Preppin' Data

 Challenge by Tom Prowse. '55 years of hurt, Never stopped me dreaming!' It was another night of pain for England fans on Sunday evening when they lost yet another penalty shootout in the European Football Championship final. This seems like it has been a common outcome for a lot of the tournaments that England have taken part in over the years, but what does the data agree?  The challenge this week is to analyse the all of the penalty shootouts in the World Cup and European Championships (Euro's) since 1976. Input Data is from Wikipedia ( World Cup & Euro's ) and is two sheets:  World Cup Euro's Requirements Input Data Determine what competition each penalty was taken in Clean any fields, correctly format the date the penalty was taken, & group the two German countries (eg, West Germany & Germany) Rank the countries on the following:  Shootout win % (exclude teams who have never won a shootout) Penalties scored % What is the most and least successf...

The Head Honchos of Chin & Beard Suds Co want to understand how their website is performing. Are the web pages being tested on the right devices and Operating Systems? Are we considering language and cultural references? To conduct this analysis, our website stats are held in a frustrating way but this analysis won't be done just once so we want to set up the flows so they are available in a reusable way to conduct this analysis whenever needed.  Requirements Input the Excel workbook Create a single table for each of Origin, Operating System and Browser Clean up values and percentages Convert any < 1% values to 0.5 If percent of total fields don't exist in any files, create these and make them an integer Create a consistent output for all three files Outputs 3 Files with the following 6 columns: Type (either Origin, Browser or Operating System) Change in % page views This Month Pageviews Value This Month Pageviews % All Time Views Valu...

You can view our solution workflow below and download it here . Full solution workflow. There are 3 main learning objectives for this week's Preppin' Data challenge: Using "Wildcard Unions" & file paths. Using aggregations to calculate "percent of total" fields. Creating Filter calculations. Unioning the data and getting the regions We’ve got five CSVs, one for each region, and all of them have the same data structure – sounds like a call for a Wildcard Union input! Instead of inputting each file separately we can: Applying a Wildcard Union Add one of regional stock files to the canvas. Switch to the Multiple Files tab. Select Wildcard Union . If they share a folder with other files, define a “ Matching Pattern ” of “* Stock Purchases.csv”.  This means only  files that end in “Stock Purchases.csv” will be imported into the union. Click Apply . From here, Tableau Prep automatically unions all the data together for yo...