Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level| Pre-Conference Workshops: Sunday, April 3, 2016 | |
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R for Predictive Modeling: A Hands-On Introduction  Max Kuhn, Pfizer |
Big Data: Proven Methods You Need to Extract Big Value  Marc Smith, Connected Action Consulting Group |
| Day 1: Monday, April 4, 2016 (PAW Workforce runs in parallel on this day - dual registration required) |
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| 8:00-8:45am | Registration Room: North Registration |
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| 8:00-8:45am | Networking Breakfast Room: Salon 8 & 9 |
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| 8:45-8:50am | Conference Welcome • Room: Golden Gate A Adam Kahn, Rising Media, Inc. | ||
| 8:50-9:40am |
KEYNOTE • Room: Golden Gate A Weird Science: How to Know Your Predictive Discovery Is Not BS Eric Siegel, Predictive Analytics World |
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| 9:40-10:00am | Diamond Sponsor Presentation • Room: Golden Gate A Enabling Data Science for Lambda, Lakes and Bases Dr Avishkar Misra, Oracle 
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| 10:00-10:30am | Exhibits & Morning Coffee Break Room: Salon 8 & 9 |
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| Track 1: All Levels Room: Salon 3 & 4 |
Track 2: Expert/Practitioners Room: Golden Gate A |
Track 3: Financial Services Room: Salon 5 & 6 |
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| 10:30-11:15am | Uplift modeling | Modeling Methods (Algorithms) |  Predictive Investing (VC) |
Case Study: U.S. Bank Uplift Modeling: Optimize for Influence and Persuade by the Numbers Eric Siegel, Predictive Analytics World |
The Five Tribes of Machine Learning, and What You Can Take from Each Pedro Domingos, University of Washington |
Case Study: Microsoft Ventures Predicting Startup Success: Finding the Unicorns among Wildebeests Mukund Mohan, Microsoft Strategy |
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| Analytics in Microsoft's Move to SaaS |
Uplift Modeling | Predictive Audit Planning |
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| 11:20-11:40am | Case Study: Microsoft Predicting User and Device Upgrade Issues Moving to Windows as a Service Hans Wolters, Windows and Devices Group, Microsoft |
Case Study: Telenor Applying Next Generation Uplift Modeling to Optimize Customer Retention Programs Patrick Surry, Hopper | Case Study: General Electric Advanced Analytics and the Corporate Audit Function Sundar Victor, GE Corporate Peter Stansbery, GE Corporate |
| 11:45-12:05pm | Predictive Analytics for SEO and Online Marketing | ||
| Case Study: CanIRank.com Predicting Online Marketing Success: Five Lessons Learned Matt Bentley, CanIRank.com |
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| 12:05-1:30pm | Lunch in Exhibit Hall Room: Salon 8 & 9 |
Lunch & Learn • Room: Salon 5 & 6 Using Apache Spark on the Mainframe to Reduce Fraud in the Financial Sector Paul DiMarzio, IBM  |
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| 12:30-1:00pm |
Book Signing • Room: eZone, Booth 418 Revised and Updated Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die Eric Siegel, Founder, Predictive Analytics World Books are free to PAW Business attendees |
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| 1:30-2:15pm |
KEYNOTE • Room: Golden Gate A Buy or Wait? Consumer-friendly Airfare Prediction or How the Bunny Saves You Money Patrick Surry, Hopper |
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| 2:15-2:25pm | Gold Sponsor Presentation • Room: Golden Gate A How Can We Find the Future on the RadarMap of Big Data? Tatsuo Nakamura, VALUENEX 
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| 2:25-2:35pm | Gold Sponsor Presentation Cognitive Data Science for Predictions Ruban Phukan, DataRPM 
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| Cross-Enterprise Deployment | Predicting Virality on Social Media | Omni-Channel |
