Agenda

Predictive Analytics World for Government Washington, DC 2018

September 17-21, 2018

Workshops - Monday, September 17th, 2018

9:00 am
Workshop

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:
How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
What are the best practices along the way?
How do you make it sure it works on new data?
In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.
Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.
This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.
Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.
If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.
What you will learn:
The tremendous value of learning from data
How to create valuable predictive models with machine learning for your business
Best Practices, with real-world stories of what happens when things go wrong
Prerequisites:
The workshop is filled with real-world stories and explanations of methods that are visual and analogy-based, rather than mathematical. Each section is designed to make clear the gist of its concept to a complete novice, and to conclude with intriguing ideas for advanced researchers. Experience has shown that attendees who get the very most out of the course:
Have some experience with programming, or algorithmic approaches to problem-solving
Have taken an introductory course in probability or statistics … but most importantly
Have a problem to solve that inspires and anchors their learning as techniques are introduced

Session description
Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research

Predictive Analytics World for Government - Washington, D.C. - Day 1 - Tuesday, September 18th, 2018

7:45 am
Registration & Breakfast
8:45 am
Chair's Opening Remarks
9:00 am
Keynote 1
The Keynote Description will be available shortly.
Session description
9:30 am
Panel Discussion:
The Session Description will be available shortly.
Session description
Panelists
Peter Aiken
Founding Director
Data Blueprint
Ethan Chen
Director, Division of Data Management Services and Solutions
FDAFDA CDER OSP Office of Business Informatics
10:30 am
Coffee Break / Networking / Lead to Breakout Sessions
11:00 am
Management Level – Track #1

Avoidable hospital readmissions, where a patient was readmitted for reasons later discovered to be avoidable, are estimated to be a $17 billion annual problem for Medicare. Using automated machine learning and open-source software on a public dataset, we show how to quickly compare, tune, and train several models using cutting edge algorithms. Our predictive model can be used to predict patients who are likely to be readmitted, what factors were important, and hopefully help reduce the financial impact of this problem in the long term.

Session description
Speaker
Cal Zemelman
Director
CVP
Management Level – Track #2

Reducing crime while effectively managing their internal resources is the overriding goal of every municipal police force. Recognizing the value of data, the Toronto Police Force has embarked upon on a data-driven strategy to optimize the level of decision-making in achieving this goal. Tools and solutions utilizing both demographic data from Environics Analytics alongside the Toronto Police Service’s own data provided the capability of building supply and demand models for better allocation of their police resources. Given organizational strategy needs, we also developed a customized territory system that resulted in better assignment of divisions across Toronto.

Session description
Speakers
Richard Boire
Senior Vice President
Environics Analytics
Ian Williams
Manager of the Business Intelligence and Analytics Unit
Toronto Police Service
Technical Track #A

We present the results of leveraging Apache Spark cluster to process massive datasets using divide and aggregate model, just as we do in resampling methods and cross validation techniques. We present a reusable framework to inject custom models and run the model over numerous disjoint partitions. During the aggregation phase results are obtained using specified aggregation model -- simple averaging or votes mechanism--. We compare results with simple execution model over smaller datasets.

Session description
Speaker
Raman Kannan
Adjunct
NYU Tandon School of Engineering
11:50 am
Management Level – Track #3
The Session Description will be available shortly.
Session description
Speaker
Bryan Jones
Owner and Principal Consultant
Strategy First Analytics
Management Level – Track #4
The Session Description will be available shortly.
Session description
Speaker
Andrew Ferguson
Professor of Law
UDC David A. Clarke School of Law
Technical Track #B

Join us as we prepare an advance analytical workflow that predicts the probability of existing Park & Recreation customers to respond to a campaign they want to implement to increase revenues at the Parks. The session will include importing and preparing several data sources and blending it with other spatial and demographic data. We will also generate an interactive map to help visualize the solution.

