Stefan Feuerriegel: Catalogue data in Spring Semester 2020
|Name||Prof. Dr. Stefan Feuerriegel|
|Field||Management Information Systems|
Professur f. Wirtschaftsinformatik
ETH Zürich, WEV G 201
|Telephone||+41 44 632 06 93|
|Department||Management, Technology, and Economics|
|Relationship||Assistant Professor (Tenure Track)|
Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC.
This course can be complemented with Discovering Management (Excercises) 351-0778-01L.
|3 credits||3G||L. De Cuyper, S. Brusoni, B. Clarysse, S. Feuerriegel, V. Hoffmann, T. Netland, G. von Krogh|
|Abstract||Discovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. The module provides an overview of the principles of management, teaches knowledge about management that is highly complementary to the students' technical knowledge, and provides a basis for advancing the knowledge of the various subjects offered at D-MTEC.|
|Objective||The objective of this course is to introduce the students to the relevant topics of the management literature and give them a good introduction in entrepreneurship topics too. The course is a series of lectures on the topics of strategy, innovation, marketing, corporate social responsibility, and productions and operations management. These different lectures provide the theoretical and conceptual foundations of management. In addition, students are required to work in teams on a project. The purpose of this project is to analyse the innovative needs of a large multinational company and develop a business case for the company to grow.|
|Content||Discovering Management aims to broaden the students' understanding of the principles of business management, emphasizing the interdependence of various topics in the development and management of a firm. The lectures introduce students not only to topics relevant for managing large corporations, but also touch upon the different aspects of starting up your own venture. The lectures will be presented by the respective area specialists at D-MTEC.|
The course broadens the view and understanding of technology by linking it with its commercial applications and with society. The lectures are designed to introduce students to topics related to strategy, corporate innovation, corporate social responsibility, and business model innovation. Practical examples from industry will stimulate the students to critically assess these issues.
|Prerequisites / Notice||Discovering Management is designed to suit the needs and expectations of Bachelor students at all levels as well as Master and PhD students not belonging to D-MTEC. By providing an overview of Business Management, this course is an ideal enrichment of the standard curriculum at ETH Zurich.|
No prior knowledge of business or economics is required to successfully complete this course.
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 credits||1G||S. Feuerriegel|
|Abstract||Prior 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.|
|Objective||Overall learning goal|
By the end of the course, students will be able to plan, implement and evaluate analytics in applied settings in order to generate value from data for society, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage –by leveraging the growing amounts of structured and unstructured data.
Detailed breakdown by objective
To achieve this overall goal, students should after participation being able to:
Objective 1 (Managerial aspects): Understand the processes and challenges of analytics-related projects
• Identify applications for analytics in corporations and organizations that create value
• List implications for management when undertaking a project involving business analytics
• Apply the data mining process CRISP-DM to their actual setting
Objective 2 (Methodological challenges): Understand common methods for performing business analytics
• Translate use cases of business analytics into a mathematical model formulation
• Name common methods for business analytics, as well as their underlying concepts
• Compare the properties of these models
Objective 3 (Practical implementation): Performing actual evaluations of business analytics based on real-word datasets
• Preprocess data in order to transform it into relational structures
• Apply statistical software (e.g. “R” or Python) to perform business analytics in practice
• Evaluate the results in order to choose the best-performing method
|Content||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.
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.
Note: the course is a block course teaching the theoretical elements. This provides then the basis for a project work where individual students or groups implement analytics to a business-relevant datasets. This project underlies eventually the grading.
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
b. Variable selection
c. Handling non-linearities
d. Ensemble learning
e. Unsupervised learning
f. Working with unstructured data
6. Managerial implications
|Literature||James, 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.
|Prerequisites / Notice||Please note that we expect simple scripting skills (e.g. in Python), as students will apply their theoretical knowledge by implementing a machine learning application with given open-source packages.|
|363-1100-00L||Risk Case Study Challenge |
Does not take place this semester.
|3 credits||2S||A. Bommier, S. Feuerriegel|
|Abstract||This 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.|
|Objective||Students 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.|
|Content||Get 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
|Prerequisites / Notice||Please 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.
|364-1119-00L||Next-Generation Information Systems |
Number of participants limited to 10.
|1 credit||1S||S. Feuerriegel, E. Fleisch|
|Abstract||This 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.|
|Objective||The 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.|
|365-1120-00L||Executive Business Analytics |
Exclusively for MAS MTEC students (2nd semester).
|1 credit||1G||S. Feuerriegel|
|Abstract||This course will combine cutting edge thinking about Artificial Intelligence & Machine Learning, with application use cases and a practical framework that will enable participants to determine and plan their own workplace application.The focus is less on the how (i.e. how the algorithms function) but more on techniques to identify suitable use cases.|
|Objective||Objective 1 (Managerial aspects): Understand the processes and challenges of analytics-related projects |
• Being able to identify applications for analytics in corporations and organizations that create value
• Being able to list implications for management when undertaking a project involving business analytics
• Being able to to describe the data mining process CRISP-DM to their actual setting
Objective 2 (Methodological challenges): Understand common methods for performing business analytics
• Being able to name common methods for business analytics, as well as their underlying concepts
• Being able to contrast supervised vs. unsupervised learning (clustering)
|Content||Prior to the start of the Information Age in the late 20th century, 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”. |
As examples, machine Learning algorithms enable detection of patterns and predict or recommend actions by processing large data sets of information, instead of response to instructions. Deep learning is a type of machine learning using Neural Networks to process huge amounts of data through successive layers of learning to arrive at a conclusion or recommendation.
This highly interactive and application driven course will lay a foundation of understanding of these cutting-edge concepts, followed by a contemporary Case Study of relevance to marketplace application. The class dialog will bring out the underlying complexities of under-standing business challenges and determining the suitability of AI solutions thus enhancing participants AI/ML decision making.
This will be followed by a participative discussion to connect the knowledge and case study application to the participants own experiences. Based on it, we jointly define the criteria for the type of situations where AI and ML are appropriate and develop potential solutions.
Developing a technological solution to an AI challenge is only the first step. The practitioner will need to recognize implementation as a potentially disruptive change that will require careful change management leadership for effective implementation. Given the novelty of the theme and the rare experience in industry, this part will be accompanied by insights from practitioners.
|Lecture notes||The following technical aspects will be covered from a methodological angle:|
- 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?
|Literature||The course involves two pre-readings that students are kindly asked to read before the first class:|
DeepMind creates algorithm to predict kidney damage in advance
Building the AI-Powered Organization
|Prerequisites / Notice||Students, who have already successfully completed the course “Business Analytics (363-1098-00)” can't register again.|