Dynamischer Default-Fachbereich geändert auf EIT

Course EIT-EMS-653-K-6

Enterprise Data Science (2V, 3.0 LP)

Course Type

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

Basedata

SWS	2V
CP, Effort	3.0 CP = 90 h
Position of the semester	1 Sem. in SuSe
Level	[6] Master (General)
Language	[EN] English
Lecturers	Kienle, Frank, PD Dr.-Ing. (EXT \| DEPT: EIT)
Area of study	[EIT-EMS] Microelectronic Systems Design
Livecycle-State	[NORM] Active

The lecture addresses students that are interested in the topic of data science applied within large cooperations. Enterprise data science is linking the business perspective to realistic mathematical models and the constraints of an enterprise IT environment. All three topics address the lecture with a focus on applied machine learning and predictive models in Python.

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.

Literature

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

Materials

All documents will be provided in the OLAT and GitHub system.

Requirements for attendance (informal)

Basic programming experience in Python (pandas, scikit-learn)
Basic statistical know-how

Requirements for attendance (formal)

None

References to Course [EIT-EMS-653-K-6]

Module	Name	Context
[EIT-EMS-653-M-6]	Enterprise Data Science	P: Obligatory	2V, 3.0 LP

Module Handbook