Deploying to gh-pages from @ c78a222 🚀

listings.json

@@ -5,7 +5,6 @@
       "/workshops/shiny_python_intro.html",
       "/workshops/dataviz.html",
       "/workshops/ml_dl_python.html",
-      "/workshops/causal.html",
       "/workshops/tidymodels_advanced.html",
       "/workshops/pkg_dev.html",
       "/workshops/arrow.html",
@@ -14,6 +13,7 @@
       "/workshops/r_pharma.html",
       "/workshops/databases.html",
       "/workshops/shiny_python_adv.html",
+      "/workshops/causal.html",
       "/workshops/quarto_dashboards.html",
       "/workshops/posit_tools_python.html",
       "/workshops/posit_tools_r.html",

search.json

@@ -72,9 +72,9 @@
   {
     "objectID": "workshops/causal.html",
     "href": "workshops/causal.html",
-    "title": "Add workshop title here",
+    "title": "Causal Inference in R",
     "section": "",
-    "text": "Description\nFull workshop description goes here. Multi-paragraph ok.\n\n\nAudience\nThis course is for you if you:\n\nlist at least\nthree attributes\nfor your target audience\n\n\n\nInstructor(s)\n\n\n\n\n\n\n\n\n\n\nInstructor bio, including link to homepage."
+    "text": "Description\nIn this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.\nIn both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.\n\n\nAudience\nThis course is for you if you:\n\nknow how to fit a linear regression model in R,\nhave a basic understanding of data manipulation and visualization using tidyverse tools, and\nare interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships.\n\n\n\nInstructor(s)\n\n\n\n\n\n\n\n\n\n\nMalcolm Barrett is an epidemiologist and research software engineer at Stanford University. After receiving his Ph.D. in epidemiology from the University of Southern California, he worked as a data scientist at Apple and Posit. His work has focused on causal inference methodology and software development, including many R packages for causal inference.\n\n\n\n\n\n\n\n\n\nTravis Gerke, Sc.D., is Director of Data Science at the PCCTC, a contract research organization that facilitates clinical trials and real-world evidence studies in oncology. He is also co-founder and chief scientific officer of cStructure, a technology company built to empower teams with a collaborative causal design and inference platform."
   },
   {
     "objectID": "workshops/intro_ds_python.html",
@@ -95,7 +95,7 @@
     "href": "index.html",
     "title": "Workshops",
     "section": "",
-    "text": "Order By\n       Default\n         \n          Title\n        \n         \n          Instructor(s)\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\nTitle\n\n\nInstructor(s)\n\n\nDescription\n\n\n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdvanced Shiny for Python\n\n\nGordon Shotwell\n\n\nTake your Shiny apps to the next level with modules \n\n\n\n\nDashboards with Quarto (R + Python)\n\n\nMine Çetinkaya-Rundel\n\n\nCreate sleek, elegant, and eye-catching dashboards with static and/or interactive elements with Quarto. For R and Python users. \n\n\n\n\nData Science Workflows with Posit Tools — Python Focus\n\n\nSam Edwardes, Gagandeep Singh\n\n\nBuild an opinionated and Pythonic data science workflow using Posit’s professional products and open-source tools. \n\n\n\n\nData Science Workflows with Posit Tools — R Focus\n\n\nRyan Johnson, Katie Masiello\n\n\nUse open source packages and Posit’s professional tools — Workbench, Connect, and Package Manager — to improve your end-to-end data science workflows. \n\n\n\n\nDevOps for Data Scientists\n\n\nRika Gorn\n\n\nThis workshop is intended for data scientists who wish to learn more about the basic principles and tools of DevOps and to get hands-on experience putting DevOps workflows into production. \n\n\n\n\nFrom R User to R Programmer\n\n\nEmma Rand, Ian Lyttle\n\n\nImprove your R programming skills and reduce the amount of duplication in your code. \n\n\n\n\nIntro to MLOps with vetiver\n\n\nIsabel Zimmerman\n\n\nUtilize the vetiver framework in Python and R for efficient versioning, deployment, and monitoring of machine learning models in production. \n\n\n\n\nIntroduction to Data Science with Python\n\n\nPosit Academy Team\n\n\nLearn the foundations of Python for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals. \n\n\n\n\nIntroduction to Data Science with R and Tidyverse\n\n\nPosit Academy Team\n\n\nLearn the foundations