Name: Playing Nice: Using PMML, Python, R, and SAS for Production Analytics
Start: 2015-07-23T18:30:00-04:00
End: 2015-07-23T20:30:00-04:00
Location: GWU, Funger Hall, Room 103

Data scientists often use R and Python to train statistical and machine learning models. However, using these models in a production database or Hadoop platform can be difficult. This talk will present several strategies that pair R and Python with SAS and PMML to train sophisticated predictive models and deploy them to in-database and Hadoop environments to make predictions on new data. For a sneak peek, check out: https://github.com/sassoftware/enlighten-integration.

Bio:

Patrick Hall is a senior staff scientist at SAS where he designs new data mining and machine learning technologies. He is the 11th person worldwide to become a Cloudera certified data scientist. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University in 2012.

Schedule:

6:30 - 7:00 Food, drinks and mingling
7:00 - 7:10 Intros and announcements
7:10 - 8:15ish Presentation, Q&A
8:15ish Off to data drinks at Tonic

Marck Vaisman

Abhijit

Patrick Hall

Statistical Programming DC

Data Community DC

Technology

Data Science

Applied Statistics

Data Mining

Machine Learning

R Project for Statistical Computing

Python

Computer Programming

Data Visualization

Data Analytics

Data Management

Big Data

Business Intelligence

Predictive Analytics

Open Source

Playing Nice: Using PMML, Python, R, and SAS for Production Analytics

GWU, Funger Hall, Room 103

Share

Statistical Programming DC

Playing Nice: Using PMML, Python, R, and SAS for Production Analytics

Statistical Programming DC

Details

Members are also interested in