lecy
commented
5 years ago I have looked over our options from the Doodle Poll, and have decided to schedule three review sessions to ensure everyone can make at least one time slot. I am also coordinating times with the CPP 523: Program Eval I review sessions so you can piggy-back meetings if convenient. The times will be as follows:
Mon 12pm - 1pm: 523 Program Eval Mon 1pm - 2pm: 526 Data Science
Tues 5pm - 6pm: 523 Program Eval Tues 6pm - 7pm: 526 Data Science
Wed 5pm - 6pm: 526 Data Science Wed 6pm - 7pm: 523 Program Eval
LOCATION: You can attend the session in-person if you are on campus, or virtually: Policy Analytics Lab: 4th Floor of UCENT (take a left off the elevators) Zoom session: https://asu.zoom.us/j/944016877 You are welcome to schedule virtual office hours outside of the review sessions as well:
https://calendly.com/lecy/15min?month=2019-08
I find that a lot of this content is easier to digest by walking through a couple of examples rather than trying to make sense of the jargon. If you are finding something confusing, don't hesitate to set up a time to chat!
Best,
Prof Lecy
Greetings Class -
Welcome to CPP 526: Foundations of Data Science I. This class introduces you to core concepts in the exciting and fast-growing field of data science, focusing specifically on data programming in the R language.
This email is a bit lengthy but please read it carefully as it has important details about the class.
I have scheduled two ONLINE ORIENTATION sessions for the class so that I can give you an overview of what to expect and you can ask questions before the semester is in full swing:
Wednesday Aug 21, 7pm Thursday Aug 22, 10am Via Zoom: https://asu.zoom.us/j/944016877
I will record these sessions for those that cannot attend.
You can get a jump on the class before the official start on Thursday. Try to complete as many items as you can:
(1) Read the Syllabus: Due to the nature of the material is the course, we will be using a custom course shell built with GitHub, a collaboration platform used by the data science community. This format allows us to more easily share data and R code for assignments and lectures. The course shell is located here: https://ds4ps.org/cpp-526-fall-2019/
(2) Install Software: We will use R and R Studio Desktop for this course. Both are free. You can learn more about these programs from CH-01 and CH-02 of the course text, and you can download them from the R Network and R Studio pages.
(3) Create a GitHub Account: The course shell includes a "Get Help" tab that allows you to post questions about lectures and labs. You need a GitHub account to post questions, and for collaboration on some projects.
(4) Indicate Review Session Availability: You can schedule a virtual meeting anytime (see the syllabus for details about scheduling), but I will also hold a couple of review sessions each week to discuss material and answer questions about labs. Since this is an asynchronous course, I can't guarantee we can find times that accommodate everyone, but I can record sessions and post them for those that can't make it. Complete this DOODLE POLL to indicate your availability.
(5) Introduce Yourself on YellowDig: We will be using a platform called YellowDig for discussions. For your first post this week, please introduce yourself to the class and say something about (a) your previous experiences with data analytics, and (b) why you joined this class/program.
(6) Preview Data-Driven Documents: Although you will do analysis in R, you would not send a client computer code as your final product. The analysis needs to be packaged as a report, website, dashboard, or similar format. The R community has developed some really cool tools to make it easy to combine computer code with regular text, and package it in a variety of formats. Markdown is a simple convention for formatting text. R Markdown allows you to combine R code with regular text. Read this overview of data-driven documents to get a sense for how they work. I am looking forward to working with you all this semester!
Prof. Lecy