Thursday, November 29, 2018, 6:00 PM – 8:00 PM
A KPMG Diversity in Data event
Data related disciplines, from machine learning to data engineering, robotics and more, continually reshape the world we live in. In a field that reinvents today and builds up tomorrow, it is more crucial than ever that everyone has a voice.
Diversity in Data aims to celebrate the achievements of data practitioners and leaders today. We celebrate and support diversity in data professions – within the workplace as well as within the increasingly automated world they shape.
We hope that you can make the date and join a community of like minded people to network, share and contribute with people who are helping our industry move forward.
Speaker: Xavier Conort, Chief Data Scientist @ Data Robot
Data Scientists have been highly successful at automating modelling through Machine learning, and continue to build capabilities to extract powerful insights at an impressive pace.
On the other hand, Statisticians have been attempting to manually build complex and robust models with features from Generalized Linear Models (GLMs), such as p-values, exponential distributions, link functions, offsets and mixed models.
These GLMs functions are little-known by Data Scientists while Statisticians may dismiss Machine Learning tools that they find too complex, calling them “black boxes”.
Are Statisticians missing something here that could present important opportunities to help them find patterns and build solutions for the increasingly larger and more complex ranges of data?
This presentation will show that Statisticians and Data Scientists can complement, and learn important practices from each other.
The Xgboost package, one of the most popular open source projects, is a good example of such collaboration.