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Three Days:
$1,995
Series Package: $2,995
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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. There are no prerequisites for this course.
However, participants will benefit by reviewing
the
CRISP-DM guide ahead of the training.
"Predictive Analytics & Data Mining:
Strategic Implementation" offers a concentrated presentation of capabilities, limitations, risks, rewards,
use cases, best practices, strategy and lifecycle management.
Those in attendance will actively step through the industry
standard process for data mining and realize why an advanced
degree in statistics, mathematics or computer science is no
longer needed to succeed in predictive analytics. Live
working sessions reveal real-world obstacles and breakthroughs
from which to interpret, learn and apply.
Practitioners seeking to drill down into the
tactical implementation
of predictive analytics methods may also attend TMA's " Predictive
Analytics & Data Mining: Model Development course.
The "Model Development" course is the counterpart to this
production within the series, two days immediately
preceding this course at the same public venue.
Make sure to view the
course series overview page
to compare the two primary orientations and target the most fitting
agenda for your experience, situation and objectives. |
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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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Experience firsthand that actual
model-building is not as complicated as it
might have seemed through the lecture segments
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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 data-driven extension
to 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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How to define business objectives for a discovery process
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Project deployment, performance and maintenance issues
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Building confidence through
hands-on participation
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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. The instructor has been deeply involved
with the design, development and deployment of real-world data mining
solutions.
This course does not drill deeply into specific algorithms or technical
implementation issues. For a comprehensive
presentation of model development methodology and techniques,
refer to the "Predictive
Analytics & Data Mining: Model Development" course which
directly precedes this event at public venues. This event
in the series presents strategic and process challenges that are
critical to the success of
deploying applied models in real world business
environments.
Leading commercial and open-source products will be used from a
vendor-neutral perspective to illustrate and compare methods -- not
to showcase tools.
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, hands-on experience, a valuable index of
data mining resources and certification upon attending the full
series and passing an on-line exam. |
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COURSE OUTLINE
INTRODUCTION
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What is predictive analytics?
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Shift your thinking
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The goal of modeling
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Physical systems
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Human behavior
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Behaviors of interest
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Setting up the game
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Project team
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Phased development cycle
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Definitions
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Data sandbox
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Formulas vs. Model development
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The conflict between algorithm
objectives and business objectives
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Why use predictive analytics?
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Definition of data mining
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Successful data mining is
goal-directed analysis
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Traditional statistics are
insufficient in today's world
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What can data mining do?
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Data mining opportunities
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Data mining business goals
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Data mining analytic goals
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Why the majority of data mining
projects fail
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How much data is needed to
develop a model?
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How many variables?
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Rules of thumb
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Types of sampling
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Experimental design
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Predictive analytics key
technologies overview*
* Methods and techniques are detailed in the
Model Development course
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Who needs brains when you've
got software?
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Low-Risk / High-ROI project
design
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The business justification for
predictive analytics: Goal driven analytics
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Organizational predictive
analytics opportunity identification
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Incremental project design
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Single-tailed model
development: Identify positive impacts
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Single-tailed model
development: Identify negative impacts
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Two-tailed model
development: Conflict resolution
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Ranking across the
continuum: Adding resolution
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Subdividing dimensions:
Adding detail
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Forecasting model
development
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A 'real world' standardized
development process:
The CRoss-Industry Standard Process for Data Mining (CRISP-DM)
USE CASE WORKSHOP #1
Implement CRISP-DM
for a single-tailed model
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Business Understanding (CRISP 1)
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Determine business objective
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Background and business
objectives
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Identify decision process
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Business success criteria
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Identify performance metrics
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Calculate current baseline
levels of performance
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Determine modeling objectives
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Assess resource availability
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Produce project plan
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Prepare Business Understanding
Deliverables
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Data Understanding (CRISP
2)
(
Note: CRISP-DM
Parts 3, 4 and 5 are detailed in the "Model Development"
course and extended into practice in this course. It is
helpful but not necessary
to have had the tactical drill-down into these Parts prior to
their implementation. )
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Data Preparation (CRISP-DM 3)
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Modeling (CRISP-DM 4)
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Evaluation (CRISP-DM 5)
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Deployment (CRISP 6)
USE CASE WORKSHOP #2
Second CRISP-DM pass
for a two-tailed model implementation
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Business Understanding
(CRISP-DM 1)
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Data Understanding (CRISP-DM 2)
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Data Preparation (CRISP-DM 3)
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Modeling (CRISP-DM 4)
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Evaluation (CRISP-DM 5)
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Deployment (CRISP 6)
EXTENDED MODELING TOPICS
WRAP-UP AND NEXT STEPS
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PA&DM: "Model
Development" Course
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Certification Exam (for those
who complete the series)
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Product training courses
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Keep learning!
