sense given the dataset. This helped the lead domain
expert better understand the questions and context he wanted addressed, resulting in a successful end product. </li>
</ul>
<h4 style="font-size: 18px;">Tableau Expressions</h4>
<ul style="font-size: 20px;">
<li>Fixed Level-Of-Detail Expressions are invaluable for providing dynamic proportional visualizations. This lets
you group by any variable and maintain proper proportions while
selecting and deselecting parts of that group.</li>
<li>Combining table calculations in Tableau enhances modularity. I created separate calculations for the numerator
and denominator of the relevant variable
for dynamic proportions, then combined them into a separate calculation.</li>
</ul>
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<h3 class="major">Instacart Customer Segmentation</h3>
<p>Using Python and Tableau to understand customer spending behavior and drive revenue in multiple ways.</p>
<a href="Instacart.html" class="special">Learn more</a>
</article>
<article>
<a href="Pig_E_Bank.html" class="image"><img src="images/Pig_E_Icon.png" alt="" /></a>
<h3 class="major">Pig E. Bank Churn Modeling</h3>
<p>Modeling customer behavior with Python's XGBoost library and SHAP values for feature importance.</p>
<a href="Pig_E_Bank.html" class="special">Learn more</a>
</article>
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<h2 class="major">Get in touch</h2>
<p> A seasoned and self-made fund manager, averaging a 40% CAGR for six years, and demonstrating an expertise in applied data analytics..</p>
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