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Service Analytics A

Service Analytics A
type: Vorlesung (V) links:
semester: SS 2019
time: 2019-04-23
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II


2019-04-30
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-05-07
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-05-14
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-05-21
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-05-28
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-06-04
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-06-11
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-06-18
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-06-25
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-07-02
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-07-09
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-07-16
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II

2019-07-23
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal
10.50 Kollegiengebäude Bauingenieure II


lecturer: Prof.Dr. Thomas Setzer
Prof. Dr. Hansjörg Fromm
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/x86y4euFSxmVr2_roSCQxA">2595501</a>
Prerequisites

Recommendations:

The lecture is addresed to students with interests and basic knowledge in the topics of Operations Research, decritptive and inductive statistics.

Bibliography
  • Business Forecasting, Wilson, J. H., Keating, B., McGraw-Hill, 2002
  • Multivariate Data Analysis, Hair, J. F., Black, B., Babin, B., Anderson, R. E., 2008
  • Analytics at Work, Davenport, T. H., Harris, J. G., Morion, R., Harward Business Press, 2010
  • Business Analytics for Managers, Jank, W., Springer, 2011

Online Sources:

  • The data deluge, The Economist, Feb. 2010
  • Competing on Analytics, T. Davenport in Harward Business Review, Feb. 2007
  • Mit Advanced Analytics können Händler Kundendaten optimal nutzen, McKinsey Handelsmarketing, Feb. 2011

Further readings will be provided in the lecture.

Content of teaching

Today's service-oriented companies are starting to optimize the way services are planned, operated, and personalized by analyzing vast amounts of data from customers, IT-systems, or sensors. As the statistical learning and business optimization world continues to progress, skills and expertise in advanced data analytics and data and fact-based optimization become vital for companies to be competitive. In this lecture, relevant methods and tools will be considered as a package, with a strong focus on their inter-relations. Students will learn to analyze and structure large amounts of potentially incomplete and unreliable data, to apply multivariate statistics to filter data and to extract key features, to predict future behavior and system dynamics, and finally to formulate data and fact-based service planning and decision models.

More specifically, the lessons of this lecture will include:

  • Co-Creation of Value Across Enterprises
  • Instrumentation, Measurement, Monitoring of Service Systems
  • Descriptive, predictive, and prescriptive Analytics
  • Usage Characteristics and Customer Dynamics
  • Big Data, Dimensionality Reduction, and Real-Time Analytics
  • System Models and What-If-Analysis
  • Robust Mechanisms for Service Management
  • Industry Applications of Service Analytics

Tutorials
Students will conduct lecture accompanying, guided exercises throughout the semester.

Workload

The total workload for this course is approximately 135.0 hours. For further information see German version.

Aim

Participants are able to structure large sets of available data and to use that data for planning, operation, personalization of complex services, in particular for IT services. They learn a step-by-step approach starting with analyzing possibly incomplete data, techniques of multivariate statistics to filter data and to extract data features, forecast techniques, and robust planning and control procedures for enterprise decision support.

Exam description

The assessment consists of a written exam (60 min) (according to §4(2), 1 of the examination regulation).