Your goal during EDA is to develop an understanding of your data. Vague, than an exact answer to the wrong question, which can always be made “Far better an approximate answer to the right question, which is often “There are no routine statistical questions, only questionable statistical To do data cleaning, you’ll need to deploy all the tools of EDA: visualisation, transformation, and modelling. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. As your exploration continues, you will home in on a few particularly productive areas that you’ll eventually write up and communicate to others.ĮDA 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. Some of these ideas will pan out, and some will be dead ends. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. More than anything, EDA is a state of mind. Use what you learn to refine your questions and/or generate new questions.ĮDA is not a formal process with a strict set of rules. Search for answers by visualising, transforming, and modelling your data. This chapter will show you how to use visualisation and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short.
0 Comments
Leave a Reply. |