DocHub provides a seamless and user-friendly option to join data in your Advertising Contract. Regardless of the characteristics and format of your document, DocHub has all it takes to ensure a fast and hassle-free editing experience. Unlike similar services, DocHub stands out for its excellent robustness and user-friendliness.
DocHub is a web-centered solution enabling you to tweak your Advertising Contract from the convenience of your browser without needing software installations. Because of its simple drag and drop editor, the ability to join data in your Advertising Contract is quick and easy. With rich integration options, DocHub allows you to import, export, and modify paperwork from your preferred program. Your completed document will be stored in the cloud so you can access it instantly and keep it secure. You can also download it to your hard drive or share it with others with a few clicks. Alternatively, you can turn your document into a template that stops you from repeating the same edits, including the ability to join data in your Advertising Contract.
Your edited document will be available in the MY DOCS folder in your DocHub account. In addition, you can use our editor panel on right-hand side to merge, split, and convert files and reorganize pages within your papers.
DocHub simplifies your document workflow by offering an integrated solution!
We just learned how to take two data sets with the same fields and union them together to make a longer data set. But what if you want to blend multiple data sets with different data? For instance, pretend we want to create this really great 360 view of our customers, so we can build a predictive model from it. Well want to start with as much information about the customers that we can get, before we can determine which variables well want to use in the model. Well take a transaction data set thats summarized to the customer level, showing their total transactions, total spend, etc. Then we might want to use another data set that shows what marketing campaigns they responded to. Maybe, we can even pull in a third data set with their address data showing how far these customers are located from their store locations. To blend these data sets together, well need some information in common amongst all three of these files. That information in common is whats going to tie each record
