Welcome to the CS280 course repository! We are happy to have you taking this class, and we hope that you're as excited about the up and coming field of data science as we are. In this course there are two types of labs: project and tool labs. There slated to have one lab per week. We try to alternate between Tool Labs and Project Labs. The Tool Labs are meant to introduce you to new tools, and the Project Labs are meant for you to implement the tools you learn in a semester long data workflow project that you can put on your resume. The details for each type of lab will be below. If there is reference code for a lab, it will probably be in this repo.

There will be 6 project labs throughout the semester. Roughly one every other week. These labs are meant for you to implement the tools that you've been learning. For example, we use a workflow management software called Airflow pretty frequently in this class, and you learn it in Tool Lab 0, but apply the tool in Project Lab 1. These labs will pretty much take you through the entire data science pipeline, and we will have a Project Lab for every major section of the pipeline. The Project Labs are worth about 25% of your grade, and are worth 35 points each.

The purpose of the Tool Labs are to teach you new softwares and tools. These will be a fairly basic introduction to a few tools (Maybe 3-5 different tools per tool lab, with the exception of the Introduction to Airflow lab). We don't go in depth into any of the tools except for the ones that we are using in the project labs. But we highly recommend doing additional research into the tools we teach you all. We only have a limited time in this class, and because there are so many different tools out there, we have to fly through some of them. But all of the ones we show to you are heavily used in the industry.

• Late work will be accepted with a penatly of 10% per day not including weekends
• If you need an exception, email the TAs or the professor, and we will most likely give the extension. If you don't email us before the deadline, we're much less likely to give the extension.
• Once your work has been graded, please reach out to the TA during office hours with any questions regarding your grading. They will explain the reasoning for any deducted points. If you feel like their reasoning isn't just, reach out to the professor. This is the same for the python labs, the autograder might not work for your lab, so come in to the TAs office hours to see if it was the autograder, or just your code.
• Do not share your completed code on the internet after the class.
• Please be respectful to the TAs and professor, they're working really hard to make this class a good experience.