Data Mining: Level II

Training Provided by The Modeling Agency ABOUT THIS COURSE This second level offering presents a deeper examination of the data mining process at a functional level. Attendees will observe demonstrations of computer-guided analytical techniques for extracting and interpreting complex business rules from data. If you desire a rapid and substantial boost in your understanding of data mining concepts, tools, techniques and supporting methods, then this course is designed for you. 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. WHO SHOULD ATTEND 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 BENEFITS OF ATTENDING - 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 THE BUSINESS CHALLENGE The rapid emergence of electronic data processing and collection methods has lead some to call recent times as the "Information Age." However, it may be more accurately termed as "The Age of the Data Glut." Most businesses either posses a large database or have access to one. These databases contain so much data that it becomes very difficult to understand what that data is telling us. 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 WHAT MAKES THIS COURSE UNIQUE This course does not restrict or skew the presentation of data mining methods through a single product. Rather, the course gives consideration to all resources from a vendor-neutral position. The instructor has over ten years of experience in applying data mining technology to real-world applications. 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.

Data Mining: Level II 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

About The Training Provider: The Modeling Agency

More Training from The Modeling Agency

• Data Mining: Level I
• Data Mining: Level III