Welcome to week 1! Although we will not meet in class until the end of this week (Monday, October 18th), I do not want to waste time in getting started. We only have 7 weeks together (😢) so I want to pack each week as full as possible. I am assuming you have fully read the syllabus by now but if not please do so that you understand how this class is structured and what is expected of you. I will spend the first few minutes of class on Monday re-iterating a few points so that we are all on the same page but I do not want to spend 30-60 minutes doing so, so make sure you read (and re-read) it thoroughly.
Please download the materials for Monday’s class:
|Customer churn data||Data|
|Cincy crimes data||Data|
The focus of this week is two-fold:
- To get yourself (re-)introduced to R and the RStudio IDE.
- To introduce you to base R data selection/manipulation/cleaning techniques.
Below outlines the reading and homework that you complete before next week’s class. The skills and functions introduced in these readings will be necessary for Monday’s in-class activities. If you have any questions or concerns your first step should be going to our Canvas Discussions and posting your issue. You and your classmates should be monitoring Canvas to help each other out. In addition, I will also be watching them and will chime in when necessary but my hope is that this will be a social process where everyone contributes to knowledge advancement.
1. Introduction to R and the RStudio IDE
First, get yourself (re-)introduced to R and the RStudio IDE by reading and completing the following tutorials.
- Complete Homework #1 located in this week’s folder.
- One person from each group will submit via Canvas the group’s .R script and Word document.
- This homework assignment is due by 9AM, October 25, 2021.
- BEFORE next week’s class on October 25th, read Chapter 27 sections 27.1 through 27.5 of R for Data Science.
- As you read, check your answers for the guided reading with this solutions manual.
- On the course website, read the pages for the midterm and final project. We’ll discuss them next week.
See you in class on Monday!