Predictive Analytics

Course contents

The course 'Predictive Analytics' follows the course 'Business Intelligence'. As the data path from sensor over acquisition to processing has already been examined, now the focus is on deriving statements about the future from the processed data. After an introduction to the topic, the first part deals with classical data science procedures based on statistical methods. In the second part, modern methods - in particular machine learning - will be demonstrated and developed. The entire course is accompanied by practical examples in Matlab.

The content of the course at a glance:
- Introduction to the topic
- Classic data science methods
o Potential future scenarios
o Application of statistical methods: Regression, Clustering, Factors and time series analyses
- Machine learning
o Supervised learning: Support Vector Maschines, Neuronal Networks, Deep Learning, Random Forests, Bayesian Networks
o Unsupervised Learning: k-Means, Deep Learning
o Enriched learning

Department

Master's Program Industrial Engineering & Management