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by
Tony Rathburn
Two Days:
$1,295
Levels I and II Package: $1,995
SCHEDULE AND SITE DETAILS
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ABOUT THIS COURSE
Data mining is essentially
a discovery process -- a process riddled with common yet elusive
strategic pitfalls. Project failure
is rarely due to poor model development. Rather, data mining projects
often fall short of their potential due to flawed or overlooked
assessment, business understanding, project definition and strategic
planning specifically for information discovery.
If you are
looking
for an intensive vendor-neutral and non-promotional introduction to data mining
best practices and an approach to predictive analytics which is critical to modeling success, then this course is designed for you.
Data Mining: Level I offers a concentrated presentation of data
mining terminology, capabilities, limitations, risks, rewards,
case studies, best practices, standard process and strategy.
Those in attendance will be exposed to popular methods of
predictive modeling, application examples, live illustrations
and resources to get started.
Practitioners seeking to drill down into the
tactical implementation
of predictive analytics may also attend the Data
Mining: Level II offering: an additional two days immediately
following this course at the same site. |
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WHO SHOULD ATTEND
IT/IS EXECUTIVES AND MANAGERS: CIOs, CKOs, CTOs,
Stakeholders, Functional Officers, Technical Directors and Project Managers
LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL
MANAGERS: Risk Managers, Customer Relationship Managers,
Business Forecasters, Inventory Flow Analysts, Financial Forecasters,
Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce
Company Executives
TECHNOLOGY PLANNERS: Who survey
emerging technologies in order to prioritize corporate investment
CONSULTANTS: Whose competitive
environment is intensifying and whose success requires competency
with data mining and related emerging information technologies
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BENEFITS OF ATTENDING
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Make better business decisions based on information hidden
within
your data
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Develop a strong vocabulary and understanding of data mining
terminology
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Communicate with confidence among your developers and consultants
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Plan and manage your data mining projects effectively from
the start
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Leave with resources, contacts and actionable plans to substantially
reduce your project preparation time, costs and risks
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THE BUSINESS CHALLENGE
Traditionally, organizations use data tactically - to manage operations.
For competitive edge, leading organizations use data strategically
- to expand the business, to improve profitability, to reduce costs,
anticipate behavior, and market more effectively. The mining of
data for predictive indicators creates information assets that an
organization can leverage to achieve these strategic objectives.
Predictive analytics is a new component in an enterprise's
decision support system (DSS) architecture. It complements and interlocks
with other DSS capabilities such as query and reporting, on-line
analytical processing (OLAP), data visualization, and traditional
statistical analysis. These other DSS technologies are generally
retrospective.
The predictive aspect of data mining may be defined
as "the data-driven discovery and modeling of hidden patterns
in large volumes of data." Predictive analytics differs from
the retrospective technologies above because it produces models
-- models that capture and represent hidden patterns and interactions
in the data. Via data mining, a user can discover patterns and build
models automatically, without knowing exactly what s/he's looking
for.
The resulting models are both descriptive and
prospective. They address why things happened and what is likely to
happen next. A user can pose "what-if" questions to a data-mining
model that cannot be queried directly from the database or
warehouse. Examples include: "What is the expected lifetime value of
every customer account," "Which customers are likely to open a money
market account," or "How will production quality be affected if
various resources are adjusted?" |
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WHAT YOU WILL LEARN
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Basic principles and terminology for predictive analytics
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Who is utilizing predictive analytics, and why
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What are common project pitfalls and how to avoid them
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Project deployment, performance and maintenance issues
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How to define business objectives for a discovery process
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How to get started
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WHAT MAKES THIS COURSE UNIQUE
This course offers a balanced and non-promotional presentation of
data mining topics and its role in enterprise decision support. For
over nineteen years, the instructor has been deeply involved
with the development and deployment of real-world data mining
solutions.
The
presentation divided into four sessions of approximately
three-hours each. The first session is intended to provide a
general overview of predictive analytics. Subsequent
sessions address three specific issues critical to success in
the application of data mining in business environments.
This course does not drill deeply into specific algorithms or technical
implementation issues. It is also not a comprehensive
presentation of a development methodology as presented in the
Level II offering. Rather, the Level I course presents
strategic and process issues that are critical in the success of
deploying applied models in real world business
environments.
Leading products will be used from a
vendor-neutral perspective to illustrate and compare methods.
