List of Tables

Chapter 2. The data science process

Table 2.1. A list of open-data providers that should get you started

Table 2.2. An overview of common errors

Table 2.3. Detecting outliers on simple variables with a frequency table

Table 2.4. An overview of techniques to handle missing data

Chapter 3. Machine learning

Table 3.1. Confusion matrix example

Table 3.2. The first three rows of the Red Wine Quality Data Set

Table 3.3. The findings of the PCA

Table 3.4. How PCA calculates the 11 original variables’ correlation with 5 latent variables

Table 3.5. Interpretation of the wine quality PCA-created variables

Table 3.6. The first three rows of the Red Wine Quality Data Set recoded in five latent variables

Chapter 4. Handling large data on ...

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