Built site for gh-pages

Quarto GHA Workflow Runner · Quarto GHA Workflow Runner · commit a7f4de386b7e · 2025-04-10T21:27:05.000Z
diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-49ca8ff8
+8fdd80d2
diff --git a/modules/week02/hw-02.html b/modules/week02/hw-02.html
@@ -225,7 +225,7 @@ <h1 class="title">Week 2 - Data Cleaning</h1>
 </header>
 
 
-<p>**Please use Canvas to return the assignments: &lt;&gt;{target=“_blank”}**</p>
+<p><strong>Please use Canvas to return the assignments: <a href="https://ucsb.instructure.com/courses/26293/assignments/357220" target="_blank">https://ucsb.instructure.com/courses/26293/assignments/357220</a></strong></p>
 <p>We cleaned the <code>Snow_cover</code> column during class. Inspiring yourself from the steps we followed, do the following in a quarto document:</p>
 <ol type="1">
 <li><p>Clean the <code>Water_cover</code> column to transform it into the correct data type and respect expectations for a percentage</p></li>
@@ -240,11 +240,11 @@ <h3 class="anchored" data-anchor-id="setup">Setup</h3>
 <section id="the-expectations-are" class="level3">
 <h3 class="anchored" data-anchor-id="the-expectations-are">The expectations are:</h3>
 <ul>
-<li>The Quarto document <code>eds213_data_cleaning_assign_YOURNAME.qmd</code> should run if your repo is cloned locally (5pts; Reproducibility)</li>
-<li>The code should output a csv file named <code>all_cover_fixed_YOURNAME.csv</code> in the <code>data/processed</code> folder (5pts; Code)</li>
-<li>Comment well you code (10pts; Documentation)</li>
-<li>Your quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30pts; Documentation)</li>
-<li>The code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50pts; Technical concepts)</li>
+<li>The Quarto document <code>eds213_data_cleaning_assign_YOURNAME.qmd</code> should run if your repo is cloned locally (5 pts; Reproducibility)</li>
+<li>The code should output a csv file named <code>all_cover_fixed_YOURNAME.csv</code> in the <code>data/processed</code> folder (5 pts; Code)</li>
+<li>Comment well you code (10 pts; Documentation)</li>
+<li>Your quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30 pts; Documentation)</li>
+<li>The code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50 pts; Technical concepts)</li>
 </ul>
 <p><strong>Note</strong>: We recommend starting by importing the csv file with the corrected <code>Snow_cover</code> column (<code>data/processed/snow_cover.csv</code>) we generated during class (my code <a href="https://github.com/UCSB-Library-Research-Data-Services/bren-meds213-data-cleaning/blob/later/data-cleaning.qmd">here</a>)</p>
 <p>Finally, remember there are many correct ways of doing this, so document your work well so in 6 months from now you can easily remember your thought process and decisions made while cleaning those data.</p>
@@ -255,7 +255,7 @@ <h3 class="anchored" data-anchor-id="how-to-submit-your-assignment-to-canvas">Ho
 <li><p>Add the URL of your repository at the top of your repo’s README</p></li>
 <li><p>Download your repository as a <code>.zip</code> file and upload it to canvas</p></li>
 </ul>
-<p><strong>Credit: 100pts</strong></p>
+<p><strong>Credit: 100 pts</strong></p>
 
 
 </section>
diff --git a/search.json b/search.json
@@ -354,7 +354,7 @@
     "href": "modules/week02/hw-02.html",
     "title": "Week 2 - Data Cleaning",
     "section": "",
-    "text": "**Please use Canvas to return the assignments: &lt;&gt;{target=“_blank”}**\nWe cleaned the Snow_cover column during class. Inspiring yourself from the steps we followed, do the following in a quarto document:\n\nClean the Water_cover column to transform it into the correct data type and respect expectations for a percentage\nClean the Land_cover column to transform it into the correct data type and respect expectations for a percentage\nUse the relationship between the three cover columns (Snow, Water, Land) to infer missing values where possible and recompute the Total_cover column as needed\n\nAdd comments to your quarto document about your decisions and assumptions, this will be a large part of the grading (see below).\n\nSetup\nYou should start a new quarto document named eds213_data_cleaning_assign_YOURNAME.qmd on your fork of the GitHub repository bren-meds213-data-cleaning. Don’t forget to use the metadata present in the data folder\n\n\nThe expectations are:\n\nThe Quarto document eds213_data_cleaning_assign_YOURNAME.qmd should run if your repo is cloned locally (5pts; Reproducibility)\nThe code should output a csv file named all_cover_fixed_YOURNAME.csv in the data/processed folder (5pts; Code)\nComment well you code (10pts; Documentation)\nYour quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30pts; Documentation)\nThe code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50pts; Technical concepts)\n\nNote: We recommend starting by importing the csv file with the corrected Snow_cover column (data/processed/snow_cover.csv) we generated during class (my code here)\nFinally, remember there are many correct ways of doing this, so document your work well so in 6 months from now you can easily remember your thought process and decisions made while cleaning those data.\n\n\nHow to submit your assignment to Canvas:\n\nAdd the URL of your repository at the top of your repo’s README\nDownload your repository as a .zip file and upload it to canvas\n\nCredit: 100pts"
