In our previous section of the series we discussed the impact of missingness and techniques to address this. In this final section of the series we look at how we can use drag-and-drop tools to accelerate our EDA. As mentioned at the beginning of this series, SAS Viya offers multiple
In our last blog we explored the potential impact of missingness in data in terms of its impact on models which require complete case analysis. We took a simple view that data was missing with an equal, independent, probability for any given model input. This week we explore cases where
In the previous section of this series we discussed ways of assessing the relationship between variables. This week we change the focus to the shape and sparsity of our dataset. One area of Explanatory Data Analysis which we’ve missed so far is the impact of missingness in data. Having missing
In the previous section of this series we looked at basic summary statistics. In this article we start to consider the relationships between variables in our dataset. As part of your Explanatory Data Analysis it is worth looking for correlation between variables. Generally, when referring to correlation we mean the
Following on from my last blog introducing the series, in this section, we’ll take a first look at Explanatory Data Analysis with basic summary statistics. Getting started with a new dataset in analytics can be daunting. It can help when first looking at a dataset to start with basic summary
Following on from my introductory blog series, Data Science in the Wild, we’re going to start delving into how you can scale up and industrialise your Analytics with SAS Viya. In future blogs we will look at how you can augment your R & Python code to leverage SAS Viya
