Description
To get familiar with the most important challenges, tasks, tools and techniques
related to data science in research and industry. The subject focuses on
solving realistic problems, to directly apply the basic concepts and results.
There is a strong intention to co-operate also with industrial partners within R&D
projects. Challenges is Data Science (Research and Industry). Case Study,
Image Recognition. Case Study, Processing Big Data. Recommender Systems.
Digital Advertisements, Internet Search. Gaming. Fraud and Risk Detection.
Route Planning, Delivery, Optimization. Robotics. Autonomous Driving. Project
Work. Industrial Projects.

Competences
- his/her knowledge covers the main concepts of data science
- he/she can use his/her knowledge about data science in practice.
- he/she accepts and adapts to the requirement of the ethical standards, work, and organizational cultures
- he/she is responsible for his/her professional activates in a group or alone

Compulsory readings
- W. McKinney: Python for Data Analysis (1 ed.). O'Reilly Media, Inc. 2012.
- Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.
- D. Conway, J.M. White: Machine Learning for Hackers, O'Reilly Media, Inc., 2012.
- I. Goodfellow, Y. Bengio, A. Courville: Deep Learning, MIT Press, 2016.

Recommended readings
- W. McKinney: Python for Data Analysis (1 ed.). O'Reilly Media, Inc. 2012.
- Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.
- D. Conway, J.M. White: Machine Learning for Hackers, O'Reilly Media, Inc., 2012.
- I. Goodfellow, Y. Bengio, A. Courville: Deep Learning, MIT Press, 2016.