World of Watson 2016 - Implementing data science

Speakers Industry
ALY–1023: Creating a Data Science Program from the
Ground Up with IBM Data and Analytics
Sessions Advanced analytics
Breakout Session
, 09:00 AM – 09:45 AM
It’s possible to make sense of information chaos. Blue Cross Blue Shield of Western
New York (BCBS of WNY), a multi-billion dollar healthcare organization, used IBM
technology to create end-to-end self service capabilities that prove the value of
information in their enterprise. With IBM InfoSphere Business Glossary, InfoSphere
DataStage, a PureData Appliance, Cognos Analytics and SPSS solutions, BCBS of WNY
evolved lightly governed data assets into an exploration sector so data scientists and
top-tier analysts could take a Darwinian approach to their research. They can now move
from idea to business outcome in a fraction of the time of traditional warehousing, while
making compliance happy with appropriate controls and rigor.
ALY–1610: IBM Data Science Experience: An
24 October 2016

Christopher Keaton, BlueCross BlueShield
of WNY

David Russell, LPA Software Solutions
Healthcare & Life Sciences
https://www-01.ibm.com/events/global/wow/sessions/print/index.html
1 of 20 9/26/2016 1:41 PM

Speaker Industry
Easy-to-Use, Universal Platform for Data Scientists to
Collaborate
Theater Session
, 06:00 PM – 06:20 PM
Data Science Experience (DSX) is a cloud-based development environment for
real-time, high-performance analytics that gives data scientists the ability to access and
ingest massive amounts of data and deliver new business insights. It comes with the
popular Jupyter Notebook, R Studio, Apache Spark and much more. This session will
demonstrate DSX’ capabilities in creating and deploying an end-to-end use case. We
illustrate how to build and deploy a model to predict locations of future accidents in New
York City, using weather forecast and historic accident data. We achieved an accuracy of
68% for the test data and 63% for validation data in predicting accidents at zip code
level (FP and FN rates: 0.37, 0.32).
ALY–1636: A Cognitive Approach to Recognize Entities in
Data Sets Using The Data Science Experience
Theater Session
, 08:30 AM – 08:50 AM
I will present a novel set of APIs, called Cognitive Detection (CD), for two data
exploration tasks. The CD APIs enable data scientists to train models that recognize
entities on structured data sets. For example, trained models can distinguish between a
telephone number (e.g., 802-552-9055) and a social security number (e.g., 542-61-7824)
without the need of generating speciﬁc rules for each entity. In addition, CD adapts to
each user’s data and models are updated continuously with new data. The CD APIs also
24 October 2016

Shahab SHEIKH-BAHAEI, IBM Analyst/Press
26 October 2016

2 of 20 9/26/2016 1:41 PM

Speaker Industry
enable data scientists identify outliers from a set of string values. I will demo the CD APIs
in The Data Science Experience, a cloud-based tool for data scientists.
Speaker Industry
ALY–1638: Building a word2vec Model with Twitter Data
Using the IBM Data Science Experience
Hands-on Lab
, 04:00 PM – 06:30 PM
It is always amazing when someone is able to take a very hard, present-day problem
and translate it to one that has been studied for centuries. This is the case with
word2vec, which transforms words into vectors. Text is unstructured data and has been
explored mathematically far less than vectors. Newton (1642-1726) may have been the
ﬁrst one to study vectors, while text mining studies began just a few decades ago. This
session will show how to build word2vec models with Apache Spark using the IBM Data
Science Experience—a cloud-based tool for data professionals to learn, collaborate
and create. In addition, the discussion will include examples of how these models are
used in data products.
ALY–1689: Making Your Data Science Project Successful:
Myths to Disregard and Tips for Success
JORGE CASTANON, IBM Not Industry Speciﬁc
25 October 2016

JORGE CASTANON, IBM Consumer Products
3 of 20 9/26/2016 1:41 PM

Speaker Industry
Theater Session
, 04:30 PM – 04:50 PM
Data science is at the forefront of many organizational leaders’ thoughts. But how does
one make sure that a project can be successful? While a number of websites offer
guidance on the qualifications for a data scientist, much of the advice is not written by
people with significant production implementation experience. As a result, a number of
myths have developed on what is needed for successful data science projects. The
product manager for IBM SPSS Modeler, an expert in building and deploying predictive
models, will provide practical advice on what is required and what is not required to
ensure your organization can be successful with your data science project.
25 October 2016

