Merge pull request #87 from earthlab/dev-lessons

Dev lessons merge into dev

Author: Leah Wasser

_events/course-materials/2017-01-17-earth-analytics-course.md

@@ -11,33 +11,31 @@ endTime: '17:50'
 
 ## About
 
-This advanced, multidisciplinary course will address major
-questions in Earth science and teach students to use the
-analytical tools necessary to undertake exploration of ‘big
-scientific data’. If you are a graduate and undergraduate (junior/
-senior) student in the natural/social science disciplines with an
-interest in learning about computationally intensive science, this
-course is for you.
+This advanced, multidisciplinary course covers addressing major
+questions in Earth science using computationally intensive approaches. Students
+learn analytical tools necessary to undertake exploration of large, spatio-temporal
+scientific data. The course is geared towards graduate and upper level (junior/
+senior) undergraduate students in the natural/social sciences.
 
 ### Example Course Science Topics (Topics subject to change):
 
-* **Climate & Disturbance (Fire / Drought / Permafrost):** How
-changing climate impacts natural disturbance systems.
-* **Land Processes (Erosion):** Identify how rapid and slow
-landscape evolution impacts our lives;
-* **Vegetation:** Determine what is driving Colorado forest
-dieback;
+* **Flooding & Erosion:** We will use data from in situ sensor networks including
+USGS stream gage data and NOAA weather data to better understand flood event drivers
+and impacts.
+* **Fire:** We will use light detection and ranging (lidar) and spectral remote sensing data combined with on
+the ground field measurements to better understand the drivers and impacts of
+wildfires.
+* **Permafrost:**
 * **Climate & Society:** Social media and web as a powerful
 means to understand climate impacts.
-* **Data Integration:** understand issues associated with
-integration data from various sources (scale, resolution, format)
-
 
 Throughout the semester, we will work together to build the
 following skills:
 
 * **Scientific programming:** Use the R / R-studio environment to
 access, process & visualize scientific data.
+* **Data Integration:** understand issues associated with
+integration data from various sources (scale, resolution, format)
 * **Find / access scientific data:** Programmatic (API) access of
 data (NASA, USGS, etc.) via API’s.
 * **Communication/Collaboration:** refine cross- discipline
@@ -49,7 +47,6 @@ using multiple “cores”.
 
 ### Semester Project
 
-End of the semester Course Project: Students will choose a
-science question of interest to explore throughout the semester.
-They will apply skills learned in the course, to produce and
-present a final project.
+All students will select a topic for and use skills learned in the course to work
+collaboratively on an end of the semester project.
+

_events/courses/2017-01-17-earth-analytics-course.md

@@ -18,18 +18,17 @@ startTime: '3:00'
 endTime: '17:50'
 ---
 
-## Earth Analytics Course Home
+## <i class="fa fa-home" aria-hidden="true"></i> Earth Analytics Spring 2017
 
-This advanced, multidisciplinary course will address major questions in Earth
-science and teach students to use the analytical tools necessary to undertake
-exploration of ‘big scientific data’. If you are a graduate or undergraduate
-(junior/senior) student in the natural/social science disciplines with an
-interest in learning about computationally intensive science, this
-course is for you.
+This advanced, multidisciplinary course covers addressing major
+questions in Earth science using computationally intensive approaches. Students
+learn analytical tools necessary to undertake exploration of large, spatio-temporal
+scientific data. The course is geared towards graduate and upper level (junior/
+senior) undergraduate students in the natural/social sciences.
 
 <div class='notice--success' markdown="1">
 
-# Info
+## Course Details
 
 **Instructor:** Dr. Leah A. Wasser
 
@@ -38,29 +37,35 @@ course is for you.
 * **Location:** SEEC S125
 * **Sections:** GEOG 4563 (Junior / Senior Level undergraduate), GEOG 5563 (Graduate)
 
+Office  Hours
+
+We need to add these here. 
+
 Please direct questions to Leah Wasser (leah.wasser at colorado.edu).
 
 </div>
 
-### Example Science Topics:
 
-* **Climate & Disturbance (Fire / Drought / Permafrost):** How
-changing climate impacts natural disturbance systems.
-* **Land Processes (Erosion):** Identify how rapid and slow
-landscape evolution impacts our lives;
-* **Vegetation:** Determine what is driving Colorado forest
-dieback;
+
+### Example Course Science Topics (Topics subject to change):
+
+* **Flooding & Erosion:** We will use data from in situ sensor networks including
+USGS stream gage data and NOAA weather data to better understand flood event drivers
+and impacts.
+* **Fire:** We will use light detection and ranging (lidar) and spectral remote sensing data combined with on
+the ground field measurements to better understand the drivers and impacts of
+wildfires.
+* **Permafrost:**
 * **Climate & Society:** Social media and web as a powerful
 means to understand climate impacts.
-* **Data Integration:** understand issues associated with
-integration data from various sources (scale, resolution, format)
-
 
 Throughout the semester, we will work together to build the
 following skills:
 
 * **Scientific programming:** Use the R / R-studio environment to
 access, process & visualize scientific data.
+* **Data Integration:** understand issues associated with
+integration data from various sources (scale, resolution, format)
 * **Find / access scientific data:** Programmatic (API) access of
 data (NASA, USGS, etc.) via API’s.
 * **Communication/Collaboration:** refine cross- discipline
@@ -72,7 +77,5 @@ using multiple “cores”.
 
