join the nosql movement 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 figure data science process step data preparation data transformation--you don' need to transform the data much at this pointyou want to search it as is but you'll make the distinction between page titledisease nameand page body this distinction is almost mandatory for search result interpretation combining data--all the data is drawn from single source in this caseso you have no real need to combine data possible extension to this exercise would be to get disease data from another source and match the diseases this is no trivial task because no unique identifier is present and the names are often slightly different you can do data cleansing at only two stageswhen using the python program that connects wikipedia to elasticsearch and when running the elasticsearch internal indexing systempython--here you define what data you'll allow to be stored by your document storebut you won' clean the data or transform the data at this stagebecause elasticsearch is better at it for less effort elasticsearch--elasticsearch will handle the data manipulation (creating the indexunder the hood you can still influence this processand you'll do so more explicitly later in this