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| 2:40-3:00pm | Case Study: Autodesk Adopting Analytics - The Autodesk Journey Adam Sugano, Autodesk |
Case Study: Mashable Understanding Viral Diffusion: Data Science at Mashable Haile Owusu, Mashable | Case Study: CIBC Driving the Omnichannel Experience with Predictive Analytics Rebecca Pang, CIBC |
| 3:05-3:25pm | Cross-Enterprise Deployment | ||
| Case Study: Hewlett Packard Enterprise Operationalizing Analytics: 10 Key Process Areas for Embedding Predictive Analytics into Business Operations, Applications and Machines Ken Elliott, Hewlett Packard Enterprise |
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| 3:25-3:55pm |
Exhibits & Afternoon Break Room: Salon 8 & 9 |
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| Track 1: All Levels Room: Salon 3 & 4 |
Track 2: Expert/Practitioners Room: Golden Gate A |
Track 3: Financial Services Room: Salon 5 & 6 |
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| Crowdsourcing Predictive Analytics | Uplift Modeling | Cross-Enterprise: Revenue Modeling & Predictive Maintenance
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| 3:55-4:40pm | Case study: GE, Facebook and Walmart
What's Possible at the Cutting Edge of Predictive Modeling Anthony Goldbloom, Kaggle |
Case Study: Lynda.com (a LinkedIn company) Leveraging an Erroneous Treatment. Did We Wake Sleeping Dogs, Reactivate Engagement or Do Nothing at All? Jim Porzak, DS4CI.org Ming Ng, LinkedIn | Case Study: Microsoft Predictive Analytics @work inside Microsoft: Revenue Modeling & Predictive Maintenance Ivan Judson, Microsoft |
| Crowdsourcing Predictive Analytics | Design of Experiment; Social Media Applications | Churn Modeling
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| 4:45-5:30pm | Case Study: City of Boston Predicting Restaurant Violations via Yelp Reviews: Crowdsourcing for Social Good Peter Bull, DrivenData |
Case Study: Facebook Advanced Experimentation in Social Networks Mario Vinasco, Facebook | Case Study: PayPal eCommerce Churn - from Definition to Prediction to Reactivation Julian Bharadwaj, PayPal |
| 5:30-7:00pm | Networking Reception Room: Salon 8 & 9 |
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| 7:15pm | Dinner with Strangers Sign up in advance at the eZone, Booth 418 |
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| 7:00-10:00pm | Bay Area useR Group Meeting Room: Salon 5 & 6 |
Bay Area SAS Users Group Meeting Room: Golden Gate A |
| Day 2: Tuesday, April 5, 2016 (PAW Workforce runs in parallel on this day - dual registration required) |
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| 8:00-8:45am | Registration Room: North Registration |
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| 8:00-8:45am | Networking Breakfast Room: Salon 8 & 9 |
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| 8:45-9:30am |
KEYNOTE • Room: Golden Gate A Case Study: Stitch Fix Keys to Growing a World Class Data Science Team – Some Observations from Stitch Fix Kim Larsen, Stitch Fix |
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| 9:30-9:40am | Plenary Session • Room: Golden Gate A Industry Trends: Highlights from the 2015 Data Miner Survey Karl Rexer, Rexer Analytics |
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| 9:40-10:00am | Diamond Sponsor Presentation • Room: Golden Gate A DataRobot: Better Prediction. Faster Gourab De, DataRobot 
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| Track 1: All Levels Room: Salon 3 & 4 |
Track 2: Expert/Practitioners Room: Golden Gate A |
Track 3: Financial Services Room: Salon 5 & 6 |
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| Hadoop & Other Open Source Tools | Retail Predictive Analytics | Tracking Satisfaction Via Social Media |
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| 10:05-10:25am | Open Source Lambda Architecture with Druid, Kafka, Samza, and Hadoop Gian Merlino, Imply |
Case Study: SmarterHQ The Revolution in Retail Customer Intelligence Dean Abbott, SmarterHQ | Case Studies: Capital One, Chase and Experian How Well Do You Really Know Your Customer? Steven Ramirez, Beyond the Arc |
