Tom a s Horv ath BUSINESS ANALYTICS · (Michael Benner & Alan Barnett)1 \I think of analytics as a...
Transcript of Tom a s Horv ath BUSINESS ANALYTICS · (Michael Benner & Alan Barnett)1 \I think of analytics as a...
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Tomas Horvath
BUSINESS ANALYTICS
Lecture 0
Overview
Information Systems and Machine Learning Lab
University of Hildesheim
Germany
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BA and its relation to BI
“Business analytics is the continuous iterative exploration andinvestigation of past business performance to gain insight and drivebusiness planning.” (Michael Benner & Alan Barnett)1
“I think of analytics as a subset of BI based on statistics, predictionand optimization. The great bulk of BI is much more focused onreporting capabilities. ’Analytics’ has become a sexier term to use –and it certainly is a sexier term than ’reporting’ – so it’s slowlyreplacing ’BI’ in many instances” (Tom Davenport)2
1http://www.docstoc.com/docs/7486045/Next-Generation-Business-Analytics-Technology-Trends
2http://www.informationweek.com/news/software/bi/showArticle.jhtml?articleID=222200096
Tomas Horvath ISMLL, University of Hildesheim, Germany 1/11
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What is the course about?
First of all, the lecturer is not an economist. . . It means, that he cannot provide You with any economical blabla about business. . .
Instead, we’ll learn how to extract implicit, previously unknown andpotentially useful information from data, i.e. data mining.
Why to mine data?
• There are often useful information hidden in data.
• Lots of data has been/is being collected, impossible to analyze allof it manually for managers.
• Competitive pressure, i.e. more knowledge about customers andgoods, better business decisions.
Tomas Horvath ISMLL, University of Hildesheim, Germany 2/11
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Origins of DM
Tomas Horvath ISMLL, University of Hildesheim, Germany 3/11
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The goal of the course
Provide You with basic data mining – pattern mining, forecasting,recommendation and web mining – techniques.
• Solid theoretical background
• Some freely available software tools
The course is intended to address topics which are complementaryto the contents of other ISMLL courses
• Machine Learning (MSc)
• Advanced Topics in Machine Learning (MSc)
• Bayesian Networks (MSc)
• Business Intelligence (BSc)
• Database Systems and Semantic web related courses at UHi
Tomas Horvath ISMLL, University of Hildesheim, Germany 4/11
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The contents of the course
1 The CRISP-DM methodology• The KDD process; Data pre-processing; Data mining basics.
2 Patterns• Clustering; Frequent Itemset Mining; Association Rules.
3 Time series, Sequences• Time series forecasting; Time series/Sequence classification.
4 Recommender Systems• User feedback; Item vs. Rating prediction; Collaborative filtering &
Content-based filtering; Context-aware recommendations.
5 Web and Social mining• Tag recommendation; Link prediction; Trust prediction; Opinion
mining.• Student projects
Tomas Horvath ISMLL, University of Hildesheim, Germany 5/11
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The CRISP-DM methodology
Tomas Horvath ISMLL, University of Hildesheim, Germany 6/11
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Patterns
• Which items appear together in one basket frequently?
• Which customers have similar shopping behaviours?
Tomas Horvath ISMLL, University of Hildesheim, Germany 7/11
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Time series, Sequences
• Is there any trend or seasonality in data?
• How to forecast future values?
• How to classify customers based on their credit card usage?
• How to detect anomalies?
Tomas Horvath ISMLL, University of Hildesheim, Germany 8/11
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Recommender Systems
• Which “unseen“ items would the user be most likely interested in?
• How would the user rate an “unseen” item?
Tomas Horvath ISMLL, University of Hildesheim, Germany 9/11
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Web and Social Mining
Tomas Horvath ISMLL, University of Hildesheim, Germany 10/11
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Good to know. . .
First of all: Please, sign up for the course in LSF!
• Lectures• provided by Tomas Horvath• Every Wednesday: 08-10 ct. in D017• Every second Thursday: 8-10 ct. in B26
• Tutorials• provided by Osman Akcatepe• Every odd Thursday: 16-18 ct. in B26
• Final mark will be computed as• 20% from the tutorials• 80% from the final examination
Tomas Horvath ISMLL, University of Hildesheim, Germany 11/11