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The Analytics' Edge-M-

In the big data landscape, it becomes imperative to understand what tools are available to gather data, to subsequently aggregate data into information and how to use this information to make better decisions. To this end, we discuss approaches to predictive analytics, which refers to the ability of discerning patterns from past and current data to prediction of future events. Examples include healthcare applications such as heart attack pattern detection and financial applications such as fraud detection in regards to credit card usage. We then turn to prescriptive analytics which involves optimization of resources based on existing data and recognized patterns. Applications include revenue management at airlines and hotels or online dating sites where regression and optimization is supposed to figure out the perfect match. In a third step, we will discuss how analytics transforms existing business models and enables new breeds of business models with examples such as auto insurance tariffs based on in-situ driving data. The analyses will be carried out in R and Python.
Kurs ID
SCM607
Art des Kurses
MSc Kurs
Wochenstunden
2,5
ECTS
5
Semester
HS 2021
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Arnd Huchzermeier
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Course Description:

In the big data landscape, it becomes imperative to understand what tools are available to gather data, to subsequently aggregate data into information and how to use this information to make better decisions. To this end, we discuss approaches to predictive analytics, which refers to the ability of discerning patterns from past and current data to prediction of future events. In particular, we focus on algorithms to tackle classification and regression problems as well as unsupervised learning techniques such as clustering and dimensionality reduction. Examples include healthcare applications such as heart attack pattern detection and financial applications such as fraud detection in regards to credit card usage. We then turn to prescriptive analytics which involves optimization of resources based on existing data and recognized patterns. Applications include revenue management at airlines and hotels or online dating sites where regression and optimization is supposed to figure out the perfect match. The analyses will be carried out in R.

Descriptive analytics:

Visualization of data patterns; predictive analytics: regression techniques, clustering methods, tree-based regression, random forests and support vector machines; prescriptive analytics: linear and integer programming

Date Time
Friday, 03.09.2021 08:00 - 11:15
Tuesday, 07.09.2021 11:30 - 15:15
Monday, 13.09.2021 11:30 - 15:15
Tuesday, 21.09.2021 08:00 - 11:15
Friday, 01.10.2021 15:30 - 17:00
Tuesday, 05.10.2021 11:30 - 15:15
Monday, 11.10.2021 15:30 - 18:45
Wednesday, 13.10.2021 08:00 - 11:15
Familiarity with statistical learning techniques, modelling capabilities in R
James, G., D. Witten, T. Hastie, R. Tibshirani: An introduction to statistical learning. Springer, 2014.
Case studies related to real data, programming assignments in R
Group-based assignments and final project
150
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