Book Image

SQL Server 2017 Machine Learning Services with R.

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R.

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

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...