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<html>
<head>
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<title>Biol 609: Advanced Data Analysis for Biology</title>
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<li>
<a href="index.html">Home</a>
</li>
<li>
<a href="overview.html">Overview</a>
</li>
<li>
<a href="schedule.html">Schedule</a>
</li>
<li>
<a href="final.html">Final</a>
</li>
<li>
<a href="error.html">R Errors</a>
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<a href="syllabus/adv_data_analysis_syllabus_2016a.doc">Syllabus</a>
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<a href="resources.html">Resources</a>
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<a href="http://byrneslab.net/resources/boston-area-data-science-groups/">Local Meetups</a>
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<div id="header">
<h1 class="title toc-ignore">Biol 609: Advanced Data Analysis for
Biology</h1>
</div>
<p><strong>Overview</strong> This course will cover the advanced
statistical modeling techniques needed for students investigating
complex biological systems. The course aims to have students focus on
thinking about the biological processes that they are studying in their
research and how to translate them into statistical models of realistic
complexity. This includes models that deal with autocorrelation, mixed
models, multivariate Structural Equation Models with latent variables,
and more. We will also emphasize Bayesian inferential techniques, as
they have proven to be powerful and flexible in a wide variety of
situations. They are also often philosophically aligned with scientists
goals, perhaps more often than frequentist techniques. The course will
take a hands-on computational approach, allowing students to first
approach concepts theoretically, and then implement them in the
programming language R.</p>
<p><strong>Objectives</strong></p>
<ul>
<li><p>To learn how to think about your study system and research
question of interest in a systematic way and match it with a realistic
process-based model.</p></li>
<li><p>To understand how to build and fit hierarchical/multilevel models
in a likelihood and Bayesian framework.</p></li>
<li><p>Provide the grounding needed to effectively collaborate with
statistical experts.</p></li>
<li><p>Allow students to gain the knowledge necessary to become
life-long learners of data analysis techniques, able to incorporate new
techniques into their analytic toolbelt as needed.</p></li>
</ul>
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