![data prep zone.png](W3380wMGvFzO)

[Data Preparation Channels/Sales/Providers Zones](flow_zone:63RmgxK) blend the datasets from disparate sources and apply a first pass of data cleansing.

- [compute_Product_sales join recipe](recipe:compute_Product_sales) adds the market brand name and unit price to the raw sales dataset.
- [compute_Product_sales_prepare recipe](recipe:compute_Product_sales_prepared) process the date column as a Date type, after which we extract the date_year and date_week of sales. We will later use these two columns as a part of the join key with the market outreach data. Also a prepare steps computes the product ```revenue = product_quantity * unit_price``` which is a main feature for the analysis in the rest of this project.
- Prepare recipes [compute_Omnichannel_campaign_data_prepared](recipe:compute_Omnichannel_campaign_data_prepared) and [compute_Providers_data_prepared](recipe:compute_Providers_data_prepared) and [compute_Providers_data_prepared](recipe:compute_Providers_data_prepared) apply data cleaning steps. 

[Weekly Sales and Marketing by HCP  (Account ID)](flow_zone:CUXyF7d) group the sales data for each account at a weekly level. 
- [compute_Product_sales_by_account_id recipe](recipe:compute_Product_sales_by_account_id) groups the sales data  by account_id, product_id, date_year and date_week to aggregate the weekly product sales sum for each account. 
- Join recipe [compute_Product_sales_by_acc_joined](recipe:compute_Product_sales_by_acc_joined) adds all available marketing outreach and providers characteristics to the more complete sales dataset. 