Materials and Resources
This is the crux of everything. If you read one part of the syllabus, let it be this one.
Lectures
Tuesdays and Thursdays will, for the most part, be regular lectures where I drone on about something you'll need to know to do the assignments and exams.
Wednesdays are special: those are flipped lecture days, where you (the students!) will ad-lib, answering questions from lectures, posing some of your own, and working through those as well.
Every three weeks, we'll hold a review session where I will specifically address any questions you have on the assignments, exams, or lecture material. Come if you have any questions, or want to help answer some questions.
Textbooks
There are no required textbooks. If, however, you wish to purchase a reference textbook, I would highly recommend the following:
- Grus, Joel. Data Science from Scratch: First Principles with Python (1st ed., 2015) ISBN-13: 978-1491901427.
- McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (1st ed., 2012) ISBN-13: 978-1449319793.
- Shaw, Zed. Learn Python the Hard Way (3rd ed., 2013) ISBN-13: 978-0321884916.
- Matthes, Eric. Python Crash Course (1st ed., 2016) ISBN-13: 978-1593276034.
JupyterHub
This is the primary point of interaction for homework assignments and exams. Jupyter notebooks posted for lecture will be accessible from the general web (instructions to follow), but homework assignments and exams will be accessible through this link:
It's only accessible from on-campus, or with a campus VPN. Check out this EITS webpage on remote access if you need assistance.
Slack
This is the primary point of interaction for asking for / offering help. I will answer questions when I can, but also I encourage everyone to help each other out, too!
https://eds-uga-csci1360.slack.com/
I will send out invites to the Slack channel using your UGA email address.
GitHub
This isn't a requirement, but is here for your reference. I'll post all the Jupyter lecture slides here as I release them.