Session description
Speakers
Marie Breton
Solutions Engineer
Alteryx
Corey Long
Manager
Deloitte Consulting
12:35 pm
Networking Lunch
Executive Lunch
1:30 pm
Keynote #2
The Keynote Description will be available shortly.
Session description
Speaker
Tom Davenport
Independent Senior Advisor
Deloitte Analytics, Deloitte Consulting LLP
2:00 pm
Panel Discussion:
The Session Description will be available shortly.
Session description
Moderator
Vishal Kapur
Principal
Deloitte Consulting, LLC
Panelist
Hudson Hollister
Executive Director
Data Transparency Coalition
3:00 pm
Coffee Break / Networking / Lead to Breakout Sessions
3:30 pm
Management Level – Track #5

Graph methods provide a powerful way to represent a network of objects and the relationship or connections between them.  Examples include power grids connecting cities, social networks that connect users, and traffic systems connecting roads.  The study of networks has also emerged as a means of analyzing complex financial transactions, including those in tax administration.  In addition to graph databases for interactive visualization, a diverse set of approaches exists for graph mining, including subgraph discovery and anomaly detection.  The continued maturity of tools like R, Python, and Spark, along with distributed computing frameworks, has provided new opportunities to apply graph methods for a wide range of business problems—including those involving massively large data sets.  This case study provides a glimpse of how a variety of graph-based approaches are being used at the IRS and discusses lessons learned, business impact, and future direction.

Session description
Speaker
Jeff Butler
Associate Director of Data Management
IRS Research, Analysis, and Statistics organization
Management Level – Track #6

The difficulty of implementing a new data initiatives often goes under-appreciated, particularly the challenges facing most organizations. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data professional, but beyond that there are common cultural and structural barriers that must be eliminated in order to leverage data effectively. This talk will discuss these barriers—the titular “Seven Deadly Data Sins”—and in the process will also:

- Elaborate upon the three critical factors that lead to data initiative failure
- Demonstrate a two-phase data strategy implementation process
- Explore the sources and rationales behind the “Seven Deadly Data Sins,”

and recommend solutions and alternative approaches

In this manner data investments can be more accurately focused on your organization's strategic priorities. Only when past the prerequisites, can organizations develop a disciplined, repeatable means of improving the data literacy, processes, and supply - the three key elements required to improve data support for strategy. Think of it as a repeatable process for identifying and removing data constraints.

Session description
Speaker
Peter Aiken
Founding Director
Data Blueprint
Mini-Course Technical Track #C

By slowing down and circling around in order to decipher parking signs and avoid tickets, drivers in major cities such as San Francisco cause a lot of traffic jams. These behaviors can potentially endanger the safety of pedestrians and represent a threat on the transportation environment. 

We exploit deep learning techniques to predict parking signs from street-level imagery and find their actual location on a map. Multiple APIs are applied to read and extract the rules from the signs. The resulted map of digitized parking rules along with driver\'s GPS information are used to build products that facilitate parking.

Session description
4:20 pm
Management Level – Track #7
The Session Description will be available shortly.
Session description
Speaker
LeAnna KentElder Research
Data Scientist
Elder Research Inc.
Management Level – Track #8
The Session Description will be available shortly.
Session description
Speaker
Timothy J. Kropp
Senior Advisor to the Chief Data Officer
US Department of Health and Human Services, Office of the Inspector General
Mini-Course Technical Track #D
The Session Description will be available shortly.
Session description
5:00 pm
Networking Reception
6:00 pm
End of first Conference Day

Predictive Analytics World for Government - Washington, D.C. - Day 2 - Wednesday, September 19th, 2018

7:45 am
Registration & Breakfast
8:45 am
Chair's Opening Remarks
9:00 am
Keynote #3
The Keynote Description will be available shortly.
Session description
Speaker
David WilliamsUSPS
Board of Governors (Nominated)
USPS
9:30 am
Plenary Session:
The Session Description will be available shortly.
Session description
Sponsored by
DataRobot
10:00 am
Keynote #4

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.


But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
10:30 am
Coffee Break / Networking
11:00 am
Management Level – Track #9

Customer Relationship Management (CRM) is often limited in the public sector – usually comprised of post-hoc surveys and/or personal interactions. The Alcohol and Tobacco Tax and Trade Bureau (TTB) expands CRM to include interactions with its web products, and uses web analytics to more deeply understand its customers. Using web data and open-source tools, TTB can tell stories about its web products, like which pages:•Were the most viewed;•Had anomalous spikes in viewers;•Had a trend movement in viewers.This allows TTB to strengthen its CRM by tuning its current web products and having more targeted customer outreach.

