Transcript Data Mining with SQL Server and R

The information herein is for informational purposes only and represents the opinions and views of Project
Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors.
Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation.
Portions © 2014 Project Botticelli Ltd & entire material © 2014 Microsoft Corp unless noted otherwise. Some slides
contain quotations from copyrighted materials by other authors, as individually attributed or as already covered
by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product
names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The
information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of
the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions,
it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli
cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli
makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.
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Data Mining
Introduction to BI & Big Data
DAX
MDX
Excel BI
Seek
profitable
customers
Understand
customer
needs
Correct
data
Predictive
analytics
Detect and
prevent
fraud
Build
effective
marketing
campaigns
Anticipate
customer
churn
Predict
sales &
inventory
Attributes
Aggregates
Flags
Relationships
single
1.
2.
3.
Training data
Mining Model
Data for
predictions
DM
DM Engine
Engine
Mining Model
Mining Model
With
predictions
SSDT
Excel
Visio
SSMS
Excel/Visio/SSRS/SSIS/Your App
OLE DB/ADOMD/XMLA
App
Data
Deploy
Analysis Services
Server
Mining Model
Data Mining Algorithm
Data
Source
Algorithm
Description
Decision Trees
Finds the odds of an outcome based on values in a training set, presents visually
Association Rules
Identifies relationships between cases
Clustering
Classifies cases into distinctive groups based on any attribute sets
Naïve Bayes
Clearly shows the differences in a particular variable for various data elements
Sequence
Clustering
Groups or clusters data based on a sequence of previous events
Time Series
Analyzes and forecasts time-based data combining the power of ARTXP (developed
by Microsoft Research) for short-term predictions with ARIMA for long-term
accuracy.
Neural Nets
Seeks to uncover non-intuitive relationships in data
Linear Regression Determines the relationship between columns in order to predict an outcome
Logistic
Regression
Determines the relationship between columns in order to evaluate the probability
that a column will contain a specific state
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The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties,
express, implied or statutory, as to the information in this presentation.
Portions © 2014 Project Botticelli Ltd & entire material © 2014 Microsoft Corp unless noted otherwise. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright
ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and
represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and
Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.