Data mining, the art and science of learning
from data, covers a number of different procedures. This course covers key
unsupervised learning techniques: association rules, principal components
analysis, and clustering. (Introduction to Predictive Modeling covers
techniques that are used to predict a record's class or the value of an outcome
variable on the basis of a set of records with known outcomes).
Learn with Mr. Anthony
Babinec in online course " Data Mining:
Unsupervised Techniques" at Statistics.com. For more details
please visit at http://www.statistics.com/datamining/.
We, C-eLT, Pune, partner with Statistics.com
and offer these courses to Indian participants at special prices payable in
INR.
The course will include an integration of
supervised and unsupervised learning techniques. This is a hands-on course -
participants in the course will have access to an Excel-based comprehensive
tool for data-mining, XLMiner, the use of which will be explained in the
course. Participants will apply data mining algorithms to real data, and will
interpret the results.
Who Should Take This Course:
Marketers seeking to specify customer segments
and identify associations among products purchased, environment scientists
seeking to cluster observations, analysts who need to identify the key
variables out of many, MBA's seeking to update their knowledge of quantitative
techniques, managers and scientists who want to see what data-mining can do,
and anyone who wants a practical hands-on grounding in basic data-mining
techniques.
Course Program:
Course outline: The course is structured as follows
SESSION
1: Principal Components Analysis
- The goal - dimensionality reduction
- The principal components
- Scale variance estimation
- Normalizing the data
- Principal components and least orthogonal
squares
- Exercises
SESSION
2: Clustering
- What is cluster analysis?
- Hierarchical methods
- Nearest neighbor (single linkage)
- Farthest neighbor (complete linkage)
- Group average (average linkage)
- Optimization and the k-means algorithm
- Similarity measures
- Other distance measures
- The curse of dimensionality
- Exercises
SESSION
3: Association Rules
- Discovering association rules in transaction
databases
- Support and confidence
- The apriori algorithm
- Shortcomings
- Exercises
SESSION
4: Integration of Supervised and Unsupervised learning
- Clustering into customer segments
- Profiling of customer segments
- Classifying new records by segment
The final lesson is an integration of
supervised and unsupervised techniques. To get the full benefit of this course,
familiarity with supervised learning is needed, but those not requiring this
integration can learn about clustering, association rules and principal
components without having had a course in supervised learning.
The instructor, Anthony
Babinec, is the president of AB Analytics and served previously as Director
of Advanced Products Marketing at SPSS; he worked on the marketing of
Clementine and introduced CHAID, neural nets and other advanced technologies to
SPSS.
You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course. The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week. Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.
We, the Center for eLearning and Training
(C-eLT), Pune, partner with Statistics.com and offer these courses to Indian
participants at special prices payable in INR.
Call: 020 6680 0300 / 322
Websites:
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