Schwerpunkt Wirtschaftsinformatik
Aktuelle Veranstaltungen nach Lehrstühlen

Aktuelle Veranstaltungen nach Lehrstühlen

Nachfolgend können Sie sich über die angebotenen Lehrveranstaltungen aus dem Schwerpunkt Wirtschaftsinformatik informieren.

Lehrstuhl mit Schwerpunkt Betriebliche Informationssysteme

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow’s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. “Adventure Works Cycles”, SQL Server sample database
2. “Retail Sense transaction data”, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Lehrstuhl mit Schwerpunkt Informations- und IT-Servicemanagement

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Lehrstuhl mit Schwerpunkt Internet- und Telekommunikationswirtschaft

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Lehrstuhl mit Schwerpunkt Produktion und Logistik

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Juniorprofessur mit Schwerpunkt E-Commerce

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Lehreinheit für Wirtschaftsinformatik

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1

Honorarprofessur für Wirtschaftsinformatik

Data Warehousing and Data Mining (Master)
SWS:
Dozierende:
Lehner, Franz, Prof. Dr.
Veranstaltungstyp:
Vorlesung
Veranstaltungsart:
Gastvorlesung in engl. Sprache von Saji K. Mathew, PhD
Beginn des Lehrevaluationszeitraums:
Beschreibung:
COURSE PHILOSOPHY
Data-driven decisions have become a distinctive factor defining the success mantra of high performance firms. When used wisely, analytical capabilities have enormous power to enhance the competitiveness of almost any company or enterprise. It is therefore imperative that tomorrow?s business leaders learn to apply data mining techniques to enterprise data to draw management insights in decision making pertaining to their business domain.

This course equips students with the models, tools and thinking required to use enterprise and other data for business decisions. Thus, the course will enable you to prepare for a career in consulting, business analytics and market research. Students who are seeking positions as functional managers would also benefit from the course as future users of data for decisions. Function managers will gain insights to set directions for research, and also to commission and evaluate business research projects.

The business context of the course is set in the backdrop of decision problems surrounding market customization: segmentation, profiling and targeting; forecasting covering sectors such as retail, manufacturing, and stock markets. This course will introduce the context of OLAP and data mining, and cover prominent modeling techniques in data mining such as decision trees, regression, clustering, time series and ANN.

COURSE OBJECTIVES
- Introduce business intelligence architecture and its components covering databases, data warehouse, OLAP and data mining
- Learn to convert business problems into data mining problems and thus understand data mining process.
- Explore data mining techniques covering classification, statistical learning, machine learning and text mining with applications in business
- Develop skills for using data mining software tools to solve business problems.

COURSE CONTENTS
- Introduction, expectations, QA. Vocabulary of business analytics, Business value of analytics, BI architecture, adoption
- Fundamentals of data management, RDBMS, SQL, Data warehousing, ETL, OLAP, Big data and analytics, OLAP demo
- Data sources for data mining, data mining process, over view of data mining techniques, statistics and algorithms in data mining, statistical learning, Privacy issues and ethics in data mining
- Classification, classification techniques, classifier performance, scoring models,
Classification using decision trees; Implementation in R: problem of targeted mailing
- Cluster analysis, clustering techniques, Implementation in R: Clustering for segmentation and profiling
- Survival analysis, Hazard, Customer churn, Customer profitability and life time value using data mining
- Machine learning, Artificial neural networks for time series modeling, Implementation in R/MATLAB: Financial time series modeling using ANN
- Mining the web: Text mining, process, text mining using R- the case of a movie discussion Forum

SOFTWARE
- R
- MATLAB (likely to drop)
- MS Excel, MS Access
These software packages come with extensive help documentation to get started and get going. Special training to use some features of the software will be offered on demand.
Ort:
(ITZ) R 252: Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, (JUR) SR 147a: Mittwoch. 24.05. 14:00 - 17:00
Zeiten:
Termine am Donnerstag. 18.05. 10:00 - 12:00, Donnerstag. 18.05. 16:00 - 18:00, Freitag. 19.05. 08:00 - 12:00, Montag. 22.05. 08:00 - 10:00, Montag. 22.05. 12:00 - 14:00, Dienstag. 23.05. 14:00 - 18:00, Mittwoch. 24.05. 08:00 - 12:00, Mittwoch. 24.05. 14:00 - 17:00, Ort: (ITZ) R 252, (JUR) SR 147a
Erster Termin:Do , 18.05.2017 10:00 - 12:00, Ort: (ITZ) R 252
Semester:
SS 17
Veranstaltungsnummer:
37680
TeilnehmerInnen
Studierende der Master-Studiengänge Wirtschaftsinformatik und Business Administration sowie Erasmus-Studierende
Lernorganisation:
PEDAGOGY - The course will be conducted through classroom lectures, lab sessions, exercises, quizzes, assignment discussions/presentations and mini projects. - Small groups of students will be formed in the beginning of the course. The team projects will be based on business problems involving modeling, selected from various domains depending on the interest/experience/ambitions of the teams. The teams will also work on brief analytical presentations of research papers related to BI and analytics
Leistungsnachweis:
EVALUATION Written examinations 35% Project work 40% Assignments 25%
Anrechenbar für:
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Methoden > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Grundlagen > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Wirtschaftsinformatik > Version 1 > Masternote > Wirtschaftsinformatik/ Informations Systems > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > International Management and Marketing > Vertiefung > 266140 | Data Mining and Data Warehousing
Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Masternote > Wirtschaftsinformatik / Information Systems > Vertiefung > 266140 | Data Mining and Data Warehousing
Sonstiges:
Die Veranstaltung "Data Warehousing and Data Mining" findet geblockt voraussichtlich im Zeitraum vom 18.05. bis zum 24.05.2017 jeweils von 08:00 Uhr bis 12:00 Uhr statt.

Es sind leider bereits alle Plätze vergeben - eine Anmeldung ist nicht mehr möglich.
ECTS-Punkte:
5
Ende des Lehrevaluationszeitraums:
Literatur:
DATA SOURCES
1. ?Adventure Works Cycles?, SQL Server sample database
2. ?Retail Sense transaction data?, Real life data of a fashion retailer
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
4. Yahoo! Finance
5. www.twitter.com
6. HTTP://WWW-BCF.USC.EDU/~GARETH/ISL/

READINGS
Text Book (Recommended):
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques, 3rd ed, MA: Elsevier.

Suggested Readings
  • James, G., Witten, D., Hastie, T. and Tibshirani,R. (2013) An Introduction to Statistical Learning with Applications in R, Springer: NY
-Hastie, T.,Tibshirani,R. and Friedman, J. (2008) The Elements of Statistical Learning, Data Mining, Inferences and Prediction, Springer: NY
-Berry, M. J. A. and Linoff, G. S. (2006) Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Delhi: Wiley Publications.
  • Davenport, H.D., & Harris, J.G. (2007). Competing on analytics, The new science of winning. Boston, MA: Harvard Business School Press.
  • Davenport, H. D. (2014) Big Data @ Work, Dispelling the Myths, Uncovering the Opportunities, Harvard Business Review press, Boston.
Hinweise zur Anrechenbarkeit:
Weitere Informationen zu dieser Veranstaltung:
Heimatinstitut: Lehrstuhl für Wirtschaftsinformatik mit Schwerpunkt Informations- und IT-Servicemanagement
Angemeldete Teilnehmer: 19
Anzahl der Postings im Forum: 6
Anzahl der Dokumente im Downloadbereich: 1