Congrats on making it all the way through! We hope y'all learned something cool the last few weeks.
| Quick Links | Content Link | Last Updated |
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| Showcase Submissions |  |
11-21-2021 |
| Leadership Slides |  |
11-21-2021 |
We've learned quite a lot this semester.
We started off with the basics of working with data in Python, learning how to manipulate columns and observations. We moved on to more advanced topics, learning to clean messy data, or get it into the formats we needed. After that, we jumped into building visualizations, and made them useful for exploring trends through interactivity.
Halfway through the semester, we took an aside to talk about GitHub, and how to share our code through Git. Shortly after, we stepped into machine learning, building up the human intuition behind models, seeing some behind-the-scenes of several algorithms, and capping off with a runthrough of classification metrics.
All the content will live on in some format at github.com/ishaandey/node, if not at this website.
We're always looking to improve here, and your thoughts are key piece of making that happen. For starters:
- How were projects? Was there enough structure? Too much structure?
- What'd you learn? How'd that compare to what you expected to learn?
- Which parts of Node frustrated you? Which did you look forward to?
- Would a reverse classroom style have worked better? I.e. If we self-taught half the lesson before workshop, then practice during?
- Would you have liked more social events? Even if they were online? What about on Grounds?
- How were labs? Helpful? Too much new content? Too long?
Slack us what you think!
There's so many places to go from here.
- Continue your journey here at Forge, and consider taking Node Pro next semester!
- Take some UVA classes! The instructors would be more than happy to suggest Stat, CS, and DS courses for all majors & skill levels
- Make something cool! Pick a new dataset, refine a project from class, there's no wrong expeience here
- Talk to a professor! No matter which department you're in, there's bound to be folks who need data experience — I've worked in places from sociology to biology.
- Read! There's tons of good books out there. If you're looking for something technical, try An Introduction to Statistical Learning by James et al. On the ethics side, I've recently read Weapons of Math Destruction by Cathy O'Neil.