Ted Fischer, IBM Not Industry Specific
4 of 20 9/26/2016 1:41 PM

Speaker Industry
ALY–1833: Does Open Source Scale? Cognitive Lessons
Learned at The Nielson Company
Theater Session
, 02:00 PM – 02:20 PM
An open source approach to data science and analytics is no longer an option, but a
necessity. The development of open source tools such as R and Spark and the
availability of APIs are enabling companies to walk away from traditional models and
embrace cognitive computing. While the benefits of open source has been touted for
years, it can be a challenge for corporations; and success requires finding a balance
between maintaining current operations and enabling future cognitive initiatives. In this
talk, we will discuss how the data science team at Nielsen is tackling this challenging
problem, and share how to avoid pitfalls that stand in the way of true cognitive business.
ALY–1881: Verizon Wireless on What’s Hot and What’s
Cool in Stream Computing: Emerging Trends You Need
to Know
Breakout Session
, 08:00 AM – 08:45 AM
Verizon Wireless has undergone a transformation to create a real-time framework that
includes consolidated data sources, a decision engine, high-performance data streams,
established data science practice, analytical models, machine learning and cognitive
capabilities. This enables them to know the customers’ end-to-end journey in real-time
to connect the context with customer data. Verizon has become proactive rather than
24 October 2016
Jennifer Shin, Nielsen Not Industry Specific
26 October 2016

5 of 20 9/26/2016 1:41 PM

Speakers Industry
reactive in engaging and personalizing customer experience, while also creating value
for their shareholders.
Speakers Industry
ALY–2168: Brunel: An Open Source Data Visualization
Language for Data Scientists
Theater Session
, 03:00 PM – 03:20 PM
There is an ever-expanding amount of data and there aren’t enough people with the
skills to tap its potential. Visualization is a critical tool for the analytic journey, but there’s
a disconnect: the tools that offer data scientists the greatest ﬂexibility require
programming and are tightly integrated into complex languages. That’s why we created
the Brunel visualization language. This simple language can be used within data science
solutions (Notebooks, Python, R, Scala, Spark) as well as with online apps or in your own
web application. This talk will describe Brunel, and also show you how you can add
compelling live visualizations to your web application or blog in minutes! Visit
https://brunelvis.org to learn more.
ALY–2689: USAA Scales Data Science and Predictive
Roger C. Rea, IBM
Bashyam Damodaram, Verizon Wireless
Telecommunications
25 October 2016

Graham Wills, IBM
Daniel Rope, IBM
Not Industry Speciﬁc
6 of 20 9/26/2016 1:41 PM

Speakers Industry
Analytics for Enterprise Decisioning
Breakout Session
, 10:00 AM – 10:45 AM
Using best practices, cutting-edge technology and innovative solutions, USAA has
scaled advanced analytics so it is now embedded enterprise-wide. Predictive modeling
output has increased 400% each year, and is now a part of real-time operational
decision ﬂows for mobile personalization, marketing and sales, insurance underwriting
processes, risk evaluation, call center optimization and fraud detection. In this session,
you’ll gain an understanding of how to scale data science operations in a skilled
resource constrained environment.
24 October 2016

Alex Fly, embel assist
michael willette, USAA
Banking
7 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–2926: IBM Data Science Experience Hands-On
Introduction
Hands-on Lab
, 09:00 AM – 11:30 AM
Get familiar with IBM Data Science Experience by learning about the product features
and functionality in a hands-on lab.
Speaker Industry
ALY–2941: IBM Data Science Experience Advanced
Workﬂow from Notebooks to RStudio
Hands-on Lab
, 04:00 PM – 06:30 PM
Learn advanced techniques with IBM Data Science Experience by completing a
hands-on lab guiding you through a workﬂow starting with Jupyter Notebooks and
working to RStudio. See how easy it is to use multiple open source tools together in Data
Science Experience—all powered by Spark.
26 October 2016
Thomas Schaeck, IBM
Guru Banavar, IBM
Education
24 October 2016

Brandon MacKenzie, IBM Education
8 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–2954: How We Use Data Science Experience to
Analyze Data Science Experience
Breakout Session
, 01:00 PM – 01:45 PM
Learn how the IBM Data Science Experience team uncovers insights with Jupyter
Notebooks and RStudio to analyze user actions. In this session, we look at how you can
use the tools available in Data Science Experience to make the product better through
data-driven decisions.
25 October 2016

