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<!DOCTYPE html>
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<title>0.1 Objectives | README.utf8</title>
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<li><a href="./">Computational Thinking for Social Scientists</a></li>
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<li class="chapter" data-level="0.1" data-path=""><a href="#objectives"><i class="fa fa-check"></i><b>0.1</b> Objectives</a></li>
<li class="chapter" data-level="0.2" data-path=""><a href="#contact"><i class="fa fa-check"></i><b>0.2</b> Contact</a></li>
<li class="chapter" data-level="0.3" data-path=""><a href="#special-thanks"><i class="fa fa-check"></i><b>0.3</b> Special thanks</a></li>
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<li><a href="https://github.com/jaeyk/PS239T/" target="blank">Published with bookdown</a></li>
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<p><strong>Computational Thinking for Social Scientists</strong></p>
<p>This is the git repository for <a href="https://jaeyk.github.io/PS239T/">the <em>Computational Thinking for Social Scientists</em> website</a>. This book intends to help social scientists to think computationally and develop proficiency with computational tools and techniques, necessary to conduct research in computational social science. Mastering these tools and techniques not only enables social scientists to collect, wrangle, analyze, and interpret data with less pain and more fun, but it also let them to work on research projects that would previously seem impossible.</p>
<div id="objectives" class="section level2" number="0.1">
<h2><span class="header-section-number">0.1</span> Objectives</h2>
<p>The course is currently divided into two main subjects (fundamentals and applications) and six main sessions.</p>
<p><strong>Fundamentals</strong>
* In the first section, students learn best practices in data and code management using Git and Bash.
* In the second, students learn how to wrangle, model, and visualize data easier and faster.
* In the third, students learn how to use functions to automate repeated things and develop their own data tools (e.g., packages).</p>
<p><strong>Applications</strong>
* In the fourth, students learn how to collect and parse semi-structured data at scale (e.g., using APIs and webscraping).
* In the fifth, students learn how to analyze high-dimensional data (e.g., text) using machine learning.
* In the final, students learn how to access, query, and manage big data using SQL.</p>
</div>
<div id="contact" class="section level2" number="0.2">
<h2><span class="header-section-number">0.2</span> Contact</h2>
<p>Content developer: <a href="https://jaeyk.github.io/">Jae Yeon Kim</a>: <a href="mailto:[email protected]" class="email">[email protected]</a></p>
</div>
<div id="special-thanks" class="section level2" number="0.3">
<h2><span class="header-section-number">0.3</span> Special thanks</h2>
<p>This course is a remix version of <a href="https://github.com/rochelleterman/PS239T">the course</a> originally developed by <a href="http://rochelleterman.com/">Rochelle Terman</a> then revised by <a href="http://rachelbernhard.com/">Rachel Bernhard</a>. Other teaching materials draw from the workshops I created for <a href="https://dlab.berkeley.edu/">D-Lab</a> and <a href="https://data.berkeley.edu/research/discovery-program-home">Data Science Discovery Program</a> at UC Berkeley.</p>
<p><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" /> This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
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