Module Handbook

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Module EIT-EMS-653-M-6

Enterprise Data Science (M, 3.0 LP)

Module Identification

Module Number Module Name CP (Effort)
EIT-EMS-653-M-6 Enterprise Data Science 3.0 CP (90 h)

Basedata

CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Lecturers
Area of study [EIT-EMS] Microelectronic Systems Design
Reference course of study [EIT-88.?-SG#2021] M.Sc. Automation and Control (A&C) [2021]
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V EIT-EMS-653-K-6
Enterprise Data Science
P 28 h 62 h
PROJ-Schein
ja PL1 3.0 SuSe
  • About [EIT-EMS-653-K-6]: Title: "Enterprise Data Science"; Presence-Time: 28 h; Self-Study: 62 h
  • About [EIT-EMS-653-K-6]: The study achievement [PROJ-Schein] proof of successful completion of the project(s) must be obtained. It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (45-60 Min.)
  • Examination Frequency: each semester

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

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.

Competencies / intended learning achievements

  • Systematic approach to link a business problem to a data driven application
  • Implementation and validation techniques of machine learning models
  • Improved Python programming skills
  • Understanding of enterprise needs and skillsets for data science

Requirements for attendance (informal)

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

Requirements for attendance (formal)

None

References to Module / Module Number [EIT-EMS-653-M-6]

Course of Study Section Choice/Obligation
[EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010] Elective Subjects [W] Elective Module
[EIT-88.A44-SG#2018] M.Sc. Media and Communication Technology [2018] Technical Elective Subjects [W] Elective Module
[EIT-88.?-SG#2021] M.Sc. Electrical and Computer Engineering [2021] Technical Elective Modules [W] Elective Module
[EIT-88.?-SG#2021] M.Sc. Media and Communication Technology [2021] Technical Elective Modules [W] Elective Module
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] Elective Subjects [W] Elective Module
[EIT-88.?-SG#2021] M.Sc. Automation and Control (A&C) [2021] Major "Connected Automation Systems" (CAS) [P] Compulsory
[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] Elective Subjects [W] Elective Module
Module-Pool Name
[GS-CVT-EE-E-MPOOL-6] Catalog Electives Electrical and Computer Engineering