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Stefan Feuerriegel: Katalogdaten im Frühjahrssemester 2019

NameHerr Prof. Dr. Stefan Feuerriegel
LehrgebietWirtschaftsinformatik
Adresse
Professur f. Wirtschaftsinformatik
ETH Zürich, WEV G 201
Weinbergstr. 56/58
8092 Zürich
SWITZERLAND
Telefon+41 44 632 06 93
E-Mailsfeuerriegel@ethz.ch
URLhttp://mis.ethz.ch
DepartementManagement, Technologie und Ökonomie
BeziehungAssistenzprofessor (Tenure Track)

NummerTitelECTSUmfangDozierende
363-1098-00LBusiness Analytics
Students from the MAS MTEC are not applicable for this course and are kindly asked to enroll in the course "Executive Business Analytics (365-1120-00L)" instead.
3 KP2GS. Feuerriegel
KurzbeschreibungPrior to the start of the Information Age in the late 20th century, companies were forced to collect data from non-automated sources manually. Companies back then lacked the computing capabilities necessary for data to be analyzed, and as a result, decisions primarily originated not from knowledge but from intuition.
LernzielWith the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on computer-aided “Business Analytics”.

Business analytics refers to technologies that target how business information (or sometimes information in general) is collected, analyzed and presented. Combining these features results in software serving the purpose of providing better decision support for individuals, businesses and organizations.

This course will teach what distinguishes the varying capabilities across business analytics – namely the underlying methods. Participants will learn different strategies for data collection, data analysis, and data visualization. Sample approaches include dimension reduction of big data, data visualization, model selection, clustering and forecasting.
In particular, the course will teach the following themes:
• Forecasting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured data to facilitate the predictive performance? What are metrics to evaluate the performance of predictions?
• Data analysis: How can one derive explanatory power in order to study the response to an input?
• Clustering: How can businesses group consumers into distinct categories according to their purchase behavior?
• Dimension reduction: How can businesses simplify a large amount of indicators into a smaller subset with similar characteristics?
During the exercise, individual assignments will consist of a specific problem from business analytics. Each participant will be provided with a dataset to which a certain method should be applied to using the statistics software R.
InhaltContent:
1. Motivation and terminology
2. Business and data understanding
a. Data management and strategy
b. Data mining processes
3. Data preparation for big data
a. Software and tools
b. Knowledge representation and storage
c. Information preprocessing
4. Explanatory modeling
5. Predictive modeling
a. Classification
b. Variable selection
c. Handling non-linearities
d. Ensemble learning
e. Unsupervised learning
f. Working with unstructured data
6. Managerial implications
LiteraturJames, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer.
Sharda, Delen & Turban (2014): Business Intelligence: A Managerial Perspective on Analytics. Pearson.
363-1100-00LRisk Case Study Challenge Belegung eingeschränkt - Details anzeigen
Limited number of participants.

Please apply for this course via the official website (www.riskcenter.ethz.ch). Once your application is confirmed, registration in myStudies is possible.
3 KP2SB. J. Bergmann, A. Bommier, S. Feuerriegel
KurzbeschreibungThis seminar provides master students at ETH with the challenging opportunity of working on a real risk modelling and risk management case in close collaboration with a Risk Center Partner Company. For the Spring 2019 Edition the Partner will be Zurich Insurance Group.
LernzielStudents work on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the risk-related cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar students visit the partners’ headquarters, conduct interviews with members of the management team as well as internal and external experts, and present their results.
InhaltGet a basic understanding of
o The insurance and reinsurance business
o Risk management and risk modelling
o The role of operational risk management

Get in contact with industry experts and conduct interviews on the topic.

Conduct a small empirical study and present findings to the company
Voraussetzungen / BesonderesPlease apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than February 15, 2019.
The number of participants is limited to 14.
364-1119-00LNext-Generation Information Systems Belegung eingeschränkt - Details anzeigen
Number of participants limited to 10.
1 KP1SS. Feuerriegel, E. Fleisch
KurzbeschreibungThis seminar will explore recent advances in the areas of information systems and business analytics with a particular focus on quantitative research. An essential aspect of any research project is dissemination of the findings arising from the study.
LernzielThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
365-1120-00LExecutive Business Analytics Belegung eingeschränkt - Details anzeigen
For MAS MTEC students (second semester) but classes are together with MSc students.

Students, who have already successfully completed the course “Business Analytics (363-1098-00L)” can't register again.
3 KP2GS. Feuerriegel
KurzbeschreibungThis course will teach what distinguishes the varying capabilities across business analytics – namely the underlying methods. Participants will learn different strategies for data collection, data analysis, and data visualization. Sample approaches include dimension reduction of big data, data visualization, model selection, clustering and forecasting.
LernzielForecasting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured data to facilitate the predictive performance? What are metrics to evaluate the performance of predictions?
Data analysis: How can one derive explanatory power in order to study the response to an input?
Clustering: How can businesses group consumers into distinct categories according to their purchase behavior?
Dimension reduction: How can businesses simplify a large amount of indicators into a smaller subset with similar characteristics?
InhaltPrior to the start of the Information Age in the late 20th century, companies were forced to collect data from non-automated sources manually. Companies back then lacked the computing capabilities necessary for data to be analyzed, and as a result, decisions primarily originated not from knowledge but from intuition. With the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on computer-aided “Business Analytics”.

Business analytics refers to technologies that target how business information (or sometimes information in general) is collected, analyzed and presented. Combining these features results in software serving the purpose of providing better decision support for individuals, businesses and organizations.
Voraussetzungen / BesonderesStudents, who have already successfully completed the course “Business Analytics (363-1098-00)” can't register again.