JORGE CASTANON, IBM
Greg Filla, IBM
Consumer Products
9 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–3029: From Lab to Production: Scale Up Your Data
Science with IBM Data Science Experience
Hands-on Lab
, 04:00 PM – 06:30 PM
Learn how IBM Data Science Experience can be used in early the stages of a data
science project to create models that can then be scaled. By making it easy to change
Spark instances with Jupyter notebooks or RStudio, Data Science Experience makes it
easy to scale up when you are ready.
25 October 2016
Brandon MacKenzie, IBM
Greg Filla, IBM
Education
10 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–3049: How IBM Data Science Experience Fits Into
Your Analytics Team
Breakout Session
, 10:00 AM – 10:45 AM
From start to finish, this session breaks down the analytics workflow and where IBM
Data Science Experience fits. Questions that every analytics team has will be answered:
Where is the data coming from? What connections do we have for our team? How do we
collaborate and add members to a project? How do we deploy our model?
24 October 2016
Deepak RANGARAO, IBM
carlo appugliese, IBM
Information Technology
11 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–3079: BlocPower Uses IBM Data Science
Experience to Model Energy Consumption
Breakout Session
, 09:00 AM – 09:45 AM
In this session, we examine IBM's collaborative work with BlocPower to see how the
latest tools are being used to model energy consumption in buildings all over New York
City. IBM Data Science Experience was used in a project that worked through the data
mining workﬂow to help BlocPower make data-driven business decisions.
27 October 2016
Guru Banavar, IBM
Tooraj Arvajeh, Blocpower
Energy & Utilities
12 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–3085: Digital Partnerships: Key to Accelerating in the
Age of Cognitive Computing
Breakout Session
, 12:00 PM – 12:45 PM
The rise of high-performance compute analytics enables a sector like agriculture to
double down on global productivity due to its dependency on accounting for multiple
sources of variation: environmental, genetics, genomics, weather, location, geospatial
components, etc. Being able to accelerate the dynamics of all these components
together requires key partnerships in data sciences verticals from deep learning,
machine learning, base statistics, genetics and data sciences. This talk will cover some
of the key partnerships that Monsanto has embarked upon in high-performance
compute analytics to accelerate and increase agricultural productivity.
ALY–3281: Hands-On Introduction to Apache Spark
Hands-on Lab
, 09:00 AM – 11:30 AM
This hands-on session will provide participants with a basic knowledge of Apache
Spark. The session has two hands-on labs to teach participants how to start coding
using Apache Spark in Juypter Notebook on the IBM Data Science Experience/IBM
Bluemix cloud platform. Lab One, “Hello Spark,” shows you the basics to create RDDs,
pull in data ﬁles, and run map, ﬁlter and other basic transformation command. Lab Two,
24 October 2016
Yana Ageeva, IBM
Anju Gupta, Monsanto
26 October 2016

13 of 20 9/26/2016 1:41 PM

Speakers Industry
“Spark SQL,” shows you the basics to create DataFrames, run Sspark SQL, create
tables, join tables/DataFrames, and visualize Spark SQL results with matplotlib.
Speakers Industry
ALY–3285: Hands-On Introduction to Apache Spark
Machine Learning
Hands-on Lab
, 12:30 PM – 03:00 PM
This hands-on session will give participants a basic knowledge of Apache Spark
machine learning. The hands-on labs teach participants how to start coding using
Apache Spark in Juypter Notebook on the IBM Data Science Experience/IBM Bluemix
cloud platform. The lab shows the basics to format data and train a machine-learning
algorithm with Apache Spark. The lab will use the Alternating Least Square algorithm to
build a recommender system based on online commerce data.
ALY–3297: Why the Data Science Professional Beneﬁts
from Using Apache Spark
Deepak RANGARAO, IBM
24 October 2016

Mokhtar Kandil, IBM
14 of 20 9/26/2016 1:41 PM

Speakers Industry
Breakout Session
, 04:00 PM – 04:45 PM
Apache Spark was specifically designed with the goal of having a unified stack of data
processing and analytics capabilities all in one environment. The goal? To make it easier
and faster for data professionals to perform data science, advanced analytics, and
development on big data. Apache Spark instantly makes the work of data science
professionals easier by unifying data access across the organization. Because data
access is unified, one or two lines of code can be used to pull data from multiple data
sources. Attend this session to learn about how to use Apache Spark as a lever to solve
bigger problems, accelerate delivery of business applications, and develop a blueprint
for innovation.
25 October 2016