+    "text": "Please use Canvas to return the assignments: https://ucsb.instructure.com/courses/26293/assignments/357220\nWe cleaned the Snow_cover column during class. Inspiring yourself from the steps we followed, do the following in a quarto document:\n\nClean the Water_cover column to transform it into the correct data type and respect expectations for a percentage\nClean the Land_cover column to transform it into the correct data type and respect expectations for a percentage\nUse the relationship between the three cover columns (Snow, Water, Land) to infer missing values where possible and recompute the Total_cover column as needed\n\nAdd comments to your quarto document about your decisions and assumptions, this will be a large part of the grading (see below).\n\nSetup\nYou should start a new quarto document named eds213_data_cleaning_assign_YOURNAME.qmd on your fork of the GitHub repository bren-meds213-data-cleaning. Don’t forget to use the metadata present in the data folder\n\n\nThe expectations are:\n\nThe Quarto document eds213_data_cleaning_assign_YOURNAME.qmd should run if your repo is cloned locally (5 pts; Reproducibility)\nThe code should output a csv file named all_cover_fixed_YOURNAME.csv in the data/processed folder (5 pts; Code)\nComment well you code (10 pts; Documentation)\nYour quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30 pts; Documentation)\nThe code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50 pts; Technical concepts)\n\nNote: We recommend starting by importing the csv file with the corrected Snow_cover column (data/processed/snow_cover.csv) we generated during class (my code here)\nFinally, remember there are many correct ways of doing this, so document your work well so in 6 months from now you can easily remember your thought process and decisions made while cleaning those data.\n\n\nHow to submit your assignment to Canvas:\n\nAdd the URL of your repository at the top of your repo’s README\nDownload your repository as a .zip file and upload it to canvas\n\nCredit: 100 pts"
   },
   {
     "objectID": "installing-duckdb.html",
diff --git a/sitemap.xml b/sitemap.xml
@@ -2,182 +2,182 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/hw_bonus.html</loc>
-    <lastmod>2025-04-10T21:22:12.296Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.051Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week03/hw-03-3.html</loc>
-    <lastmod>2025-04-10T21:22:12.304Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week03/hw-03-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.304Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week07/hw-07-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.326Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.081Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week07/index-07.html</loc>
-    <lastmod>2025-04-10T21:22:12.326Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.081Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/index-06.html</loc>
-    <lastmod>2025-04-10T21:22:12.324Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/python-programming.html</loc>
-    <lastmod>2025-04-10T21:22:12.324Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/hw-06-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.323Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week04/hw-04-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.314Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.069Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week04/hw-04-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.314Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.070Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/hw-05-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.315Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/hw-05-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.315Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/index-05.html</loc>
-    <lastmod>2025-04-10T21:22:12.316Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week01/hw-01-3.html</loc>
-    <lastmod>2025-04-10T21:22:12.299Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.054Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week01/hw-01-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.299Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.054Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week09/index-09.html</loc>
-    <lastmod>2025-04-10T21:22:12.399Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.152Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week09/hw-09.html</loc>
-    <lastmod>2025-04-10T21:22:12.399Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.152Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week08/case-a.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week08/index-08.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week02/hw-02.html</loc>
-    <lastmod>2025-04-10T21:22:12.303Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/installing-duckdb.html</loc>
-    <lastmod>2025-04-10T21:22:12.295Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.051Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/syllabus.html</loc>
-    <lastmod>2025-04-10T21:22:12.463Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.213Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/index.html</loc>
-    <lastmod>2025-04-10T21:22:12.295Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.051Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/resources.html</loc>
-    <lastmod>2025-04-10T21:22:12.463Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.213Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/about.html</loc>
-    <lastmod>2025-04-10T21:22:12.292Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.048Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week02/index-02.html</loc>
-    <lastmod>2025-04-10T21:22:12.303Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week08/case-b.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week08/case-c.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week08/case-d.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week09/FAIR_assessment_tool.html</loc>
-    <lastmod>2025-04-10T21:22:12.399Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.152Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week01/hw-01-1.html</loc>
-    <lastmod>2025-04-10T21:22:12.299Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.054Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week01/index-01.html</loc>
-    <lastmod>2025-04-10T21:22:12.299Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.054Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/hw-05-4.html</loc>
-    <lastmod>2025-04-10T21:22:12.316Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/bash-essentials.html</loc>
-    <lastmod>2025-04-10T21:22:12.315Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week05/hw-05-3.html</loc>
-    <lastmod>2025-04-10T21:22:12.315Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.071Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week04/hw-04-3.html</loc>
