Module Handbook

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Course WIW-BWL-DS-K-1

Basics of Data Science (2V+1U, 6.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K 6.0 CP
2 V Lecture 30 h 60 h
1 U Exercise class/tutorial (in small groups) 15 h 75 h
(2V+1U) 6.0 CP 45 h 135 h

Basedata

SWS 2V+1U
CP, Effort 6.0 CP = 180 h
Position of the semester 1 Sem. in SuSe
Level [1] Bachelor (General)
Language [EN] English
Lecturers
Area of study [WIW-WIN] Business Information Systems and Operations Research
Livecycle-State [NORM] Active

Contents

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

Exercises:

  • 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

Requirements for attendance (informal)

None

Requirements for attendance (formal)

None

References to Course [WIW-BWL-DS-K-1]

Module Name Context
[WIW-BWL-DS-M-1] Basics of Data Science P: Obligatory 2V+1U, 6.0 LP