Dr. Anthony Babinec, President of AB Analytics and previously Director of
Advanced Products Marketing at SPSS, will present two online courses in
data mining at statistics.com, each running from October 7 – November
4. Participants can ask questions and exchange comments with Dr. Babinec
via a private discussion board throughout the period.
1. “Data Mining – Unsupervised Techniques” covers key unsupervised
learning techniques used in data mining – association rules, principal
components analysis, and clustering. This is a hands-on course that
includes an integration of supervised and unsupervised learning techniques.
2. “Rule Induction” course covers two main machine-learning approaches to
generating, or “discovering,” useful rules that describe the data in large
databases. Association learning (producing “association rules” – if you
bought “x”, you may also like “y”) will be considered, looking at the
industry standard method: APRIORI. As noted above, this is an
unsupervised technique. The course also covers two decision tree methods –
C4.5 and CHAID. Both are supervised machine learning processes in which
classification rules are generated from data in which the class of each
record is known (fraud/not-fraud; purchaser/not-purchaser, etc.). The
rules can then be applied to similar data in which the class is not known.
There is some overlap in coverage between the two courses; #1 is broader
and more attentive to the larger data mining context, #2 is deeper and pays
greater attention to the algorithms. A tuition discount of 50% is
available for “Rule Induction” if you take both courses at the same time:
use the coupon code “RULES” (or “RULESA” for academic pricing) when signing
As with all online courses at statistics.com, there are no set hours when
you must be online, and you can interact with the instructor over a period
of 4 weeks via a private discussion board. We estimate you will need about
10 hours per week.
For details and
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