From c25ca46544fffd395c9f8140f773b70a65fabf4d Mon Sep 17 00:00:00 2001 From: Ben Steinberg Date: Thu, 12 Jan 2017 21:56:09 -0500 Subject: [PATCH] hone in -> home in (#517) --- EDA.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EDA.Rmd b/EDA.Rmd index 322d6a9..550e2ff 100644 --- a/EDA.Rmd +++ b/EDA.Rmd @@ -10,7 +10,7 @@ This chapter will show you how to use visualisation and transformation to explor 1. Use what you learn to refine your questions and/or generate new questions. -EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will hone in on a few particularly productive areas that you'll eventually write up and communicate to others. +EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will home in on a few particularly productive areas that you'll eventually write up and communicate to others. EDA is an important part of any data analysis, even if the questions are handed to you on a platter, because you always need to investigate the quality of your data. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. To do data cleaning, you'll need to deploy all the tools of EDA: visualisation, transformation, and modelling.