October 23-27, 2016
New York
Delivering on the promise of data science
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All level tracks Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level


Agenda Overview – New York 2016
Pre-Conference Workshop: Sunday, October 23, 2016
Three hour WorkshopRoom 1A02
R Bootcamp: For Newcomers to R
Max Kuhn, Pfizer
Full-day WorkshopRoom 1A03
Big Data: Proven Methods You Need
to Extract Big Value

Vladimir Barash, Graphika

Pre-Conference Workshop: Monday, October 24, 2016
Full-day WorkshopRoom 1A23
R for Predictive Modeling:
A Hands-On Introduction

Max Kuhn, Pfizer
Full-day WorkshopRoom 1A24
Advanced Methods Hands-on:
Predictive Modeling Techniques

Dean Abbott, Abbott Analytics, Inc
Sponsored by: Dell Statistica Logo

DAY 1, Tuesday, October 25, 2016
(PAW Healthcare & PAW Financial run in parallel on this day - dual registration required)
8:00-8:45am Registration & Networking Breakfast
Room: Exhibit Area 1A
8:45-8:50am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
Room: 1A25/1A26
8:50-9:40am
KEYNOTE
Weird Science: How to Know Your Predictive Discovery Is Not BS
Eric Siegel, Predictive Analytics World
Room: 1A25/1A26
9:40-10:00am Diamond Sponsor Presentation
An Introduction to DataRobot Machine Learning Platform
Greg Michaelson, DataRobot
Room: 1A25/1A26
10:00-10:30am Exhibits & Morning Coffee Break
Room: Exhibit Area 1A
  Track 1: All Levels
Room: 1A23
Track 2: Expert/Practitioners
Room: 1A25/1A26
Track 3: Uplift Modeling
Room: 1A27
10:30-11:15am Model Overstatement Retail Predictive Analytics Uplift Modeling
Developing an Analytics Discipline in Recognizing Model OverstatementAll level tracks
Richard Boire, Environics Analytics
Case Study: SmarterHQ
The Revolution in Retail Customer Intelligence

Dean Abbott, SmarterHQ
Case Studies: Telenor;
US Bank
All level tracks
Uplift Modeling: Optimize for Influence and Persuade by the Numbers
Eric Siegel, Predictive Analytics World
11:20am-12:05pm Predictive Analytics for Sentiment Economic Forecasting Uplift Modeling
Case Study: A regional Bank in the Northeast US
Sentiment Unchained: Accelerating Analytics to Understand Customer
Behavior
All level tracks

Joseph Blue, MapR
Predicting Changes in Rental Prices in New York City Using Online Restaurant Reviews
Anasse Bari, NYU
Islam Tawfik, Learnvest
Case Study: Miles & More
(from Lufthansa)

Using Predictive Models for Demand Simulation - Purchase, Response and Uplift Modeling in Practice
Thomas Klein & Alexander Funkner, Miles & More GmbH
12:05-1:30pm Lunch in Exhibit Hall
Room: Exhibit Area 1A
1:30-2:15pm
KEYNOTE
Topic: Uplift Modeling
Persuasion Modeling in Presidential Campaigns and How It Applies to Business
Daniel Porter, BlueLabs, Obama for America
Room: 1A25/1A26
2:15-2:35pm Gold Sponsor Presentations

Room: 1A25/1A26
Thought Leadership Model Deployment Uplift Modeling
2:40-3:00pm When Should We Trust Robots with Decisions? All level tracks
Vasant Dhar, NYU
Five Best Practices for Deploying Analytic Models into Operations and Five Common Mistakes
Robert Grossman, Open Data Group

Causality in Business Analytics: Uplift Models and Directed Acyclic Graphs
Dominique Haughton, Bentley University
Jonathan Haughton, Suffolk University
3:05-3:25pm Supply Chain Optimization
Predicting and Managing Behavior In Process Industry Supply Chains All level tracks
Gary Neights, Elemica
3:25-3:55pm Exhibits & Afternoon Break
Room: Exhibit Area 1A
3:55-4:40pm Special Plenary Session
Case Study: Barclays
The Rapidly Changing Big Data Landscape: Balancing the Power with the Dangers of Confusion
Usama Fayyad, Barclays
Room: 1A25/1A26
  Track 1: All Levels
Room: 1A23
Track 2: Expert/Practitioners
Room: 1A25/1A26
Track 3: Uplift Modeling
Room: 1A27
Legal Applications Project Management with Analytics Uplift Modeling
4:45-5:05pm Case Study: LexisNexisAll level tracks
Predicting the Fate of Legislative Bills and Finding Effective User Analytics for Business Decision Making
Michael Cole, LexisNexis
Gene Osgood, LexisNexis
Case Study: The MITRE Corporation
Applying Machine Learning Techniques to Improve Quality

