As I work on getting a Data Analyst Certificate from Data Camp. I plan on working on more Analysis type projects and show case my work. The structure of this repo is simple. Each file will have a Jupyter Notebook and a dataset file or folder (depending on the number of datasets used)
The brief: The client is generally a low-cost retailer, offering many promotions and discounts. We will need to focus on such keywords. We will also need to move away from luxury keywords and topics, as we are targeting price-sensitive customers. Because we are going to be tight on budget, it would be good to focus on a tightly targeted set of keywords and make sure they are all set to exact and phrase match.
Based on the brief above we will first need to generate a list of words, that together with the products given above would make for good keywords. Here are some examples:
Products: sofas, recliners Words: buy, prices The resulting keywords: 'buy sofas', 'sofas buy', 'buy recliners', 'recliners buy', 'prices sofas', 'sofas prices', 'prices recliners', 'recliners prices'.
As a final result, we want to have a DataFrame that looks like this:
| Campaign | Ad Group | Keyword | Criterion Type |
|---|---|---|---|
| Campaign1 | AdGroup_1 | keyword 1a | Exact |
| Campaign1 | AdGroup_1 | keyword 1a | Phrase |
| Campaign1 | AdGroup_1 | keyword 1b | Exact |
| Campaign1 | AdGroup_1 | keyword 1b | Phrase |
| Campaign1 | AdGroup_2 | keyword 2a | Exact |
| Campaign1 | AdGroup_2 | keyword 2a | Phrase |
The Rebrickable database includes data on every LEGO set that has ever been sold; the names of the sets, what bricks they contain, what color the bricks are, etc. It might be small bricks, but this is big data! In this project, you will get to explore the Rebrickable database and answer a series of questions related to the history of Lego!