Earning Criteria

• Competence with R, creating a project in R, using the R Studio interface. Develop a mastery of data cleanup techniques in R and packages that allow for more efficient analysis.  Advanced methods include running more complicated analyses in R such as joining tables and importing various file types.  Develop data visualization skills with static and interactive methods in R, use RMarkdown to display analysis results in HTML and showcase findings in a compelling data-driven narrative.
• Finding Data: Badge holders are introduced to basic principles of Data Storytelling and learn core skills.  Earners are able to:  Identify high-value data for storytelling projects, consider best practices for writing with data, identify insights for stories implement new skills and identify a dataset with which to work for a sample project, and have an introduction to statistical program. R. 
• Extracting and Cleaning Data: Badge earners are introduced to basic operations in R, working with regular expressions and cleaning data in R.  Earners are able to:  explain and demonstrate the strengths of R with respect to data access, clean and organize data, explore text data loaded into R by using regular expressions, and  clean and organize data using subsets and variable selections.
• Analyzing Data: Earners understand components of structured query language (SQL) to explore/analyze data and use statistical methods for assessing data. Earners are able to: import and view data in SQL with the SQLLite Firefox add-on, create queries to perform basic math, build queries to join tables and find potentially hidden, important stories in your data  Build update queries and subqueries to find significant stories in data, and approach data in SQL with the storytelling frame of mind.
• Create data-driven maps, charts, graphs using R for visualizations.  Create visualizations with Tableau Public, examine data to identify outliers, visualize data using R built-in graphics routines to product scatter plots, bar charts, histograms, correlation plots, box plots and time series plots. Utilize ggplot2 to produce plots, know the difference between R build-in graphics and ggplot2, and estimate a confidence interval for the location of the distribution underlying a data sample.
• Learners are able to demonstrate best practices for presenting a compelling data narrative, including methods for ensuring the accuracy of data, writing with numbers and presenting powerful data stories.  Badge holders are able to: organize complex data into story components, negotiate with team members on most effective storytelling approaches, critique the strength and weaknesses of data-driven stories, and write and present a powerful, data-driven story.