|Title||Introduction to Data Science with R|
|Instructors||Adam Ginensky (email@example.com)/Rafael Nicolas Fermin Cota (firstname.lastname@example.org)|
Course Description Introduction to Data Science with R is a fast paced 10 week course. It is an old adage in the analytics community that as much as 90 % of a project consists of analysis of the data prior to the actual modeling. This class will attempt to implement that adage. Modeling can include cleaning of the data, but the heart of any predictive analytics project is understanding the basic properties of the data and the inter connections between the diﬀerent variables. This amounts to doing a number of standard statistical procedures and goes by various names including data wrangling, data munging and ETL (extract, transform, and load) . Therefore at the heart of any modern data analysis is to ﬁrst load the data into a computer and then to use software to understand the basic statistical properties of the data. I should add that visualizing the data is an important component of this process and will be an important component of this class too ! Twenty years ago, this class would have been called ’Statistics’. However in modern statistics, it is pointless to understand a technique without understanding how to implement it code. Similarly I think it is wrong to learn how to perform statistical procedures in R without understanding what one is doing. The output of a statistical function shouldn’t be a number, but rather a better understanding of the data. So the goal of this class is to learn how to understand any data set via using R to visualize and analyze the data.
At the completion of the course, students will be able to do the following:
|Instruction Format||Coursework will have following three important components:
|Assessment||A letter grade A,B,C,D or F for the course will be decided based on:
|TextBook||There will be two texts for the class.
|Pre-Requisite||The pre-requisites are the ability to do basic algebraic manipulations and some programming experience. In particular you must be able to download and install R and RStudio on your computer|
|Time||Lecture Time:Sat 10 am EST/7:30 PM IST
Lab Time:Sunday 10 am EST/7:30 PM IST
|TA Information||Steven Thornton|
|Certification||Students who successfully complete the course will receive an instructor-signed certificate with a letter grade|