In ICE #9 we did a “left join” for join1 to link the sales orders and shipments tables. We joined these tables on the variable SalesOrderID and had 1,168 observations in the resulting table. Because SalesOrderID and ShipID are in both tables, Tableau prep keeps only one of them in the merged table. 

Which particle size is optimal for aerosol deposition in the lower airways?

Which Alteryx tools can we use to drop certain observations (e.g., rows that only meet certain criteria, including having all null values)? Select all that apply.

Which Alteryx tool have we used to create new variables and/or change the values of certain variables?

In ICE#9 “join4” we combine the join3 table and the customer master table using the variable CustID and an inner join. This results in a table with 1,168 observations. If we instead link this tables using a left join and the TerritoryID variable, we get a table with 18,937 observations. This increase in observations happens because TerritoryID does not uniquely identify observations in the customer master table. 

What critical input do Agarwal et al. argue is becoming “cheap” (i.e., reduced cost) and is relevant to this course on data analytics and machine learning?

What are various ways the the data cleanse tool can be used within Alteryx? Select all answers that apply. 

Machine learning generates predictions by evaluating thousands (if not millions) of if-then statements. 

Which of the following statements regarding unique identifiers are true? Check all that apply. 

What is meant by saying that machines “learn” or machine learning?