Service Analytics A
- Type: Vorlesung (V)
- Semester: SS
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Time:
2018-04-17
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-04-24
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-05-08
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-05-15
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-05-22
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-05-29
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-06-05
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-06-12
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-06-19
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-06-26
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-07-03
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-07-10
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
2018-07-17
11:30 - 13:00 wöchentlich
10.50 Bauingenieure, Kleiner Hörsaal 10.50 Kollegiengebäude Bauingenieure II
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Lecturer:
Prof. Dr. Hansjörg Fromm
Dr.-Ing. Niklas Kühl
Prof.Dr. Thomas Setzer - SWS: 2
- Lv-No.: 2595501
Prerequisites | Recommendations: The lecture is addresed to students with interests and basic knowledge in the topics of Operations Research, decritptive and inductive statistics. |
Bibliography |
Online Sources:
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:
Tutorials |
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). By successful completion of the exercises (according to §4(2), 3 of the examination regulation) a bonus can be obtained. If the grade of the written exam is at least 4.0 and at most 1.3, the bonus will improve it by one grade level (i.e. by 0.3 or 0.4). The bonus only applies to the first and second exam of the semester in which it was obtained. |