-    <lastmod>2025-04-10T21:22:12.314Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.070Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week04/index-04.html</loc>
-    <lastmod>2025-04-10T21:22:12.314Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.070Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/hw-06-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.323Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/hw-06-3.html</loc>
-    <lastmod>2025-04-10T21:22:12.324Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week06/python-programming-cont.html</loc>
-    <lastmod>2025-04-10T21:22:12.324Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.079Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week07/hw-07-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.326Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.081Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week07/virtual-environments-notes.html</loc>
-    <lastmod>2025-04-10T21:22:12.384Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.138Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week03/hw-03-2.html</loc>
-    <lastmod>2025-04-10T21:22:12.304Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week03/index-03.html</loc>
-    <lastmod>2025-04-10T21:22:12.304Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.059Z</lastmod>
   </url>
   <url>
     <loc>https://UCSB-Library-Research-Data-Services.github.io/bren-eds213/modules/week10/index-10.html</loc>
-    <lastmod>2025-04-10T21:22:12.417Z</lastmod>
+    <lastmod>2025-04-10T21:25:26.169Z</lastmod>
   </url>
 </urlset>

Original file line number	Diff line number	Diff line change
`@@ -354,7 +354,7 @@`
`354`	`354`	`"href": "modules/week02/hw-02.html",`
`355`	`355`	`"title": "Week 2 - Data Cleaning",`
`356`	`356`	`"section": "",`
`357`		- "text": "Please use Canvas to return the assignments: <>{target=“_blank”}\nWe cleaned the Snow_cover column during class. Inspiring yourself from the steps we followed, do the following in a quarto document:\n\nClean the Water_cover column to transform it into the correct data type and respect expectations for a percentage\nClean the Land_cover column to transform it into the correct data type and respect expectations for a percentage\nUse the relationship between the three cover columns (Snow, Water, Land) to infer missing values where possible and recompute the Total_cover column as needed\n\nAdd comments to your quarto document about your decisions and assumptions, this will be a large part of the grading (see below).\n\nSetup\nYou should start a new quarto document named eds213_data_cleaning_assign_YOURNAME.qmd on your fork of the GitHub repository bren-meds213-data-cleaning. Don’t forget to use the metadata present in the data folder\n\n\nThe expectations are:\n\nThe Quarto document eds213_data_cleaning_assign_YOURNAME.qmd should run if your repo is cloned locally (5pts; Reproducibility)\nThe code should output a csv file named all_cover_fixed_YOURNAME.csv in the data/processed folder (5pts; Code)\nComment well you code (10pts; Documentation)\nYour quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30pts; Documentation)\nThe code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50pts; Technical concepts)\n\nNote: We recommend starting by importing the csv file with the corrected Snow_cover column (data/processed/snow_cover.csv) we generated during class (my code here)\nFinally, remember there are many correct ways of doing this, so document your work well so in 6 months from now you can easily remember your thought process and decisions made while cleaning those data.\n\n\nHow to submit your assignment to Canvas:\n\nAdd the URL of your repository at the top of your repo’s README\nDownload your repository as a .zip file and upload it to canvas\n\nCredit: 100pts"
	`357`	+ "text": "Please use Canvas to return the assignments: https://ucsb.instructure.com/courses/26293/assignments/357220\nWe cleaned the Snow_cover column during class. Inspiring yourself from the steps we followed, do the following in a quarto document:\n\nClean the Water_cover column to transform it into the correct data type and respect expectations for a percentage\nClean the Land_cover column to transform it into the correct data type and respect expectations for a percentage\nUse the relationship between the three cover columns (Snow, Water, Land) to infer missing values where possible and recompute the Total_cover column as needed\n\nAdd comments to your quarto document about your decisions and assumptions, this will be a large part of the grading (see below).\n\nSetup\nYou should start a new quarto document named eds213_data_cleaning_assign_YOURNAME.qmd on your fork of the GitHub repository bren-meds213-data-cleaning. Don’t forget to use the metadata present in the data folder\n\n\nThe expectations are:\n\nThe Quarto document eds213_data_cleaning_assign_YOURNAME.qmd should run if your repo is cloned locally (5 pts; Reproducibility)\nThe code should output a csv file named all_cover_fixed_YOURNAME.csv in the data/processed folder (5 pts; Code)\nComment well you code (10 pts; Documentation)\nYour quarto document should provide all the necessary explanations about your decisions and discuss potential assumptions you used (don’t forget to look at the metadata!) (30 pts; Documentation)\nThe code should perform the 3 data cleaning steps describe above to enable ingestion into a database (50 pts; Technical concepts)\n\nNote: We recommend starting by importing the csv file with the corrected Snow_cover column (data/processed/snow_cover.csv) we generated during class (my code here)\nFinally, remember there are many correct ways of doing this, so document your work well so in 6 months from now you can easily remember your thought process and decisions made while cleaning those data.\n\n\nHow to submit your assignment to Canvas:\n\nAdd the URL of your repository at the top of your repo’s README\nDownload your repository as a .zip file and upload it to canvas\n\nCredit: 100 pts"
`358`	`358`	`},`
`359`	`359`	`{`
`360`	`360`	`"objectID": "installing-duckdb.html",`