Setting the research goal

A project starts by understanding the what, the why, and the how of your project. What does the company expect you to do? And why does management place such a value on your research? Is it part of a bigger strategic picture or a “lone wolf” project originating from an opportunity someone detected? Answering these three questions (what, why, how) is the goal of the first phase, so that everybody knows...

Overview of the Data Science Process

Following a structured approach to data science helps you to maximize your chances of success in a data science project at the lowest cost. It also makes it possible to take up a project as a team, with each team member focusing on what they do best. Take care, however: this approach may not be suitable for every type of project or be the only way to do good data science. The typical data science...

R Nuts and Bolts Part-II

4.8 Explicit Coercion Objects can be explicitly coerced from one class to another using the as.* functions, if available. > x <- 0:6 > class(x) [1] "integer" > as.numeric(x) [1] 0 1 2 3 4 5 6 > as.logical(x) [1] FALSE TRUE TRUE TRUE TRUE TRUE TRUE > as.character(x) [1] "0" "1" "2" "3" "4" "5" "6" Sometimes, R can’t figure out how to coerce an object and this can result in NAs being produced. > x <- c("a", "b", "c") > as.numeric(x) Warning: NAs introduced...