"It takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!" — data scientist in Wonderland.
Data science has to at least keep up with the constantly growing data volume and complexity, and ideally it should outpace data to solve potential problems arising while processing data.
In this talk you'll see how Scala libraries such as Saddle, Smile and Spark, benefitting from Scala functional aspects, its big data ecosystem and its hybrid functional/object-oriented approach, help data science to meet the constantly evolving requirements of infrastructure, while easing the analysis and widening the possibilities of descriptive statistics, data processing and machine learning.
Using click prediction in web advertising as an example, we'll explore possibilities, benefits and ways to advance Scala for data science.
Anastasia Lieva, TabMolievAnastazia
For more than 6 years Anastasia has been into AI — for the benefit of humanity, of course! Her professional interests are in the area of machine learning and functional programming. Anastasia currently works as a data scientist in RTB area at the French company TabMo. She also teaches Data Science at Polytech Montpellier. Anastasia is an active participant in Montpellier TechHub, she created and runs the Big-Data-Montpellier and Functional-Programming-Montpellier communities and organizes their meetings.