Those who may be responsible for project leadership may benefit by obtaining a solid strategic framework through the Data Mining: Level I offering: two days immediately preceding this course at the same site. Likewise, participants who wish to apply the techniques and methods presented in this course in a hands-on workshop environment through team-oriented exercises should consider attending Data Mining: Level III.

IT PROFESSIONALS who wish to expand their skills in this increasingly visible area within the corporate IT agenda

PROJECT LEADERS who must report on developmental progress, resource requirements and system performance

DECISION SUPPORT SYSTEM ARCHITECTS who require an understanding of the infrastructures required for supporting a data mining solution

BUSINESS ANALYSTS who must develop and interpret the models, communicate the results and make actionable recommendations

FUNCTIONAL ANALYSTS: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Inventory Flow Analysts, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers, e-commerce System Architects and Web Data Analysts
 

• Vendor-neutral exposure to tools and techniques that will place you months ahead in method planning and product surveying
• Examine which methods and tools are most effective for your needs
• Avoid pitfalls in data preparation, modeling, and results interpretation
• Leave with resources, contacts and actionable plans to substantially increase your analysis capabilities while minimizing dead end

There is hardly a transaction that does not generate a computer record somewhere. All this data has meaning with respect to making better business decisions or understanding customer needs and preferences. But how do you discover those needs and preferences in a database that contains gigabits of seemingly incomprehensible numbers and facts? Data mining does just that.

The intent of this course is to offer attendees a stronger grasp of data mining techniques, and a solid understanding of how various methods and tools apply to different kinds of data intensive problems.

WHAT YOU WILL LEARN

• The data mining process and general implementation
• How to prepare raw data and benefit from visualization
• Various data mining methods and how they compare
• Advanced model building techniques
• Results analysis and validation
• Technology and product selection
• Solution integration, ongoing performance and maintenance
• Where to begin and how to obtain resources and support

In addition, live modeling demonstrations projected from the presenter's machine will support the instructional sessions. The demonstrations will highlight superior performance as well as pitfalls. The instructor will show how to evaluate various packages based on strengths, limitations, value and general performance.

COURSE OUTLINE

DAY 1

• Data Mining Description
  • What is Data Mining?
  • What Can Data Mining Do?
  • How is Data Mining Used?
  • Data Mining Process (CRISP-DM)
   
• Designing the Data Mining Project
  • Business Questions
  • Data Mining Objectives
  • Identifying Data for Data Mining
   
• Data Characterization
  • Univariate and Multivariate Descriptions of Data
  • Cleaning and Conditioning Data
        • Missing Data, Outliers, and Variable Formatting
  • Effective Use of Data Visualization
  • Demonstration #1
   
• Feature Creation and Selection
  • Binning Real-valued and Categorical Variables
  • Correcting Problems with Variable Distributions
  • Removing Redundant Variables
  • Demonstration #2
   
• Creating Data Samples
  • Ways to Sample Data
       • Random and Stratified Sampling
       • Cross-validation
       • Bootstrapping
       • How Much Data is Needed?
   
• Model Scoring and Deployment
  • How algorithms score models
       • Estimation
       • Classification
  • How users (should) score models

DAY 2

• Overview and Comparison of Data Mining Algorithms
  • Predictive vs. Descriptive Models
  • Data Mining Algorithm Taxonomy
  • Data Mining vs. OLAP
   
• Supervised Learning Algorithm Descriptions and Tips
  • Decision Trees
  • Demonstration #3
  • Linear and Logistic Regression
  • Neural Networks
  • Demonstration #4
  • Other Methods
   
• Unsupervised Learning Algorithm Descriptions and Tips
  • Association Rules
  • Clustering
  • Kohonen Self-Organizing Maps
  • Demonstration #5
   
• Model Ensembles and Deployment
  • What are model ensembles?
        • Bagging, Boosting and Other Killer B's
  • How to Create Model Ensembles
  • Model Deployment
   
• Data Mining Software Tools
  • Types of Tools
        • General Purpose vs. Industry Specific
        • Free Tools vs. Commercial Tools
  • Feature Comparisons
   
• Data Mining Resources
   

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.

Yiguang Qiu, PhD
Marketing Department
Amica Insurance

 
"The wealth of information covered in these courses, as well as the in-depth demonstrations of multiple software packages, made the sessions valuable from a wide range of perspectives.  I will certainly recommend that others attend."

Brent King
AVP, Managed Care Analytics / Business Development
HealthSmart Preferred Care

 
"The instructor and course material are first rate. Any organization that believes data mining should be a part of their business operations portfolio would be making a wise investment by attending this course."

Eric Rickard
Information Computing Sciences
SRI International
 

Stephen Pearce
Preventive Medicine
Kaiser Permanente

Lewis Kohnle
Planner / Analyst
The Mitchell Gold Company
 

Raymond D. Mooring, PhD
Wage and Investment Research
Internal Revenue Service
 

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