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 This repository will house all the materials for the first course in the data science specialization using R, developed for the UO COE.
 
 ## Learning Objectives
-- Be able to use version control with R via Git through RStudio Projects
-- Be able to use R Markdown to create reproducible dynamic reports
-- Be able to read "flat" (i.e., rectangular) data into R and explore 
-- Choose the tidyverse appropriate for introductory data science tasks (pipeline)
-- Understand and create different types of data visualizations
-- Be able to clean up a dataset using scripts in R
-- Be able to perform basic data wrangling and transformations in R
-- Understand the R package ecosystem (how to find them, install, load, and learn what they do)
+- Use version control and GitHub with R via Git through RStudio Projects
+- Use R Markdown to create reproducible dynamic reports
+- Read "flat" (i.e., rectangular) data into R
+- Explore specific vectors in data frames and perform preliminary exploratory data analysis in R
+- Choose the tidyverse package appropriate for introductory data science tasks in the Grolemund & Wickham pipeline
+  
+  ![](http://moderndive.netlify.com/images/tidy1.png)
+  
+- Understand when a visualization is appropriate based on data types and create different types of data visualizations
+- Clean up a dataset using scripts in R
+- Perform basic data wrangling and transformations in R
+- Understand the R package ecosystem (how to find them, install them, load them, and learn what they do)
 
 
 <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.