the data science process data science process setting the research goal retrieving data errors from data entry physically impossible values missing values data cleansing outliers spacestyposerrors against codebook aggregating data data preparation extrapolating data data transformation derived measures creating dummies reducing number of variables merging/joining data sets combining data set operators creating views data exploration data modeling presentation and automation figure step data preparation formatso data transformation will always come into play it' good habit to correct data errors as early on in the process as possible howeverthis isn' always possible in realistic settingso you'll need to take corrective actions in your program figure shows the most common actions to take during the data cleansingintegrationand transformation phase this mind map may look bit abstract for nowbut we'll handle all of these points in more detail in the next sections you'll see great commonality among all of these actions cleansing data data cleansing is subprocess of the data science process that focuses on removing errors in your data so your data becomes true and consistent representation of the processes it originates from by "true and consistent representationwe imply that at least two types of errors exist the first type is the interpretation errorsuch as when you take the value in your