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Supplementary materials and
resources
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Conferences and communities
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Get started on a project!
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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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ATTENDEES' COMMENTS
"There is no better way to learn Data
Mining than to do it yourself. Take this course for an invaluable
hands-on experience of project definition and implementation. Even
if your organization does not adopt predictive analytics as standard, you will have acquired a much needed skill
set."
Raymond D. Mooring, PhD
Wage and Investment Research
Internal Revenue Service
"Great presentation and
summarization of predictive analytics. I sat through two
days of the Predictive Analytics World conference and got less
from that than I received in the first two hours of this course!
Thanks!"
"This course gave
me just what I needed: a clear conceptual idea of how a data mining
project is designed and implemented, and the hands-on to gain
confidence in the process."
Dotty Korsey
Market Information Manager
Bank of Hawaii
"This course opened my eyes to the big picture in a practical
way. The content of the Project Implementation course was very
clear and responsive to my needs. My questions were
answered directly and clearly. Exceeded my expectations!"
Bill Scharffenberg
ITS - Business Solutions
Surewest Communications
“A few months ago, I attended Prediction Impact’s data mining
course. It provided a good orientation to predictive analytics.
But I wanted to learn how to really apply data mining at the
project level. TMA’s course series provided the comprehensive
and pragmatic approach I was truly seeking that will allow me to
dive confidently and properly into the practice.”
Ernest Ngwa
Aspiring Data Mining Practitioner
Lanham, MD
"I would recommend TMA's
Project Implementation course 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
"When
the only complaint is that the course could be longer, I think
you've got an excellent class! I very much enjoyed the
instructor's use of a real data set to demonstrate principles taught
throughout the entire class. The instructor went out of his way both
before and during the class to help me to translate the class
material to my own work."
Susan Glass
Senior Engineer, Biological Technologies Analysis Solutions
Wyeth
"The
instructor's presentation
was quite thoughtful and very well organized. I came away with a
solid map for the ever changing data mining landscape."
David Cousins
Divisional Scientist
BBN Technologies
"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 'Project
Implementation' 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 and predictive analytics."
Dillon Ridguard
Principal, Technology Services Group
Computer Sciences Corporation
"If you want a thorough introduction to
predictive analytics at the
project level with a wealth of real world experience solving
problems, then Tony's your guy."
Elies Koudier
Professor of Marketing
Ferris State University
"A great experience. I would recommend
this course
for anyone
interested in Predictive Analytics."
Maisam
Salehi
Analyst, Customer Insights
Giant Food Stores
"This class, by far, is the most interesting,
motivating and applicable class I have taken in a very long
time. Tony provides a refreshingly different perspective
on predictive modeling and approach methodology. Not only
would I absolutely recommend this course to any colleague
or anyone interested in the practical, yet powerful insights
into predictive modeling, but I may look into additional
learning and or professional services opportunities. I
can't wait to get back to work and jump right into applying the
concepts and learnings."
Anonymous
"Both instructors in the
series did a fantastic job of getting me up to speed in
predictive analytics much faster than
any book (or probably any other training class or conference) available."
Raymond G. Henderson
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.
"Attending The
Modeling Agency's series 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 20 participants.
Register early!
Proceed to the
On-Line Registration Form
to reserve your space today.
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