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

• Make better business decisions based on information hidden within your data

• Develop a strong vocabulary and understanding of data mining terminology

• Communicate with confidence among your developers and consultants

• Plan and manage your data mining projects effectively from the start

• Experience firsthand that actual model-building is not as complicated as it
  might have seemed through the lecture segments

• Leave with resources, contacts and actionable plans to substantially reduce
  your project preparation time, costs and risks
  

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?"

• Basic principles and terminology for predictive analytics

• Who is utilizing predictive analytics, and why

• What are common project pitfalls and how to avoid them

• How to define business objectives for a discovery process

• Project deployment, performance and maintenance issues

• Building confidence through hands-on participation

• How to get started

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.

INTRODUCTION

• What is predictive analytics?

  • Goal driven analysis of large data sets...

    • to identify an approach for allocating organizational resources

    • that enhances performance on the organization's self-defined
      performance metrics

    • to better achieve the organization's business objectives

    • using a repeatable, consistent strategy

  • Beyond traditional statistics

• Shift your thinking

• The goal of modeling

  • Physical systems

  • Human behavior

• Behaviors of interest

• Setting up the game

  • Project team

  • Phased development cycle

  • Definitions

  • Data sandbox

• Formulas vs. Model development

• The conflict between algorithm objectives and business objectives

• Why use predictive analytics?

• Definition of data mining

  • What data mining is not

  • Why mine data?

  • The key question is "so what?"

• Successful data mining is goal-directed analysis

• Traditional statistics are insufficient in today's world

• What can data mining do?

  • Data mining opportunities

  • Data mining business goals

  • Data mining analytic goals

• Why the majority of data mining projects fail

• How much data is needed to develop a model?

  • How many variables?

  • Rules of thumb

  • Types of sampling

• Experimental design

  • Data sets used

  • Types of data distribution

  • Types of decision

• Predictive analytics key technologies overview*
  * Methods and techniques are detailed in the Model Development course

• Who needs brains when you've got software?

• Low-Risk / High-ROI project design

• The business justification for predictive analytics: Goal driven analytics

• Organizational predictive analytics opportunity identification

• Incremental project design

  • Single-tailed model development: Identify positive impacts

  • Single-tailed model development: Identify negative impacts

  • Two-tailed model development: Conflict resolution

  • Ranking across the continuum: Adding resolution

  • Subdividing dimensions: Adding detail

  • Forecasting model development

• 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

• Business Understanding (CRISP 1)

  • Determine business objective

    • Background and business objectives

    • Identify decision process

    • Business success criteria

  • Identify performance metrics

  • Calculate current baseline levels of performance

  • Determine modeling objectives

    • Requirements

    • Assumptions

    • Constraints

    • Risks and contingencies

    • Terminology

    • Costs and benefits

    • Modeling goals

    • Modeling success criteria

  • Assess resource availability

    • Hardware resources

    • Sources of data and knowledge

    • Personnel sources

  • Produce project plan

  • Prepare Business Understanding Deliverables
     

• Data Understanding (CRISP 2)

  • Review data availability

  • Collect initial data

    • Data requirements planning

    • Selection criteria

    • Insertion of data

    • Construction of Output variable

  • Describe data

    • Volumetric analysis of data

    • Attribute types and values

    • Keys

  • Review assumptions and goals

  • Explore data

    • Statistical analysis

    • Data exploration

    • Suppositions for future analysis

  • Verify data quality

  • Data Understanding Deliverables

    • Initial data collection report

    • Data description report

    • Data exploration report

    • Data quality report
       

  ( 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. )
   

• Data Preparation (CRISP-DM 3)
   

• Modeling (CRISP-DM 4)
   

• Evaluation (CRISP-DM 5)

   

• Deployment (CRISP 6)

  • Plan deployment

  • Develop monitoring and maintenance plan

  • Produce final report

  • Project review

  • Deliverables

    • Deployment plan

    • Monitoring and maintenance plan

    • Final report
       

USE CASE WORKSHOP #2

Second CRISP-DM pass for a two-tailed model implementation

• Business Understanding (CRISP-DM 1)
   

• Data Understanding (CRISP-DM 2)
   

• Data Preparation (CRISP-DM 3)
   

• Modeling (CRISP-DM 4)
   

• Evaluation (CRISP-DM 5)

   

• Deployment (CRISP 6)

 
EXTENDED MODELING TOPICS

 
WRAP-UP AND NEXT STEPS

• PA/DM Level I Course: "Model Development"

• Certification Exam (for those who complete the series)

• Product training courses

• Keep learning!

• Supplementary materials and resources

• Conferences and communities

• Get started on a project!
   

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.

Kelli R. Schultz
AVP, Information Technology
iPay, LLC
 

Dillon Ridguard
Principal, Technology Services Group
Computer Sciences Corporation
 

Raymond G. Henderson
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.
 

"A great experience. I would recommend this course for anyone interested in Predictive Analytics."

Maisam  Salehi
Analyst, Customer Insights
Giant Food Stores

Dr. Joan L. Anderson
Apparel, Merchandising, and Textiles
Washington State University

Seating is limited to 18 participants.  Register early!
Proceed to the On-Line Registration Form to reserve your space today.