Data Mining Techniques: Theory and Practice
Duration: 3.0 days CEU: 1.8
Presented by Michael J. A. Berry or Gordon S. Linoff, founders of Data Miners, Inc. and co-authors of Data Mining Techniques and Mastering Data Mining
Explore the inner workings of data mining techniques and how to make them work for you. Students are taken through all the steps of a data mining project, beginning with problem definition and data selection, and continuing through data exploration, data transformation, sampling, portioning, modeling, and assessment.
Learn how to
- use a data mining methodology
- build and use decision trees and neural networks for modeling and scoring
- use survival analysis and create survival curves.
Who should attend
Business analysts, their managers, and statisticiansPrerequisites
No prior knowledge of statistical or data mining tools is required.Course Contents
Introduction to Data Mining- what is data mining?
- directed and undirected data mining
- models
- profiling and prediction
- why have a methodology?
- how data miners can inadvertently learn things that are not true
- translating business problems into data mining problems
- the importance of model stability
- finding the right input variables
- sampling to create balanced model sets
- partitioning to create training, validation, and test sets
- data preparation
- model assessment
- developing intuition about data
- data structure
- data types
- data values
- exploring distributions
- summary statistics
- histograms
- using SAS Enterprise Miner for data exploration
- the null hypothesis
- statistical significance
- confidence bounds
- variance and standard deviation
- standardized values
- correlation
- linear regression
- logistic regression
- using SAS Enterprise Miner to build regression models
- decision trees as data exploration and classification tools
- decision trees for modeling and scoring
- decision trees for variable selection
- alternate representations of decision trees
- algorithms used to build decision trees
- splitting criteria
- recognizing instability and overfitting in decision tree models
- capturing interactions between variables
- using SAS Enterprise Miner to build decision trees
- origins of neural networks
- neural networks compared with regression
- the algorithms used to train neural networks
- data preparation requirements for neural networks
- picking appropriate inputs for neural networks
- creating neural network models using SAS Enterprise Miner
- similarity and distance
- distance metrics appropriate for different kinds of data
- the role of the training set in MBR
- combining the votes of several neighbors
- other K-nearest neighbor techniques
- collaborative filtering
- using the SAS Enterprise Miner MBR node
- more on similarity and distance
- the K-means algorithm
- divisive clustering
- agglomerative clustering
- data preparation for clustering
- interpreting clusters
- finding clusters with SAS Enterprise Miner
- origins of survival analysis
- how business data is different from clinical data
- hazards and hazard charts
- retention curves and survival curves
- calculating survival from retention
- calculating hazards empirically
- parametric hazard models
- censoring
- competing risks
- survival based forecasting
- using SAS code in SAS Enterprise Miner to create survival curves
- link analysis
- genetic algorithms
- association rules
- using SAS Enterprise Miner to discover associations in retail data
- formulating the business problem as a data mining problem
- finding the tool that fits the problem
Software Addressed
This course addresses the following software product(s): SAS Enterprise Miner.Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.U.S. Schedule
| 17SEP2008 Chicago, IL 08DEC2008 Boston, MA | 29OCT2008 M2008 - Las Vegas, NV | 19NOV2008 San Francisco, CA |
Check for additional and updated schedule information online at support.sas.com/courses/bdmt.html.