In this chapter, we began exploring the different types of variables: quantitative (interval and ratio) and qualitative variables (nominal, dichotomous, and ordinal). Then, we started the hard work of data preparation; we saw how to find missing values, change the datatype, replace missing values, remove missing entries, order the table, find outliers, and finally, organize multiple sources of data into one.
Next, we discovered the exploratory statistics techniques used to derive features that can guide us in choosing the right tools to extract knowledge from data. We took a look at measures of location such as mean, median, mode, quantiles, and percentiles; measures of dispersion such as range, interquartile range, variance, standard deviation, correlation, and covariance; and measures of shape such as skewness and kurtosis.
Finally, we discussed exploratory visualization...
