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The Analytics' Edge - (E-F-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 2023
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 and subsequently aggregate data into information and how to use this information to make better decisions. In this course, we start with descriptive analytics to summarize and visualize various data types for effective communication. We then discuss different methods for predictive analytics, i.e., the ability to learn patterns from past data to predict future events. In particular, we focus on machine learning algorithms to tackle regression and classification problems as well as unsupervised learning techniques such as clustering and dimensionality reduction. Algorithms covered in this course include penalized regression, tree-based models, gradient boosting, neural networks, PCA, k-means and hierarchical clustering. Practical applications include fraud detection and marketing in the banking industry and revenue management at airlines and hotels. This module is a combination of lectures and coding exercises in R. No previous experience in R/other programming tools is required for this course, as all the basics are covered in the initial sessions.

Date Time
Friday, 01.09.2023 08:00 - 11:15
Friday, 08.09.2023 08:00 - 11:15
Wednesday, 13.09.2023 11:30 - 15:15
Tuesday, 19.09.2023 08:00 - 11:15
Tuesday, 26.09.2023 11:30 - 13:00
Monday, 02.10.2023 08:00 - 11:15
Tuesday, 10.10.2023 08:00 - 11:15
Friday, 13.10.2023 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
Individual assignments and final group project
150
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