of R for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals. \n\n\n\n\nIntroduction to tidymodels\n\n\nHannah Frick, Simon Couch\n\n\nMachine learning with tabular data using the tidymodels framework. \n\n\n\n\nMaking Tables with gt and Great Tables\n\n\nRichard Iannone, Michael Chow\n\n\nCreate publication-quality tables with gt and Great Tables. For R and Python users. \n\n\n\n\nQuarto Websites\n\n\nCharlotte Wickham, Emil Hvitfeldt\n\n\nBuild a website from scratch with Quarto. \n\n\n\n\nR in Production\n\n\nHadley Wickham\n\n\nLearn how to write robust R code that both works reliably in production, and when it fails, is easy to debug. \n\n\n\n\nUsing Databricks with R\n\n\nEdgar Ruiz\n\n\nOverview of the latests methods to connect, and interact with Databricks services. \n\n\n\n\nWhat They Forgot To Teach You About R\n\n\nShannon Pileggi, E. David Aja\n\n\nThis workshop is designed to level up experienced R programmers in debugging and personal R administration. \n\n\n\n\n\nNo matching items"
+    "text": "Order By\n       Default\n         \n          Title\n        \n         \n          Instructor(s)\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\nTitle\n\n\nInstructor(s)\n\n\nDescription\n\n\n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdd workshop title here\n\n\nInstructor 1 name, Instructor 2 name (remove if single instructor)\n\n\n1-sentence summary of workshop. \n\n\n\n\nAdvanced Shiny for Python\n\n\nGordon Shotwell\n\n\nTake your Shiny apps to the next level with modules \n\n\n\n\nCausal Inference in R\n\n\nMalcolm Barrett, Travis Gerke\n\n\nLearn to answer causal questions with causal diagrams, propensity score modeling, and more. \n\n\n\n\nDashboards with Quarto (R + Python)\n\n\nMine Çetinkaya-Rundel\n\n\nCreate sleek, elegant, and eye-catching dashboards with static and/or interactive elements with Quarto. For R and Python users. \n\n\n\n\nData Science Workflows with Posit Tools — Python Focus\n\n\nSam Edwardes, Gagandeep Singh\n\n\nBuild an opinionated and Pythonic data science workflow using Posit’s professional products and open-source tools. \n\n\n\n\nData Science Workflows with Posit Tools — R Focus\n\n\nRyan Johnson, Katie Masiello\n\n\nUse open source packages and Posit’s professional tools — Workbench, Connect, and Package Manager — to improve your end-to-end data science workflows. \n\n\n\n\nDevOps for Data Scientists\n\n\nRika Gorn\n\n\nThis workshop is intended for data scientists who wish to learn more about the basic principles and tools of DevOps and to get hands-on experience putting DevOps workflows into production. \n\n\n\n\nFrom R User to R Programmer\n\n\nEmma Rand, Ian Lyttle\n\n\nImprove your R programming skills and reduce the amount of duplication in your code. \n\n\n\n\nIntro to MLOps with vetiver\n\n\nIsabel Zimmerman\n\n\nUtilize the vetiver framework in Python and R for efficient versioning, deployment, and monitoring of machine learning models in production. \n\n\n\n\nIntroduction to Data Science with Python\n\n\nPosit Academy Team\n\n\nLearn the foundations of Python for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals. \n\n\n\n\nIntroduction to Data Science with R and Tidyverse\n\n\nPosit Academy Team\n\n\nLearn the foundations of R for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals. \n\n\n\n\nIntroduction to tidymodels\n\n\nHannah Frick, Simon Couch\n\n\nMachine learning with tabular data using the tidymodels framework. \n\n\n\n\nMaking Tables with gt and Great Tables\n\n\nRichard Iannone, Michael Chow\n\n\nCreate publication-quality tables with gt and Great Tables. For R and Python users. \n\n\n\n\nQuarto Websites\n\n\nCharlotte Wickham, Emil Hvitfeldt\n\n\nBuild a website from scratch with Quarto. \n\n\n\n\nR in Production\n\n\nHadley Wickham\n\n\nLearn how to write robust R code that both works reliably in production, and when it fails, is easy to debug. \n\n\n\n\nUsing Databricks with R\n\n\nEdgar Ruiz\n\n\nOverview of the latests methods to connect, and interact with Databricks services. \n\n\n\n\nWhat They Forgot To Teach You About R\n\n\nShannon Pileggi, E. David Aja\n\n\nThis workshop is designed to level up experienced R programmers in debugging and personal R administration. \n\n\n\n\n\nNo matching items"
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     "objectID": "workshops/shiny_python_intro.html",

