Module Handbook

  • Dynamischer Default-Fachbereich geändert auf WIW

Module WIW-BW-DS-M-2

Data Science (M, 6.0 LP)

Module Identification

Module Number Module Name CP (Effort)
WIW-BW-DS-M-2 Data Science 6.0 CP (180 h)


CP, Effort 6.0 CP = 180 h
Position of the semester 1 Sem. in SuSe
Level [2] Bachelor (Fundamentals)
Language [EN] English
Module Manager
Area of study [WIW-WIN] Business Information Systems and Operations Research
Reference course of study [WIW-82.?-SG#2021] B.Sc. Business Studies (2021) [2021]
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
Data Science
P 45 h 135 h - - PL1 6.0 WiSe
  • About [WIW-BW-DS-K-2]: Title: "Data Science"; Presence-Time: 45 h; Self-Study: 135 h

Examination achievement PL1

  • Form of examination: written exam (Klausur) (90 Min.)
  • Examination Frequency: each semester

Evaluation of grades

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


Part 0: Data -> Information -> Knowledge: Concepts in Science and Engineering

Part 1: Organizing the “Data Lake” (from data mining to data fishing):

  • Relational Database Models: Modeling and Querying Structured Attributes of Objects
  • Graph- and Network-based Data Models: Modeling and Querying Structured Relations of Objects
  • Information Retrieval: Document Mining and Querying of ill-structured Data
  • Streaming Data and High Frequency Distributed Sensor Data
  • > The Semantic Web: Ontologist’s Dream (or nightmare?) of how to integrate evolving heterogeneous data lakes

Part 2: Stochastic Models on structured attribute data:

  • From Linear to non-linear Regression models
  • Support Vector Machines
  • Deep? Neural Network Models
  • Learning from Data Streams: Training and Updating Deterministic and Stochastic Models
  • Reinforcement Learning

Part 3: Getting Ready for the Digital Twin

  • Interfacing learning components / partial models
  • Stochastic Models on graphs / networks based data


  • Analyzing Data (from SPSS to R to Python)
  • Visualizing Data with Python
  • Machine Learning with Python
  • Building and Training Deep Neural Network Models with Tensorflow and PyTorch

Competencies / intended learning achievements

Ability to extract and integrate information from a variety of heterogeneous sources
  • Well structured relational or object-oriented databases
  • Semi-structured documents and data-streams
  • Graph- / network related data

Ability to automate this extraction via adequate tools and/or languages

Ability to handle stochasticity in this extraction process

Ability to choose and train adequate forecasting and/or economic decision models on this information

Requirements for attendance of the module (informal)

Competencies from

Introduction to Statistics I and II

are required to follow this Module

Requirements for attendance of the module (formal)


References to Module / Module Number [WIW-BW-DS-M-2]