SQL Server 2017 Machine Learning Services with R: Data exploration, modeling, and advanced analytics by Tomaz Kastrun & Julie Koesmarno

Author:Tomaz Kastrun & Julie Koesmarno [Kastrun, Tomaz]
Language: eng
Format: epub
Tags: COM021050 - COMPUTERS / Databases / Servers, COM062000 - COMPUTERS / Data Modeling and Design, COM018000 - COMPUTERS / Data Processing
Publisher: Packt Publishing
Published: 2018-02-27T00:00:00+00:00

Summary

This chapter has covered important functions (among many others) for data manipulation and data wrangling. These steps are absolutely and utterly important for understanding the structure of the dataset, the content of the dataset, and how the data is distributed. These are used to mainly understand frequencies, descriptive statistics, and also some statistical sampling, as well as statistical correlations.

These steps must be done (or should be done) prior to data cleaning and data merging in order to get a better understanding of the data. Cleaning the data is of the highest importance, as outliers might bring sensitive data (or any kind of data) to strange or false conclusions: it might also sway the results in some other direction. So, treating these steps as highly important by using the powerful rx- functions (or classes) should be the task of every data engineer, data wrangler, as well as data scientist. The next chapter will be focused on RevoScaleR functions for predictive modeling, mainly focusing on creating models and running the predictions against these models.

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