workshops/causal.html

@@ -6,12 +6,12 @@
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-<meta name="author" content="Instructor 1 name">
-<meta name="author" content="Instructor 2 name (remove if single instructor)">
+<meta name="author" content="Malcolm Barrett">
+<meta name="author" content="Travis Gerke">
 <meta name="dcterms.date" content="2024-08-12">
-<meta name="description" content="1-sentence summary of workshop.">
+<meta name="description" content="Learn to answer causal questions with causal diagrams, propensity score modeling, and more.">
 
-<title>Add workshop title here</title>
+<title>Causal Inference in R</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -107,18 +107,18 @@
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title">Add workshop title here</h1>
+<h1 class="title">Causal Inference in R</h1>
   <div class="quarto-categories">
-    <div class="quarto-category">add</div>
-    <div class="quarto-category">comma</div>
-    <div class="quarto-category">separated</div>
-    <div class="quarto-category">categories</div>
+    <div class="quarto-category">r</div>
+    <div class="quarto-category">modeling</div>
+    <div class="quarto-category">causal</div>
+    <div class="quarto-category">analysis</div>
   </div>
   </div>
 
 <div>
   <div class="description">
-    <p>1-sentence summary of workshop.</p>
+    <p>Learn to answer causal questions with causal diagrams, propensity score modeling, and more.</p>
   </div>
 </div>
 
@@ -127,19 +127,19 @@ <h1 class="title">Add workshop title here</h1>
   <div class="quarto-title-meta-heading">Affiliations</div>
 
     <div class="quarto-title-meta-contents">
-    <p class="author">Instructor 1 name </p>
+    <p class="author">Malcolm Barrett </p>
   </div>
   <div class="quarto-title-meta-contents">
         <p class="affiliation">
-            Instructor 1 affiliation
+            Stanford University
           </p>
       </div>
     <div class="quarto-title-meta-contents">
-    <p class="author">Instructor 2 name (remove if single instructor) </p>
+    <p class="author">Travis Gerke </p>
   </div>
   <div class="quarto-title-meta-contents">
         <p class="affiliation">
-            Instructor 2 affiliation
+            PCCTC &amp; cStructure
           </p>
       </div>
   </div>
@@ -164,15 +164,16 @@ <h1 class="title">Add workshop title here</h1>
 
 <section id="description" class="level1">
 <h1>Description</h1>
-<p>Full workshop description goes here. Multi-paragraph ok.</p>
+<p>In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.</p>
+<p>In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.</p>
 </section>
 <section id="audience" class="level1">
 <h1>Audience</h1>
 <p>This course is for you if you:</p>
 <ul>
-<li><p>list at least</p></li>
-<li><p>three attributes</p></li>
-<li><p>for your target audience</p></li>
+<li><p>know how to fit a linear regression model in R,</p></li>
+<li><p>have a basic understanding of data manipulation and visualization using tidyverse tools, and</p></li>
+<li><p>are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships.</p></li>
 </ul>
 </section>
 <section id="instructors" class="level1">
@@ -185,9 +186,19 @@ <h1>Instructor(s)</h1>
 </colgroup>
 <tbody>
 <tr class="odd">
-<td><img src="images/name-lastname.jpg" class="img-fluid"></td>
+<td><img src="images/malcolm-barrett.png" class="img-fluid"></td>
 <td></td>
-<td>Instructor bio, including link to homepage.</td>
+<td><a href="https://malco.io/">Malcolm Barrett</a> is an epidemiologist and research software engineer at Stanford University. After receiving his Ph.D.&nbsp;in epidemiology from the University of Southern California, he worked as a data scientist at Apple and Posit. His work has focused on causal inference methodology and software development, including many R packages for causal inference.</td>
+</tr>
+<tr class="even">
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="odd">
+<td><img src="images/travis-gerke.jpg" class="img-fluid"></td>
+<td></td>
+<td><a href="https://travisgerke.com/">Travis Gerke</a>, Sc.D., is Director of Data Science at the PCCTC, a contract research organization that facilitates clinical trials and real-world evidence studies in oncology. He is also co-founder and chief scientific officer of cStructure, a technology company built to empower teams with a collaborative causal design and inference platform.</td>
 </tr>
 </tbody>
 </table>