For all group collaboration, we’ll be using the dotData Slack Channel. All you need to sign up is an @wisc.edu address.
• CS 301: Data Programming [Python], 3 credits — this is the only for sure course that I’m aware of. It’s part 1 of 2 for their programming sequence.• CS 368: Topics in Computing [R, MATLAB, C++], 1 credit — this offering changes per semester, but these languages are invaluable. R is used for most stats courses, our databases course uses C++, and many engineering courses use MATLAB.• CS 532: Matrix Methods in Machine Learning, 3 credits — NOTE: unlikely this will be part of the Data Science major, but it’s a very good applied course. I was briefly in it before switching into a different course. Seemed interesting. It’s a flipped classroom, so you watch some videos outside, and then work on homework in-class. May or may not be reserved for graduate students next spring? Unsure, but that would be odd.– Pre-Reqs: MATH 222 and (E C E 203, COMP SCI 200, 300 or 302) or graduate/professional standing• Math 234: Calc III, 4 credits — Easier than calc 2, pre-requisite for Foundational courses like the Stats 309/310 sequence (Mathematical Statistics and probability)– Pre-Reqs: MATH 222• Math 340: Linear Algebra, 3 credits — lovely course. Avoid its sibling course, Math 320, if you don’t like differential equations.– Pre-Reqs: MATH 222• Stats 301 or 302: Intro to Statistics, 3 credits — very likely be a pre-requisite for other courses required Stats courses– Pre-Reqs: None for STAT 301; MATH 221 in order to take STAT 302• Stats 327: R, 1 credit — This will just be a useful language, whether for research or other classes. They have intro, intermediate, advanced, all in one semester. If you’re more CS-focused, you could jump for CS 368 – R (caveat, you’ll need to get a credit substitution for 327)• Stats 333: Applied Regression Analysis, 3 credits — honestly wish I could have taken this. Only open to stats majors, it seems.– Pre-Reqs: (STAT 224, STAT 301, STAT 302, STAT 312, STAT 324, or STAT 371) and STAT 327 or concurrent enrollment
SAS and Genetic Algorithms Resources (10/25/2018)
- Some of you asked about a tutorial
- Tetris Example: code and live example (type in tetris on this page once it loads)
If you have more questions about Genetic Algorithms, email Matthew at: email@example.com.
Some reminders from Rachel (from SAS):
The registration for Student Symposium is November 16th, teams of 2-4 with a faculty advisory will compete in a datachallenge with a data set with SAS software to use. The top 3 teams will be highlighted at the Global Forum in April 2019.
If you have any questions for Rachel regarding SAS, contact her at: firstname.lastname@example.org.