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Here's how to get DataQuest's Data Engineering Track missions' content to work on your localhost. Using data from my Valenbisi ARIMA modeling project, I document my steps using PostgreSQL, Postico, and the Command Line to get our DataQuest exercises running out of a Jupyter Notebook.

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Zero to Hero: DATAQUEST's Become a Data Engineer

Here's how to get DataQuest's Data Engineering Track missions' content to work on your localhost. Using data from my Valenbisi ARIMA modeling project, I will walk through steps using PostgreSQL, Postico, and the Command Line to get our DataQuest exercises running out of a Jupyter Notebook.

This will not be a complete repitition of the many resources I used, so be sure to look out for any links I include if it seems I've skipped a few steps.

Important note: In DataQuest, each exercise re-initiates the connection and cursor class of psycopg2 when interacting with the Postgres DB, with no deliberate closing of the connection. When we productionize our scripts, it will be more efficient and correct to use a with statement, which will close the connection once the operations are complete. For the sake of the exercises, I will follow DataQuest's format. I will switch to the with statement as we approach production.

There will be Three Directories in this Repository, each aligning with DataQuest's Data Engineer Track. Each directory will contain a README.md with more details on the content covered in it.

For Non-Commercial Use Only

I highly reccommend participating in this course as a member of DATAQUEST.

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Here's how to get DataQuest's Data Engineering Track missions' content to work on your localhost. Using data from my Valenbisi ARIMA modeling project, I document my steps using PostgreSQL, Postico, and the Command Line to get our DataQuest exercises running out of a Jupyter Notebook.

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