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The Analytics' Edge

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.
Course code
SCM607
Course type
MSc Course
Weekly Hours
2,5
ECTS
5
Term
FS 2021
Language
Englisch
Lecturers
Prof. Dr. Arnd Huchzermeier
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the 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 descriptive and predictive analytics.

Descriptive analytics refers to the preparation, exploration, and visualization of historical data in order to identify patterns or meaning. It is a prerequisite for predictive analytics, which refers to the ability of discerning patterns from past and current data to prediction of future events. In this course, 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. The analyses will be carried out in R. R is an open source programming language and software environment for statistical computing and graphics.

Keywords:

Descriptive Analytics: data wrangling, visualization of data patterns;

Predictive Analytics: regression techniques, tree‑based regression, extreme gradient boosting, neural networks, clustering methods, and dimensionality reduction

Date Time
Monday, 08.03.2021 17:15 - 20:30
Monday, 15.03.2021 11:30 - 13:00
Tuesday, 16.03.2021 11:30 - 15:15
Monday, 22.03.2021 08:00 - 11:15
Tuesday, 30.03.2021 15:30 - 18:45
Tuesday, 06.04.2021 17:15 - 20:30
Friday, 09.04.2021 17:15 - 18:45
Friday, 16.04.2021 17:15 - 18:45
Monday, 19.04.2021 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, 2017; Wickham, H., & Grolemund, G. (2016). R for data science. O’Reilly 2017
Individual assignment(s) and group project
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