Agenda
September 25, 2019 – Washington, D.C.

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Wednesday, September 25, 2019

Wednesday

Wed

7:30 am

Wednesday, September 25, 2019 7:30 am

Registration & Breakfast

Wednesday

Wed

8:30 am

Wednesday, September 25, 2019 8:30 am

Chair’s Opening Remarks

Wednesday

Wed

9:15 am

Wednesday

Wed

10:45 am

Wednesday, September 25, 2019 10:45 am

Coffee Break / Networking / Lead to ...

Wednesday

Wed

11:45 am

Wednesday, September 25, 2019 11:45 am

5 Minute Transition

Wednesday, September 25, 2019 11:50 am

UX4AI and AI4UX

Speaker: Ketki Dhanesha,

Wednesday, September 25, 2019 11:50 am

Sponsored Lightning Round

Wednesday

Wed

12:20 pm

Wednesday, September 25, 2019 12:20 pm

Networking Lunch

Wednesday

Wed

2:20 pm

Wednesday, September 25, 2019 2:20 pm

Diamond Plenary Session

Wednesday

Wed

2:50 pm

Wednesday, September 25, 2019 2:50 pm

Coffee Break / Networking / Lead to ...

Wednesday

Wed

3:20 pm

Wednesday, September 25, 2019 3:20 pm

Coming Soon!

Wednesday

Wed

3:50 pm

Wednesday, September 25, 2019 3:50 pm

5 Minute Transition

Wednesday

Wed

3:55 pm

Wednesday, September 25, 2019 3:55 pm

Coming Soon!

Wednesday

Wed

4:25 pm

Wednesday, September 25, 2019 4:25 pm

5 Minute Transition

Wednesday, September 25, 2019 4:30 pm

Coming Soon!

Wednesday

Wed

5:00 pm

Wednesday, September 25, 2019 5:00 pm

Networking Reception

Wednesday

Wed

6:00 pm

Wednesday, September 25, 2019 6:00 pm

End of DDG 2019

Workshops

Monday, September 23, 2019
** NEW! ** Fraud Analytics & Anomaly Detection
Daniel Brannock
Hosts:
Daniel Brannock
Date:
Monday, September 23, 2019
Time:
8:30 am - 4:30 pm
Price:
Government Employee: $700
Private Industry / Contractors: $1,400 - prices increase after August 9!
** NEW! ** Fraud Analytics & Anomaly Detection

Intended Audience: Fraud analysts and managers interested in learning the latest array of data science and machine learning techniques useful for reducing and eliminating fraud, waste, and abuse.

 Knowledge Level: Attendees should have some basic experience with data-driven modeling.

Workshop Description

Dramatically reducing fraud and other abuse is a challenging task requiring a comprehensive and flexible analytics solution. Wherever your organization is along the wide spectrum of fraud analytics capabilities — from basic rules and reporting to complex network graph analysis – this workshop will show you how to build out all the components of a holistic and robust fraud analytics platform. We will review critical technical details as well as problems to watch out for based on lessons learned over years of consulting on real-world fraud cases.

This one-day session teaches best practices for fraud analytics constrained by the realities of incomplete data and unrecognized or emerging fraud patterns. Existing fraud models have proven value, but nagging concerns remain about “unknown unknowns”. A new view of the problem is required.

  • You should attend this workshop to learn:
  • What anti-fraud techniques work best
  • Graph (network) features that highlight fraud
  • How to extract information hidden in unlabeled data
  • A new way to powerfully combine supervised and unsupervised models

Attendees will learn how to:

  • Use new unsupervised learning techniques to identify previously unseen cases of fraud
  • Recognize and remove common biases
    (such as stemming from rare known fraud cases and from selection effects)
  • Prioritize anomaly types by their fraud likelihood
  • Incorporate multiple techniques and processes into an effective anti-fraud platform
Thursday, September 26, 2019
Data Science for Managers
Carl Hoover Ph.D.
Hosts:
Carl Hoover Ph.D.
Date:
Thursday, September 26, 2019
Time:
9:00 am - 4:30 pm
Price:
Government Employee: $700
Private Industry / Contractors: $1,400 - prices increase after August 9!
Data Science for Managers

Intended Audience: Business managers, executives and leaders interested in data science.

Program Level: Beginner to Intermediate

Recommend Field of Study:

  • Computer Science
  • Analytics
  • Statistics
  • Mathematics
  • Finance and Marketing

Instructional Method: Group – Live
CPE Credit’s received for completion:

  • 1.5 CPE Credits for Management Adv Service
  • 1.5 Statistics
  • 1.5 Specialized Knowledge and Apps
  • 1.5 Business mgmt. and org

 

Workshop Description

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.

What you will learn:

  • Basic concepts of data science and advanced analytics
  • Agile planning within a typical data science project lifecycle
  • Project scoping by defining appropriate data science tasks and objectives
  • Best and worst data science practices

Prerequisites:
There are no prerequisites for this workshop; however, it is best suited for participates who:

  • Have executive or mid-level management experience
  • Are currently managing or planning to manage data science projects

Learning objectives:
Participants will learn how to:

  • Understand fundamental concepts of data science
  • Plan and manage the data science project lifecycle using agile techniques
  • Define the appropriate data science problem to solve
  • Build effective data science teams within an organization
  • Avoid common pitfalls in data science management
Tuesday, September 24, 2019
Best of Predictive Analytics: Core Machine Learning and Data Science Techniques
John Elder Ph.D.
Hosts:
John Elder Ph.D.
Date:
Tuesday, September 24, 2019
Time:
8:30 am - 4:30 pm
Price:
Government Employee: $700
Private Industry / Contractors: $1,400 - prices increase after August 9!!
Best of Predictive Analytics: Core Machine Learning and Data Science Techniques

Intended Audience: Interested in the fundamentals of modern machine learning techniques.

Program Level: Intermediate
For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.

Recommend Field of Study:

  • Computer Science
  • Analytics
  • Statistics
  • Mathematics
  • Finance and Marketing

Instructional Method: Group – Live
CPE Credit’s received for completion:

  • 1.5 CPE Credits for Management Adv Service
  • 1.5 Statistics
  • 1.5 Specialized Knowledge and Apps
  • 1.5 Business mgmt. and org

 

Workshop Description

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