Session description
Speaker
Daniel Turse
Alcohol and Tobacco Tax and Trade Bureau
Management Level – Track #10
Part 1
The Session Description will be available shortly.
Session description
Part 2
The Session Description will be available shortly.
Session description
Mini-Course Technical Track #A

At the NFA, a regulatory body for the US derivatives markets, we have thousands of firms to monitor, but limited resources to do so. Over the past 4 years we have built a predictive modeling program to help solve this problem by identifying which firms pose the most risk. Over this time we have learned many important lessons, and the most important ones have little to do with picking which algorithm to use. This session will discuss practical tips that data scientists can apply to their own modeling projects covering model construction, model evaluation, data integrity, and others topics.

Session description
Speaker
Jonathan DeRuiter
Data Science Manager
NFA
11:50 am
Management Level – Track #11
The Session Description will be available shortly.
Session description
Management Track #12
Part 1
The Session Description will be available shortly.
Session description
Part 2
The Session Description will be available shortly.
Session description
Mini-Course Technical Track #B
The Session Description will be available shortly.
Session description
12:35 pm
Networking Lunch
1:30 pm
Panel Discussion:
The Session Description will be available shortly.
Session description
Moderator
Juergen A. Klenk PhD
Principal
Deloitte Consulting
2:00 pm
Plenary Session:
The Session Description will be available shortly.
Session description
Sponsored by
IBM
3:00 pm
Chair’s Closing Remarks:
The Session Description will be available shortly.
Session description
3:15 pm
End of second Conference Day

Workshops - Thursday, September 20th, 2018

9:00 am
Workshop

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.
This workshop covers:
Antidotes: the best practices that overcome the most common flawed practices
Intuitive explanations of resampling methods that ensure your models work on new data
Practical tips for both the hard and the soft skills that will see your project through to implementation
In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will survey the most advanced analytics tools in the practitioner’s toolkit, with particular emphasis on resampling tools – such as cross-validation and target shuffling (a method to avert p-hacking devised by Dr. Elder) – which reveal the true accuracy of your models.
Workshop topics also include visualization, feature engineering, global optimization, criteria of merit design, ensembles, and “soft” factors that affect success, such as human cognitive biases. Attendees will also leave with an understanding of the inner workings of the most popular algorithms – including regression, decision trees, nearest neighbors, neural networks, bagging, boosting, and random forests.
Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, illuminating the technical material covered.
If you’d like to become a more expert practitioner of predictive analytics, this workshop is for you.
What you will learn:
The 12 subtle pitfalls to watch out for on any new project
The latest ways to increase the value of predictive models and machine learning for your business
How to succeed when your biggest threat is not technology, but people (e.g., resistance to change)
Prerequisites:
The workshop is filled with real-world stories and explanations of methods that are visual and analogy-based, rather than mathematical. Each section is designed to make clear the gist of its concept to a complete novice, and to conclude with intriguing ideas for advanced researchers. Experience has shown that attendees who get the very most out of the course:
Have some experience with programming, or algorithmic approaches to problem-solving
Have taken an introductory course in probability or statistics … but most importantly
Have a problem to solve that inspires and anchors their learning as techniques are introduced

Session description
Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research

Workshops - Friday, September 21st, 2018

9:00 am
Workshop

This 1-day workshop is an introduction to data science for executives and managers of data science programs. It provides a high-level overview of modern data science concepts, tools, and techniques from a management perspective. All stages of the data science lifecycle will be discussed in the context of agile management methodologies using real-world case studies. Managers will learn how to identify skilled data scientists and build data science teams that use sound scientific methods to meet their organization’s objectives. Leaders will learn to ask the right questions, solve the right problems, keep data science projects on track, ensure their solutions are deployed and avoid common pitfalls along the way. Both technical and non-technical participants will benefit from this workshop and be equipped with the knowledge necessary to lead effective data science programs in their organizations.

Session description
Leader
Carl Hoover PhDElder Research
Chief Technology Officer
Elder Research
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