| 10:30-10:50am | Hadoop for Predictive Analytics; Intrusion Detection | ||
| Hadoop for Predictive Analytics - A Data Scientist's Secret Weapon Against Malware Threats Anwar Adil, MapR |
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| 10:50-11:20am | Exhibits & Morning Coffee Break Room: Salon 8 & 9 |
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| 11:20am-11:40am | Self-Serve Prediction; Network Security | Advanced Methods | Best Practices |
| Case Study: Incapsula Predicting the Extent and Cost of Online Attacks to Help Sell Security Software Lawrence Cowan, Cicero Group |
Case Study: Workday
Time-Series Feature Engineering Done Right Vladimir Giverts, Workday |
Q&A: Ask Karl and Steven Anything (about Best Practices - for Financial Services and Beyond) Steven Ramirez, Beyond the Arc Karl Rexer, Rexer Analytics |
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| 11:45-12:05pm | Employee Theft Detection | ||
| Case Study: Major Fashion and Apparel Retailer Caught in The Act: Loss Prevention Rules Firing & Alerts Joseph Brandenburg, Analytics4Retail |
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| 12:05-1:15pm | Lunch in Exhibit Hall Room: Salon 8 & 9 |
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| 1:15-2:00pm |
Special Plenary Session • Room: Golden Gate A Doing Space-Age Analytics with Our Hunter-Gatherer Brains Dr. John Elder, Elder Research |
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| 2:00-2:15pm | Lightning Round • Room: Golden Gate A  |
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| 2:15-3:00pm |
Expert Panel • Room: Golden Gate A Data Prep: Overcoming the Bottleneck and Nailing It Moderator: Eric Siegel, Predictive Analytics World Panelists: Dean Abbott, SmarterHQ Satadru Sengupta, DataRobot Avishkar Misra, Oracle Yohai Sabag, Optimove |
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| 3:00-3:30pm |
Exhibits & Afternoon Break Room: Salon 8 & 9 |
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| Track 1: All Levels Room: Salon 3 & 4 |
Track 2: Expert/Practitioners Room: Golden Gate A |
Track 3: Financial Services Room: Salon 5 & 6 |
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| 3:30pm-4:15pm | Social Data for Predictive | Open Source Tools | Insurance
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| Case Study: Cars.com Predicting Consumer Review Engagement and Sentiment Using only Readily Available Social and Demographic Data Michael Spadafore, Marketing Associates |
Case Study: Facebook Predictive Analytics on the Command Line Clinton Brownley, Facebook |
Case Study: The Co-operators Developing an Analytics Practice and a Science Culture in Insurance Clement Brunet, The Co-operators |
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| B2B Sales; Energy | Healthcare Analytics | Optimizing Discount Pricing
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| 4:15-5:00pm | Case Study: Omaha Public Power District Predictive Sales Targeting in the Energy Industry Nate Watson, Contemporary Analysis |
Case Study: Sutter Health Overcoming Big Data Bottlenecks in Healthcare: A Predictive Modeling Case Study Paddy Padmanabhan, Damo Consulting & Joshua Liberman, Sutter Health |
Case Study: Paychex Maximize Value and Retention With Predictive Analytics In Discounting
Jing Zhu, Paychex Inc. |
| Post-Conference Workshops: Wednesday, April 6, 2016 | |
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The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes Dr. John Elder, Elder Research, Inc. |
Supercharging Prediction: Hands-On with Ensemble Models  Dean Abbott, Abbott Analytics |
| Post-Conference Workshops: Thursday, April 7, 2016 | |
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Advanced Methods Hands-on: Predictive Modeling Techniques Dean Abbott, Abbott Analytics Sponsored by:
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Uplift Models: Optimizing the Impact of Your Marketing Kim Larsen, Stitch Fix |
