Merge pull request #7 from brendanhcullen/academy-workshops

Update pages for Academy workshops (R and Python)

workshops/intro_ds_python.qmd

@@ -1,35 +1,44 @@
 ---
-title: Add workshop title here
+title: Introduction to Data Science with Python
 author:
-  - name: Instructor 1 name
+  - name: Posit Academy Team
     affiliations:
-      - name: Instructor 1 affiliation
-  - name: Instructor 2 name (remove if single instructor)
-    affiliations:
-      - name: Instructor 2 affiliation      
+      - name: Posit, PBC
 description: |
-  1-sentence summary of workshop.
-categories: [add, comma, separated, categories]
+  Learn the foundations of Python for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals.
+categories: [data science, python]
 ---
 
 # Description
 
-Full workshop description goes here. Multi-paragraph ok.
+This is not a standard workshop, but a six-week online apprenticeship that culminates in-person at posit::conf(2024).
+
+Here, you will learn the foundations of Python for data science under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow learners. Topics will include importing packages and datasets, visualizing data with plotnine, wrangling data with pandas, and reporting reproducibly with Quarto.
+
+**No prior knowledge of Python required.** Visit [posit.co/academy](https://posit.co/products/enterprise/academy/) to learn more about this uniquely effective learning format.
+
+::: callout-important
+## Important dates
+
+🗓️ **Online sessions begin the week of July 1st, 2024.**
+
+🚫 **Registration for this workshop will close on June 24th, 2024.**
+:::
 
 # Audience
 
 This course is for you if you:
 
--   list at least
+-   are new to Python,
 
--   three attributes
+-   have dabbled in Python, but are not sure how to use Python to do data science, or
 
--   for your target audience
+-   are an R user who wants to work more closely with Python users on your team.
 
-# Instructor(s)
+# Instructors
 
-|                               |     |                                             |
-|------------------|------------------|-------------------------------------|
-| ![](images/name-lastname.jpg) |     | Instructor bio, including link to homepage. |
+|                         |     |                                                                                                                                                                                                                                                                                                   |
+|-------------------|-------------------|----------------------------------|
+| ![](images/academy.png) |     | Posit Academy is Posit's internal team of data scientists and educators. We provide a cohort-based, mentor-led, hands-on data science apprenticeship for working professionals. It is delivered on an online platform with interactive coding exercises and frequent cross-learner collaboration. |
 
 : {tbl-colwidths="\[25,5,70\]"}

workshops/intro_ds_r.qmd

@@ -1,35 +1,44 @@
 ---
-title: Add workshop title here
+title: Introduction to Data Science with R and Tidyverse
 author:
-  - name: Instructor 1 name
+  - name: Posit Academy Team
     affiliations:
-      - name: Instructor 1 affiliation
-  - name: Instructor 2 name (remove if single instructor)
-    affiliations:
-      - name: Instructor 2 affiliation      
+      - name: Posit, PBC
 description: |
-  1-sentence summary of workshop.
-categories: [add, comma, separated, categories]
+  Learn the foundations of R for data science through a cohort-based, mentor-led, hands-on apprenticeship for working professionals.
+categories: [data science, tidyverse]
 ---
 
 # Description
 
-Full workshop description goes here. Multi-paragraph ok.
+This is not a standard workshop, but a six-week online apprenticeship that culminates in-person at posit::conf(2024).
+
+Here, you will learn the foundations of R and the Tidyverse under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow learners. Topics will include the basics of R, importing, visualizing and wrangling data with the tidyverse, and reporting reproducibly with Quarto.
+
+**No prior knowledge of R required.** Visit [posit.co/academy](https://posit.co/products/enterprise/academy/) to learn more about this uniquely effective learning format.
+
+::: callout-important
+## Important dates
+
+🗓️ **Online sessions begin the week of July 1st, 2024.**
+
+🚫 **Registration for this workshop will close on June 24th, 2024.**
+:::
 
 # Audience
 
 This course is for you if you:
 
--   list at least
+-   are new to R or the Tidyverse,
 
--   three attributes
+-   have dabbled in R, but now want a rigorous foundation in up-to-date data science best practices, or
 
--   for your target audience
+-   are a SAS or Excel user looking to switch your workflows to R.
 
-# Instructor(s)
+# Instructors
 
-|                               |     |                                             |
+|                         |     |                                                                                                                                                                                                                                                                                                   |
 |------------------|------------------|-------------------------------------|
-| ![](images/name-lastname.jpg) |     | Instructor bio, including link to homepage. |
+| ![](images/academy.png) |     | Posit Academy is Posit's internal team of data scientists and educators. We provide a cohort-based, mentor-led, hands-on data science apprenticeship for working professionals. It is delivered on an online platform with interactive coding exercises and frequent cross-learner collaboration. |
 
 : {tbl-colwidths="\[25,5,70\]"}