Given Canvas does not let us put in cutoffs, here is a Google spreadsheet that will help you figure out your grade. The link will prompt you to make a copy of it.
Technology Install Directions
[If you want help, InstallFest is in the Technology Engagement Center at Th 8/31 from 1 pm – 6 pm, Friday 9/1 from 11:30 am – 4 pm, and Tuesday 9/5 from 9 am – 5 pm]
This class uses Anaconda’s Individual Edition. It’s a free open source distribution containing Python, Jupyter Notebook, and (nearly) everything for data science in Python. Go to Anaconda’s Individual Edition and download the data science toolkit for your operating system with Python version >= 3.7. If you have trouble installing, check the anaconda documentation. When you are done, we recommend trying to open a Jupyter Notebook (enter “jupyter notebook” at a command line terminal, run Jupyter Notebook like a regular Windows program, or run the Anaconda navigator program and select Jupyter Notebook) and begin familiarizing yourself with the Jupyter Notebook documentation.
We will be using Gradescope to submit labs and projects. If you are unfamiliar with Gradescope or aren’t sure how to submit your assignment, they created a Gradescope help document for you.
Jupyter Notebook Container
If something happens to your computer or you cannot install Anaconda on it, we’ve reserved containers for you through OIT. Go to the container manager and look for the Pytorch: JupyterLab with Pytorch for Data Science and Machine Learning machines. Make sure you select Pytorch and not the Jupyter: Python and Julia: data science / scientific computing notebooks option.
Click on the button to reserve your instance of the notebook. Once your instance is reserved you can click on Pytorch among your reserved containers, start the server, and upload any necessary files.
Python for Data Science
If you are new to programming in Python, there are a lot of good tutorials available. The official documentation has one: https://docs.python.org/3/tutorial/index.html and google also hosts a good tutorial with videos: https://developers.google.com/edu/python/. If you want a guide that is specific to transitioning to Python from Java, try http://python4java.necaiseweb.org/Main/TableOfContents. Note that if you are new to programming altogether, you do not meet the pre-requisites for the class and should consider CS 101 or CS 116 instead; we are assuming that you have the background to pick up basic syntax and functionality of Python on your own.
If you are new to scientific programming with Python, you may find this NumPy tutorial helpful, along with the NumPy documentation. The Python Data Science Handbook is also a very useful reference the use of Python for data science, including helpful information on commonly used libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn. For a gentler introduction, try this online data8 book developed for U.C. Berkeley’s Foundations of Data Science course and used in CS 116 at Duke.
To get all of the data science libraries you need together with a Python distribution on your local device, look at the Anaconda distribution, available for free. The Anaconda distribution of Python contains everything that you need to be successful in data science with Python, including all Python resources you should need for this course. It includes Python 3 itself, all of crucial libraries for data science (NumPy, Pandas, Matplotlib, scikit-learn, etc.), and development environments (notably the Spyder scientific computing IDE and Jupyter notebooks).
The innovation Co-Lab hosts a variety or trainings, projects, and programming that might be interesting to an aspiring data scientist. The Co-Lab also hosts regular office hours (and you can make an appointment) on a variety of technical subjects.
Academic Resource Center
Want expert consultation about study habits, learning, time management, and more? Check out the Academic Resource Center (ARC) at Duke.
Counseling and Psychological Services
Your thriving is about more than this class. If you need to talk to someone, consider Duke Counseling and Psychological Services (CAPS).