 ### Semester Project
 
-End of the semester Course Project: Students will choose a
-science question of interest to explore throughout the semester.
-They will apply skills learned in the course, to produce and
-present a final project.
+All students will select a topic for and use skills learned in the course to work
+collaboratively on an end of the semester project.

_pages/learn.md

@@ -108,12 +108,10 @@ the bottom. We'll try our best to help!
 
 ## Data Intensive Courses
 A newly designed
-
-[Earth Systems Analytics course - GEOG 4463 / 5463]({{ site.url }}/course-materials/earth-analytics/)
-will be taught January 2017. This course fuses key topics related to the grand
+[Earth Analytics course - GEOG 4563 / 5563]({{ site.url }}/course-materials/earth-analytics/)
+will be this Spring 2017. This course fuses key topics related to the grand
 challenges in science, remote sensing and computationally intensive approaches.
-The course will be held in Spring 2017 at the CU Boulder campus. Stay tuned for
-course materials as they develop.
+The course will be held in Spring 2017 at the CU Boulder campus.
 
 
 Questions? Tweet: <a href="http://twitter.com/leahawasser" class="btn btn--twitter"><i class="fa fa-twitter"></i>@leahawasser</a> or <a href="http://twitter.com/mxwlj" class="btn btn--twitter"><i class="fa fa-twitter"></i>@mxwlj</a>

_posts/course-materials/earth-analytics/2016-12-06-geog-4163-5163-syllabus.md

@@ -11,7 +11,7 @@ sidebar:
 author_profile: false
 comments: false
 ---
- 
+
 
 ## Lead Instructor
 
@@ -32,29 +32,28 @@ [email protected]
 
   </div>
 
-## Course Description
+## About the Course
 This advanced, multidisciplinary course will address major questions in Earth
 science and teach students to use the analytical tools necessary to undertake
-exploration of ‘big scientific data’. This course is desiend for upper level (junior /
+exploration of ‘big scientific data’. This course is designed for upper level (junior /
 senior level) undergraduate students and graduate students.
 
-Throughout the course we will focus on addressing questions in science using
-data and computationally intensive approaches. We will use a suite of different
-types of publically available data including:
+Throughout the course we will use computationally intensive techniques to address
+scientific questionsWe will use a suite of different
+types of publicly available data including:
 
-* various types of remote sensing data (spectral and lidar),
-* data collected from large *in situ* (on the ground) sensor networks maintained
-through organizations such as USGS and NOAA,
-* social media data, and
-* demographic (census) data.
+* Satellite and airborne lidar and spectral remote sensing data,
+* Data collected using distributed *in situ* (on the ground) sensor networks
+* Social media data, and
+* Demographic (census) data.
 
 ## Learning Outcomes
 
 At the end of this course you will be able to:
 
-* Open and visualize various types of data using the `R` programming languages.
+* Open and visualize various types of data using the `R` programming language.
 * Navigate and use the `RStudio` environment for `R`.
-* Describe and apply several approaches to efficient computing including parallelization
+* Describe and apply several approaches to efficient computing including parallelization.
 
 ## Grading
 Grading will be based on the following course assignments. Late assignments will
@@ -68,12 +67,20 @@ receive a 10% deduction per 24-hour period after the due date/time.
 | Final Project Submission | 20%  |
 | Class Participation | 15% |
 
+## Late assignments
+
+Late assignments will not be accepted in this course. If there are extenuating /
+university approved
+circumstances university-approved activity, illness, injury, family emergency, or religious
+observance that prevents you from completing an assignment on time, please
+get in touch with the instructor or the course TA as soon as possible.
+
 
-## Attendance:
+## Attendance
 
 Attendance is required for all class sessions. In the event that you must miss a class due
 to a university-approved activity, illness, injury, family emergency, or religious
-observance, you must notify me, preferably before the day of class, and the absence
+observance, you must notify the course instructor, preferably **before** the day of class, and the absence
 will be excused. Students will be given a reasonable amount of time to make up the
 work based on the type of assignment missed and the reason for their absence.
 Unexcused absences will affect the student’s grade because regular
@@ -82,5 +89,7 @@ participation is a requirement of this course.
 
 ## Readings
 
-NOTE, readings are subject to change or additional ones added. Appropriate
-notice will be given if readings do change.
+Readings are posted every week along with the homework assignment for that week.
+The material for each week will be posted no later than the Tuesday before the
+next weeks' class. If you are looking ahead to upcoming weeks, please note that,
+readings are subject to change.

