How to succeed
Applied computational statistics for analytics
"How do I study for this course? How do I follow?"
This course is a blend of lectures, hands-on labs, and homework. These three components are the core of the course and will help you learn. I lecture synchronously, but I record and make available videos of each lecture.
Lectures in this course use a combination of hand-written notes and slides. Be prepared to:
take notes (as if we were in a classroom with a whiteboard),
download slides before lecture to follow along, supplementing with notes,
try out code & download new packages,
work in teams,
participate in interactive quiz questions throughout.
As they use copyrighted material, students are not allowed to post course materials anywhere (on the internet, on their sites, in other courses, etc.). Once updated, lecture slides will be released with a link here under a Creative Commons 3.0 License; stay tuned for more info.
Software & Statistics help
New to R? New to Python? No idea what Markdown is? Can't remember something "basic" in stats? Just need a refresher?
Not to worry!
Writing scripts: One of our reference texts has a lot of background and examples - it builds scripts bottom-up! You can always start there. If you want to browse a few other resources, these seem excellent choices:
A site with various tutorials on R and statistics
MIT Open Courseware on Python
A lovely (and pretty complete?) list of resources for learning R: