You will be asked 10 questions from the list below.
Category  Number  Question 

Projects  1  Describe your role on your team. What specific tasks did you manage, or what were your major contributions? 
2  What was the single most insightful finding from your project? Describe at least two of the design choices made to highlight that finding in one of the charts in your report.  
Summary Measures  3  When might you want to use the Spearman versus Pearson measure of correlation? 
4  When summarizing the centrality of a continuous, numeric variable (e.g. age, income, etc.), when might I want to use the median versus the mean?  
5  Let’s say I summarizing the centrality of a continuous, numeric variable (e.g. age, income, etc.), and I find that the mean is substantially higher than the median. What does that tell me about how the variable is distributed?  
Variability  6  What chart type would you use to visualize the variability of a continuous, numeric variable (e.g. age, income, etc.)? 
7  What chart type would you use to visualize the variability of a categorical variable (e.g. brand, day of the week, etc.)?  
Chart Types  8  What chart type would you use to visualize the correlation between two continuous, numeric variables? 
9  What chart type would you use to visualize the relationship between two categorical variables?  
10  What chart type would you use to visualize the relationship between a categorical variable and a continuous variable?  
Tidy Data  11  (I’ll show you the first few rows of a data frame) Looking at this data frame, would you say it is in a “wide” or “long” format? Why? 
Data Types  12  (I’ll show you the first few rows of a data frame) Looking at this a data frame, how would you categorize each variable type (ordinal, nominal, ratio, or interval)? 
Visualization Principles  13  (I’ll show you a chart) Describe one good and one bad data visualization practice used in this chart. 
Comparisons  14  (I’ll show you a chart) Consider this chart. Describe a different way to visualize the data that would improve the overall interpretability. 
Cleaning  15 
(I’ll show you an Excel sheet) Consider this Excel sheet. Tell me at least two issues would you look for after reading into R using read_excel() .