_posts/course-materials/earth-analytics/week-1/co-floods-1-intro/2016-12-06-erosion-00-instructor-notes.md

@@ -11,7 +11,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 4
 ---
 

_posts/course-materials/earth-analytics/week-1/co-floods-1-intro/2016-12-06-erosion-01-intro-co-floods.md

@@ -20,7 +20,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 1
 ---
 {% include toc title="This Lesson" icon="file-text" %}

_posts/course-materials/earth-analytics/week-1/co-floods-1-intro/2016-12-06-erosion-02-precip-discharge-r-example.md

@@ -12,7 +12,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 2
 ---
 {% include toc title="This Lesson" icon="file-text" %}

_posts/course-materials/earth-analytics/week-1/co-floods-1-intro/2016-12-06-erosion-03-precip-discharge-data-co-floods.md

@@ -18,7 +18,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 3
 ---
 

_posts/course-materials/earth-analytics/week-1/intro-knitr-rmd/2016-12-06-Rmd01-why-rmarkdown.md

@@ -8,7 +8,7 @@ class-lesson: ['intro-rmarkdown-knitr']
 permalink: /course-materials/earth-analytics/week-1/intro-rmarkdown-knitr/
 nav-title: 'Intro to R Markdown'
 dateCreated: 2016-12-12
-modified: 2016-12-12
+modified: 2017-01-11
 module-title: 'Document & Publish a Workflow with R Markdown & Knitr'
 module-nav-title: 'R Markdown Intro'
 module-description: 'This module teaches participants how to use R Markdown
@@ -18,7 +18,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 1
 ---
 {% include toc title="This Lesson" icon="file-text" %}
@@ -80,14 +80,14 @@ View Slideshow: Share, Publish & Archive Code & Data</a>
 
 ## About R Markdown
 
-Simply put, `.rmd` is a text based file format that allows you to include both
+Simply put, `.Rmd` is a text based file format that allows you to include both
 descriptive text, code blocks and code output. You can run the code in `R` and
 using a package called `knitr` (which we will talk about next) you can export the
-text formated .rmd file to a nicely rendered, shareable format like pdf or html.
+text formated .Rmd file to a nicely rendered, shareable format like pdf or html.
 When you knit (or use `knitr`) the code is run and so your code outputs including
 plots, and other figures appear in the rendered document.
 
-> “R Markdown (.rmd) is an authoring format that enables easy creation of dynamic
+> “R Markdown (.Rmd) is an authoring format that enables easy creation of dynamic
 documents, presentations, and reports from R. It combines the core syntax of
 markdown (an easy to write plain text format) with embedded R code chunks that
 are run so their output can be included in the final document. R Markdown
@@ -96,7 +96,7 @@ underlying R code or data changes)."
 -- <a href="http://rmarkdown.rstudio.com/" target="_blank">RStudio documentation</a>.
 
 
-We use R Markdown (.rmd) files to document workflows and to share data processing,
+We use R Markdown (.Rmd) files to document workflows and to share data processing,
 analysis and visualization code & outputs.
 
 ## Why R Markdown?
@@ -117,9 +117,9 @@ share and publish code online.
 {: .notice}
 
 ### RMD is beneficial to your colleagues
-The link between data, code and results make `.rmd` powerful. You can share your
+The link between data, code and results make `.Rmd` powerful. You can share your
 entire workflow with your colleagues and they can quickly see your process. You
-can also write reports using `.rmd` files which contain code and data
+can also write reports using `.Rmd` files which contain code and data
 analysis results. To enrich the document, you can add text, just like you would
 in a word document that describes your workflow, discusses your results and
 presents your conclusions - along side your analysis results.
@@ -137,10 +137,10 @@ this one - were created using R Markdown!
 {: .notice}
 
 
-## Use Knitr to convert .rmd to .pdf
+## Use Knitr to convert .Rmd to .pdf
 
 We use the `R` `knitr` package to render our markdown and create easy to read
-documents from `.rmd` files. We will cover how to use `knitr` later in this
+documents from `.Rmd` files. We will cover how to use `knitr` later in this
 lesson series.
 
 <figure class="half">

_posts/course-materials/earth-analytics/week-1/intro-knitr-rmd/2016-12-06-Rmd02-RMarkdown.md

@@ -4,6 +4,7 @@ title: "Write Reports and Document Workflow Using R Markdown"
 excerpt: 'This tutorial cover how to create an R Markdown file in R and then
 render it to html using knitr.'
 authors: [Leah Wasser, NEON Data Skills]
+modified: 2017-01-11
 category: [course-materials]
 class-lesson: ['intro-rmarkdown-knitr']
 permalink: /course-materials/earth-analytics/week-1/intro-rmarkdown-knitr2/
@@ -12,7 +13,7 @@ week: 1
 sidebar:
   nav:
 author_profile: false
-comments: false
+comments: true
 order: 2
 ---
 {% include toc title="This Lesson" icon="file-text" %}
@@ -61,7 +62,7 @@ ready to create your own `.RMD` document. Do the following:
 
 1. Using the Create a new R Markdown file and choose HTML as the desired output format.
 2. Enter a Title (Earth Analytics Week 1) and Author Name (your name). Then click OK.
-3. Save the file using the following format: **FirstInitial-LastName-week-1.rmd**
+3. Save the file using the following format: **FirstInitial-LastName-week-1.Rmd**
 NOTE: The document title is not the same as the file name.
 4. Hit the <kbd>`Knit HTML`</kbd> drop down button in `RStudio` (as is done in the video above). What happens?