Scala: The unpredicted lingua franca for...

It was true until pretty recently that data scientists’ languages of choice to manipulate and make sense out of data were Python, R, or MATLAB, which led to split in the data science community and duplication of efforts in languages offering similar sets of functionality. Then distributed technologies came out of the blue, most using a convenient and easy-to-deploy platform, the JVM. Data scientists are now part of heterogeneous teams that face many problems and must work toward global solutions together, including a new responsibility to be productive and agile in order to have their work integrated into platforms. This is why technologies like Apache Spark are so important and are gaining this traction from different communities. And even though some bindings are available for legacy languages, all the creative, new ways to analyze data are done in Scala. Using a fully productive and reproducible environment combining the Spark Notebook and Docker, Xavier Tordoir explore what it means to do data science today and why Scala succeeds at coping with large and fast data where older languages fail. Xavier then introduce and summarize all the new methodologies and scientific advances in machine learning that use Scala as the main language, including Splash, mic-cut problem, OptiML, needle (DL), ADAM, and more, and demonstrate how these programs work for data scientists by enabling interactivity, live reactivity, charting capabilities, and robustness in Scala—things that were still missing from the legacy languages.