"Data science is a concept to unify statistics, data analysis, machine learning, and their related methods in order to understand and analyze actual phenomena with data." - https://en.wikipedia.org/wiki/Data_science
Exasol provides you with the functionality to do Data Science and Machine Learning, and to do this within Exasol, you can use the UDF Scripts with Script Language Containers Flavors (refer to the GitHub repository for more information), with support for common Data Science and Machine Learning libraries. The Flavor python3-ds-*, for example, provide the script language python3 with the libraries numpy, pandas, scikit-learn, Keras, Tensorflow, and many more. Furthermore, it provides Pandas Dataframe support for accessing and emitting data in Exasol.
Along with many common python libraries for Data Science and Machine Learning, we also provide the Script Language Container Flavor fancyr-*, with an extensive collection of R libraries, such as Dplyr, Caret, knitr, Lubridate, and many more. If you miss your favorite library, you can extend any of our Script Language Container Flavors, and if we see enough interest in a library, we might integrate into our future releases of the Flavors.
We provide tutorials and examples of how you can implement your Data Science and Machine Learning workflows in the following GitHub repository, as Juypter Notebooks. We typically orchestrate the Data Science and Machine Learning workflows with Exasol interfaces, such as pyexasol, r-exasol or JDBC, such that we can combine Data Science and Machine Learning within the Exasol Database with Data Science and Machine Learning with external tools.
