Welcome to the CS180 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: python and data science labs. One average, we will have two labs per week, always due on Saturday night. In addition to the python and data science labs, we will have periodic data literacy quizzes. These quizzes are to test your ability to analyze and interpret infographics, and will challenge your ability to critically think about information presented to you.

We highly recommend you clone this repository to gain access to all of the lab specs and starter code. If you have not already, you will need to 1) install git, and 2) configure your SSH key to connect to GitHub.

Once your SSH key is setup, you can clone the repo with the following command: [email protected]:porterjenkins/CS180.git

The 12 data science labs will be in the data science folder. There will be 11 different google colab (.ipynb) files. (.ipynb, jupyter notebook, and colab notebook all mean the same thing) On the days that the labs are due, download the .ipynb and create a new google colab notebook. You can watch a tutorial on how to use google colab here. When you finish the exercises on the data science lab, click the "run all" button in the runtime dropdown menu. Make sure that all of the cells work without any errors thrown before turning the assignments in through Learning Suite or Canvas (whatever your professor is using). Once you've verified that your code works, download the completed notebook file and submit just the notebook file.

The Python labs work a little bit different than the data science labs. Their purpose is to get you more familiar with the Python programming language. Python is used primarily for app development, backend, and data science. There are quite a few data science packages that we will be learning in this course, and by the end you'll become really familiar with the intricies of the Python Programming Language. As with the data science labs, there will be pre-built python files that will make it as easy as possible for you to run your code, as well as make it easy for the TA(s) to grade. Be sure to run your python code through the terminal before turning it in to Learning Suite. If the terminal output doesn't match the expected output on the assignment instructions, it won't pass the test cases in the autograder. We're working on making an accessable python autograder for students in the future, but for this semester it won't be available. You can access the instructions for the Python labs here. This page will have a link to every single lab. Please submit just the .py file on Canvas/Learning Suite.

• Late work will be accepted with a penalty of 20% per day.
• 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.