<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recent &amp; Regular Courses | Daniel Contreras</title><link>https://dcontreras.netlify.app/course/</link><atom:link href="https://dcontreras.netlify.app/course/index.xml" rel="self" type="application/rss+xml"/><description>Recent &amp; Regular Courses</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><image><url>https://dcontreras.netlify.app/media/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_3.png</url><title>Recent &amp; Regular Courses</title><link>https://dcontreras.netlify.app/course/</link></image><item><title>Applying GIS in Archaeological Research</title><link>https://dcontreras.netlify.app/course/gis/</link><pubDate>Wed, 22 May 2024 00:00:00 +0000</pubDate><guid>https://dcontreras.netlify.app/course/gis/</guid><description>&lt;p>In completing this course, students develop:&lt;/p>
&lt;ul>
&lt;li>Theoretical background on the applications of spatial variation, geospatial technologies, and GIS in archaeology.&lt;/li>
&lt;li>Fluency with geospatial methods, particularly the use of GIS, and a foundation from which to further self-teach.&lt;/li>
&lt;li>Familiarity with the acquisition (particularly of publicly-available data), management, and analysis of archaeological data that have a spatial component.&lt;/li>
&lt;li>Ability to produce polished products (including, but not limited to, maps) that communicate arguments based on geospatial data.&lt;/li>
&lt;/ul></description></item><item><title>Digital Methods in Archaeology</title><link>https://dcontreras.netlify.app/course/digitalmethods/</link><pubDate>Wed, 22 May 2024 00:00:00 +0000</pubDate><guid>https://dcontreras.netlify.app/course/digitalmethods/</guid><description>&lt;p>Digital tools have become as fundamental to archaeology as the trowel, and are today integral to archaeological practice and knowledge production. Many questions still surround the digital turn in archaeology, however. How do we go about the process of collecting, manipulating, and displaying digital data? How do we test our own ideas, and make arguments to others, with digital methods and data? How can we use digital methods to do more than simply produce impressive visualizations? Are there, in fact, intellectual rewards for using digital methods in archaeology, or are the benefits primarily to efficiency?
This course focuses on acquiring and using digital data in archaeology. Such a focus suggests three underlying questions:&lt;/p>
&lt;ul>
&lt;li>What are digital data?&lt;/li>
&lt;li>Where do digital data come from?&lt;/li>
&lt;li>What are digital data used for?&lt;/li>
&lt;/ul>
&lt;p>These questions have both practical and theoretical implications, each of which will be explored through hands-on experience and published case studies.
In addition, at a more meta-level, this course also examines the question of whether digital data and methods are fundamentally changing the practice and potential of archaeology. To engage with these questions, this course focuses on digital methods of archaeological documentation and exploration, pairing hands-on practice with critical discussion of published case studies.&lt;/p></description></item><item><title>R for Archaeological Data Analysis and Visualization</title><link>https://dcontreras.netlify.app/course/r_arch/</link><pubDate>Wed, 22 May 2024 00:00:00 +0000</pubDate><guid>https://dcontreras.netlify.app/course/r_arch/</guid><description>&lt;p>This course introduces students to the basic quantitative methods required to describe and analyze archaeological data. Each week, students will be introduced to a new statistical technique and asked to use that technique in order to solve a small research problem. Because many of the research challenges archaeologists face have to do with acquiring and managing data (especially legacy data) and then using it in argumentation, this course will emphasize not only statistical methods, but also 1) how to construct and critically evaluate arguments grounded in quantitative data, and 2) how to explore, analyze, and display data in the open-source R statistical environment.&lt;/p></description></item></channel></rss>