Results are drawn
from actual data mining applications and interpreted in the context
of business impact. Attendees will depart with a binder full of
slides, supporting notes and a valuable index of data mining resources. |
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COURSE OUTLINE
Session I -
Strategic Overview
This session introduces
participants to the conceptual foundation of data mining projects. It
is intended to give an overview of the types of problems that are
appropriate for data mining, offer an approach that is realistic for
applied model development in a business environment, and explain why
traditional approaches are insufficient.
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Why Build
Models?
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Data Mining:
What it is, and What it isn’t
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Matching
Technologies and Problem Types
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Belief in
Degree of Set Membership vs Right Answers
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Why Traditional
Statistics is Not Enough
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Where Data
Mining Works
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Defining Goals
for Better Performance
Session II – It’s
About the Data!
Improved performance generally
comes from one of two sources: getting more information content from
the available data, or getting better data. This session
explores sources of data, the strengths and limitations of various types of
data, and techniques for manipulating data to extract information
content.
Session III –
Conceptual Introduction to Core Modeling Technologies
The third session introduces a
number of the advanced technologies commonly used in data mining.
Discussion of these technologies in Level I is focused on conceptual
understanding, and the strengths and limitations of the
various methods. A tactical drill-down into techniques for
the various algorithms is reserved for the Level II offering.
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Linear Regression
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Logistic Regression
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Clustering
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Classification Trees
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Chaos
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Neural Networks
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Genetic Algorithms
Session IV – Making
Predictive Analytics Work!
The final session addresses the
Experimental Design and Project Definition aspects of Data Mining
projects. These areas are where most data mining projects fall
short of their potential.
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Experimental
Design
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The Data Mining
Process
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Case Studies
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Courses May Be Delivered At Your Site
Call (888) 742-2454 or send an
email inquiry
to receive a
value-based
spreadsheet quotation for training at your site.
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THE
PRESENTER
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THOMAS A. "TONY" RATHBURN is an experienced professional
with an exceptionally strong track record of innovation and creativity. Tony has worked with
commercial and government clients to develop data mining
solutions to significant business applications since the mid 1980’s. Mr. Rathburn
delivers custom workshops and consults on a wide range of commercial
assignments -- many involving CRM applications. He holds
extensive data mining experience in the banking, insurance, and
financial industries. |
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Mr. Rathburn’s Experience includes seven years teaching MIS
and Statistics at both the graduate and undergraduate level while
an instructor in the College of Business at Kent State University.
Tony’s experience covers a broad range of practical experience
in addition to his teaching background. His consulting expertise
has been concentrated in the business utilization of advanced knowledge
discovery techniques. He served as Vice President of Applied Technologies
for NeuralWare, Inc., a neural network tools and consulting company.
He was also the Research Coordinator for LakeShore Trading, Inc.,
a successful futures and options trading firm on the Chicago Board
of Trade.
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ATTENDEES' COMMENTS
"I would recommend TMA's Data Mining Level I to executives
weighing the costs and benefits of such projects within their organizations.
Tony approaches the course from a business management perspective
and presents the concepts in real-world cases making the task of
visualizing use of the process in one's own business a snap!"
Kelli R. Schultz
AVP, Information Technology
iPay, LLC
"Statisticians
and Analysts alike can benefit from this Data Mining course. It
is interesting to view the business objective from the other
side of the coin. Exploratory Data Analysis in Data Mining is
fun because the causality constraint of classical Statistics is
relaxed. Take this course and open up to another way of dealing
with large data sets."
Raymond D. Mooring, PhD
Wage and Investment Research
Internal Revenue Service
"The 'Data Mining:
Level I' course successfully takes the broad and complex subject
of data mining and organizes and explains it in a very logical and
understandable way. The training provides real-life examples of
the various aspects of data mining and a proven approach to successfully
achieving desired results. I can highly recommend TMA's Data Mining
courses to anyone interested in understanding the broad landscape
of data mining."
Dillon Ridguard
Principal, Technology Services Group
Computer Sciences Corporation
"Tony and Dean
both did a fantastic job of getting me up to speed much faster than
any book (or probably any other training class) available."
Raymond G. Henderson
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.
"Attending The
Modeling Agency's Data Mining Training Level I and II was a tremendously
rewarding experience, helping me to 'de-mystify data mining' and
interface with exceptionally intelligent people who live in the
data mining world."
Dr. Joan L. Anderson
Apparel, Merchandising, and Textiles
Washington State University
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Seating is limited to 18 participants.
Register early!
Proceed to the
On-Line Registration Form
to reserve your space today.
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