For SQL Server developers, this must have been quite an exhausting chapter. Of course, the whole chapter is not about the T-SQL language; it's about the R language, and about statistics and advanced analytics. Of course, developers can also profit from the capabilities that the new language has to offer. You learned how to measure associations between discrete, continuous, and combinations of discrete and continuous variables. You learned about directed and undirected data mining and machine learning methods. Finally, you saw how to produce quite advanced graphs in R.

Please be aware that if you want to become a real data scientist, you need to learn more about statistics, data mining and machine learning algorithms, and practice programming in R. Data science is a long learning process, just like programming and development. Therefore, when you start using R, you should have your code double-checked by a senior data scientist for all the tricks and tips that I haven't covered in this chapter. Nevertheless, this and the previous chapter should give you enough knowledge to start your data science learning journey, and even kick off a real-life data science project. However, as of SQL Server 2017, R is not the only language supported by SQL Server that you can use for data science.

You will learn about another option, the Python language, in the next chapter.