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Data Analytics

Today{\uc1\u8217*}s business world is characterized by an unprecedented growth of data, by 2020 we will experience a 300-fold increase from 2005. This data comes in a broad variety of forms: 420 million wearable health monitors are currently in use, more than 4 billion hours of video are watched on YouTube each month and 30 billion pieces of content are shared on Facebook every month. A lot of the data is analyzed in real time: modern cars have about 100 sensors and the NYSE captures 1 TB of trade information during each trading session. However, 1 in 3 business leaders don{\uc1\u8217*}t trust the information they use to make decisions and about 27% of respondents in one survey were unsure of how much of their data was inaccurate. 4.4 million IT jobs have been created globally to support big data. AlphaGo has recently beaten the reigning (human) Go champion.
Course code
CORE801
Course type
Doctoral Program Lecture
Weekly Hours
2,0
ECTS
3
Term
FS 2020
Language
Englisch
Lecturers
Prof. Dr. Stefan Spinler
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.

Part 01: Supervised learning

The following methods will be introduced and implemented in R with applications to real data:

  • Linear regression
  • Penalized regression
  • Logistic regression
  • CART
  • Random forests
  • Boosting
  • Support vector machines

Part 02: Unsupervised learning

The following methods will be introduced and implemented in R with applications to real data:

  • Principal Component Analysis (PCA)
  • K-means clustering
  • Hierarchical clustering
  • Spectral clustering
  • Google PageRank Algorithm
Date Time
Wednesday, 12.08.2020 09:00 - 17:00
Thursday, 13.08.2020 09:00 - 17:00
Friday, 14.08.2020 09:00 - 17:00
Foundational knowledge in R

Overview of modern machine learning methods

Limits of machine learning and artificial intelligence

The following book is a good starting point: T. Hastie, R. Tibshirani, J. Friedman: The elements of statistical learning. Springer, 2009.
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