Alan Goodrich, IBM
15 of 20 9/26/2016 1:41 PM

Speakers Industry
ALY–3310: Data Science Made Easy With IBM: A User
Panel Discussion
Panel Discussion
, 11:00 AM – 11:45 AM
It is no secret IBM is on a mission to train a million data scientists and build the next
generation of technical leaders. So, where are we today? What do our users have to
say? Come hear for yourself how corporate and academic leaders have used IBM
technology to propel their data science missions to the next level. We’ll begin with a
short overview and demo of the latest IBM technology in the data science ecosystem,
and then continue the discussion with a distinguished panel.
ALY–3416: Assemble a Data Sciences Team That Will
Deliver Spellbinding Results
Breakout Session
, 09:00 AM – 09:45 AM
Data scientists are increasingly invaluable to senior executives as they seek to increase
operational efﬁciency, improve impact analyses, reduce cost and improve forecasting.
Successful data scientists work with business leaders to understand challenges, deﬁne
analytic solutions and analyze data to uncover innovations and insights. This critically
important role requires a rare mix of skills. Along with sharing new research from the IBM
Institute for Business Value, top IBM data scientists offer advice on how to build a strong
27 October 2016
Ron Reuben, IBM
Dinesh Nirmal, IBM
26 October 2016

16 of 20 9/26/2016 1:41 PM

Speakers Industry
team with relevant experience, a solid academic foundation, varied backgrounds and
skills, and an ability to envision and work toward outcomes. Q&A discussion will include
“a day-in-the-life” of a data scientist.
Speakers Industry
ALY–3558: The Clash of the Titans: This Generation
Versus the New Generation of Data Scientists
Breakout Session
, 01:00 PM – 01:45 PM
What happens to today’s data scientists when the up-and-comers, who are currently
learning new techniques like machine learning and cognitive computing, rush the data
science gap gates and start showing off their new tricks? You only have to look at the
growing numbers of governments and businesses funding universities to teach machine
learning, or the accessibility of online courses and competitions, to see how this is going
to happen. Attend this session to learn about IBM's Analytics Platform Services (APS)
portfolio and how it might be the answer to the diversity of data science skills in
organizations.
ALY–3809: Deep Learning-Based, Real-Time Anomaly
Michael Haydock, IBM
Mark Grabau, IBM
24 October 2016

Uday Tekumalla, IBM
Luis ARELLANO, IBM
17 of 20 9/26/2016 1:41 PM

Speaker Industry
Detection on Bearing Vibrations with Apache Spark
Ask Me Anything Demo Stations
, 09:30 AM – 11:00 AM
Deep Neural Networks (DNN) are currently taking off and disrupting the data science
ecosystem. Every month a new state-of-the-art algorithm is beaten by a DNN. DNNs are
heavily used in IBM Watson APIs, and this talk will demo live a system capable of
detecting abnormal vibration patterns on bearings generated by a physical model data
simulator (Lorenz Attractor Model) capable of creating chaotic data, which is very hard
to classify. It will ingest data using the Watson IoT Platform and show how IBM Data
Science Experience can be used to detect anomalies on time-series sensor data with a
15-fold conﬁdence using a so-called DNN Auto-encoder. This component is currently
planned to become part of the IBM Watson IoT platform.
26 October 2016

Romeo Kienzler, IBM Manufacturing
18 of 20 9/26/2016 1:41 PM

Speaker Industry
ALY–3995: Up Close with IBM Data Science Experience
Theater Session
, 04:00 PM – 04:20 PM
IBM Data Science Experience is a new way of working for data scientists, whether they
are just starting out or are well on their way to mastering a discipline that is consistently
cited as the key to unlocking success in the cognitive era. Notebooks, RStudio, Spark
and more are available—but how can one turn these capabilities into something that will
drive a business forward? In this session, we will demonstrate what the art of data
science is really abou, and how IBM Data Science Experience is evolving to make
everyone a data scientist and make every data scientist an artist.
ALY–3997: Expert Exchange: Data Science Best
Practices (CRISP-DM)
Theater Session
, 03:30 PM – 03:50 PM
The success of data science, data mining, machine learning and other forms of
advanced analytics depends on a rigorous application of best practices. These best
practices encompass strategy and culture, and also how an individual or team
approaches a data analysis project. CRISP-DM is a vendor-neutral model that has been
used successfully and continues to be used to make sure analysis projects can
demonstrate success. This is the session that will help you explain your work to
executive stakeholders, show success of your analysis projects to management, and
demonstrate the success of analytics to your organization. It will be an open exchange
24 October 2016
Shashank Byramangala Krishnam, IBM Not Industry Speciﬁc
24 October 2016

19 of 20 9/26/2016 1:41 PM

Speaker Industry
with experts that have applied predictive analytics across industries.
Grant Case, IBM Not Industry Speciﬁc
20 of 20 9/26/2016 1:41 PM

World of Watson 2016 - Implementing data science

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World of Watson 2016 - Implementing data science