Enterprise Data Science (2V, 3.0 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|2||V||Lecture with integrated exercises||3.0 CP||28 h||62 h|
|(2V)||3.0 CP||28 h||62 h|
|CP, Effort||3.0 CP = 90 h|
|Position of the semester||1 Sem. in SuSe|
|Level|| Master (General)|
|Area of study||[EIT-EMS] Microelectronic Systems Design|
In this lecture, we focus on predictive modeling (machine learning) via Python and how to solve the related business problem.
Programming skills are mandatory for a data scientist; thus, programming exercises have to be done by the students. Predictive models forecast the future given historical data sets. For this machine learning might become appropriate. We will use the scikit-learn Python library and TensorFlow to demonstrate pitfalls and best practices to solve a problem. Also, the link to advanced business intelligence (BI) tools and in-memory databases is presented.
Note that full coverage of these topics is not possible, and only a selective view is presented. Thus, only basic concepts are sketched by using demos, SQL, Python, and business process modeling and notation (BPMN) representation.
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron (GITHUB resources)
- Additional recommendations given within the lecture
Requirements for attendance (informal)
- Basic programming experience in Python (pandas, scikit-learn)
- Basic statistical know-how
Requirements for attendance (formal)
References to Course [EIT-EMS-653-K-6]
|[EIT-EMS-653-M-6]||Enterprise Data Science||P: Obligatory||2V, 3.0 LP|