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Submitted URL: https://email.jmp.com/e3t/Ctc/W3+113/d2rC1c04/VVZFg_1lfssxW3vGCSN76Mb2JVkMXxJ54NChkMSn_mM5nR32W50kH_H6lZ3m7W2NWg3D2MKV...
Effective URL: https://www.jmp.com/en_us/events/live-webinars/non-series/decoding-python-using-jmp-pro-31oct2023.html?utm_campaign=...
Submission: On October 18 via api from US — Scanned from DE
Effective URL: https://www.jmp.com/en_us/events/live-webinars/non-series/decoding-python-using-jmp-pro-31oct2023.html?utm_campaign=...
Submission: On October 18 via api from US — Scanned from DE
Form analysis 2 forms found in the DOM
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<input class="searchButton" type="submit" value="Search">
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<script type="text/javascript">
function clientFormValidationError() {
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function webserviceValidationError() {
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LIVE WEBINAR
DECODING PYTHON: SHORTEN THE TIME FROM DISCOVERY TO MODEL DEPLOYMENT USING JMP
PRO
Date: October 31, 2023
Time: 2:00 p.m. ET
Location: Online
Registration: Free
Python is one of the most widely-used programming languages in data science and
machine learning. Its versatility and power have made it a popular choice for
analyzing, processing, and visualizing data in virtually every industry.
However, as data sets become more complex and difficult to manage, it can be
challenging to leverage the full potential of Python for data analysis.
In this webinar, you’ll learn more about JMP Pro, a powerful data analysis
software tool that provides an intuitive and user-friendly interface for data
visualization, modeling, and analysis. With the release of Model Screening, JMP
Pro has become even more versatile, allowing users to integrate Python into data
analysis workflows, further leveraging the power of Python throughout the
organization.
During this webinar, you will learn how to:
* Use JMP Pro to visualize, explore and clean complex data sets.
* Quickly screen, identify and prototype the best model that fits the data.
* Design and run your model experiments for hyper parameter tuning.
* Deploy JMP Pro and Python workflows for real-world applications.
--------------------------------------------------------------------------------
SPEAKER
RUSS WOLFINGER
Russ Wolfinger, PhD, is the Director of Scientific Discovery and Genomics at JMP
Statistical Software LLC, a SAS company specializing in interactive desktop
software for dynamic data visualization and analysis.
In this role, Russ leads a team in research and development of JMP-based
software solutions in the life sciences. He joined SAS in 1989 after earning a
PhD in Statistics from North Carolina State University (NCSU). For ten years, he
devoted his efforts to developing and promoting statistical procedures for mixed
models and multiple testing. In 2000, he started the Scientific Discovery
department at SAS and JMP.
Russ is co-author of more than 150 publications, 4 books, and is the most cited
author at SAS. He is a fellow of both the American Association for the
Advancement of Science and the American Statistical Association, an adjunct
faculty member at NCSU and the University of North Carolina at Chapel Hill, and
a Kaggle Competition Grandmaster.
In his free time Russ enjoys family beach trips, building with wood and stone,
golf, Brazilian jiu-jitsu, coaching volleyball, and philosophy.
--------------------------------------------------------------------------------
SPEAKER
KEMAL OFLUS
Kemal Oflus is a Principal Systems Engineer for JMP Statistical Discovery, which
creates interactive and highly visual statistical discovery software designed
for scientists and engineers. He supports sales and customer development in the
Southwest region of the U.S. as technical lead.
An applied physicist by training, Kemal previously worked as a rocket scientist
using statistical methods to support risk assessment on the design and
simulation of several projects for NASA and U.S. Air Force. He is a member of
the American Society of Quality and the American Statistical Association. Kemal
regularly delivers talks and keynotes on data mining and predictive modeling; he
also teaches data science at the University of California Riverside as a
part-time lecturer.
--------------------------------------------------------------------------------
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