Richard Eng, The MITRE Corporation
Case Study: AIG
Parametric Uplift Regression Models
Matthew Flynn, AIG Science
5:10-5:30pm Acquisition Funnel for Higher Education
Case Study: Becker College All level tracks
Enhancing the Quality of Predictive Modeling on College Enrollment

Feyzi Bagirov, Becker College
5:30-7:00pm Networking Reception
Room: Exhibit Area 1A

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DAY 2, Wednesday, October 26, 2016
(PAW Healthcare & PAW Financial run in parallel on this day - dual registration required)
8:00-9:05am Registration & Networking Breakfast
Room: Exhibit Area 1A
9:05-9:10am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
Room: 1A25/1A26
9:10-10:00am Special Plenary Session
Doing Space-Age Analytics with Our Hunter-Gatherer Brains
John Elder, Elder Research, Inc.
Room: 1A25/1A26
  Track 1: All Levels
Room: 1A23
Track 2: Expert/Practitioners
Room: 1A25/1A26
Track 3: Ads & Marketing
Room: 1A27
  Workforce Analytics Best Practices TV Ad Targeting
10:05-10:50am Case Study: Paychex All level tracks
Predicting Employee Churn with Anonymity

Chip Galusha, Paychex
Q&A: Ask Karl and Dean Anything (about Best Practices)
Dean Abbott, SmarterHQ
Karl Rexer, Rexer Analytics
Case Study:Visible World All level tracks
What Shall I Watch Now? Ensuring the Right TV Ads Are in Front of the Right Audiences
Bob Bress, Visible World
10:50-11:15am Exhibits & Morning Coffee Break
Room: Exhibit Area 1A
11:15-11:35am Social Activism Engagement Workforce Analytics Marketing Applications
Case Study: Change.org All level tracks
Anatomy of a Social Movement: Using Analytics to Change the World

Duncan Lockard, Change.org
Predictive Job Maps: Optimizing a Workforce with a Network of Predictive Models
Pasha Roberts, Talent Analytics, Corp.
Case Study: Bitly
Predictive Analytics for Different Business Types: Optimize All the Funnels
Meina Zhou, Bitly
11:40am-12:00pm Email Marketing
Case Study: Mountain America Credit Union
Maximizing Net New Deposits of a Promotional Email Campaign with Predictive Analytics
Lawrence Cowan, Cicero Group
12:00-1:15pm Lunch in Exhibit Hall
Room: Exhibit Area 1A
1:15-2:00pm
KEYNOTE
Case Study: CA Technologies
The Impact of Analytics and Digital Transformation on Humans

Saum Mathur, CA Technologies
Room: 1A25/1A26
2:00-2:15pm Lightning Round of Vendor Presentations

Room: 1A25/1A26
2:15-3:00pm Expert Panel
Data Prep: Overcoming the Bottleneck and Nailing It
Moderator: Eric Siegel, Predictive Analytics World
Panelists: John Elder, Elder Research, Inc.
Satadru Sengupta, DataRobot
Pini Yakuel, Optimove
Room: 1A25/1A26
3:00-3:30pm Exhibits & Afternoon Break
Room: Exhibit Area 1A
  Track 1: All Levels
Room: 1A23
Track 2: Expert/Practitioners
Room: 1A25/1A26
Track 3: Ads & Marketing
Room: 1A27
Enterprise Platform New Data Sources Leads Scoring, B2B, Sales Funnel
3:30-3:50pm Case Study: Johnson & Johnson All level tracks
Analytics at the Speed of Global Business: Delivering an Analytics Technology Platform
Elena Alikhachkina, Johnson & Johnson
Case Study: The Hive (U.N. Refugee Agency lab)
The Data Set You Can No Longer Ignore: Consumer Engagement with Social Issues

Rita Ko, The Hive
Case Study: IBM
Integrating Analytics and Design Thinking Approaches for Sales Order Propensity Prediction

Pitipong Lin, IBM
3:55-4:15pm Social Data for Predictive Labor Demand
Case Study: Cars.com
Predicting Consumer Review Engagement and Sentiment Using only Readily Available Social and Demographic Data

Michael Spadafore, Marketing Associates
Data-Driven Hiring of Data Scientists All level tracks
Michael Li, The Data Incubator
4:20-5:05pm Thought Leadership Consumer Experience Optimization Product Recommendations - Advanced Methods
Case Study: MIT Int Soc for Chief Data Officers All level tracks
How $1B Companies are Scaling the Chief Data and Analytics Officer Function
Richard Wendell, Tellic
Case Study: Verizon
Best Practices Enhancing Contextual Experience with Predictive Analytics

Madhu Raman, Verizon
Graph Database for complex problems - Creating Personalized Food Item Recommendations at Restaurants
Matthew Burris, Citibank

Post-Conference Workshop: Thursday, October 27, 2016
Full-day WorkshopRoom 1A23
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day WorkshopRoom 1A24
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics
Sponsored by:
Full-day WorkshopRoom 1A11
Hadoop for Predictive Analytics: Hands-On Lab
James Casaletto, MapR Technologies

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