de Big Data and Artificial Intelligence

en Big Data and Artificial Intelligence

Angaben gemäß SPO
Zugehörigkeit zu Curriculum
Modulverantwortung
Anderl, Eva (Prof. Dr.)
Prüfungsformen
ModA
Lehr- und Lernformen
SU | 4 SWS
Lehrangebote SoSe 2025
SU | 4 SWS
wird nicht angeboten
Prüfungsangebote SoSe 2025
Erstprüfer:in
Anderl, Eva (Prof. Dr.)
Zweitprüfer:in
Brehm, Lars (Prof. Dr.)
Prüfungsform
ModA
Details zur Prüfung
will be provided in class
Hilfsmittel
n.a.
Voraussetzungen und Verwendbarkeit
Voraussetzungen für die Teilnahme
Verwendbarkeit
Inhalte und Qualifikationsziele
verabschiedet am 14.04.2025
Intended Learning Outcomes 

This course is about extracting useful knowledge from (big) data. It covers the fundamental principles or concepts that
underlie data science and artificial intelligence with a main focus on the selection and application of techniques in Python and the interpretation of results in a business context.
Upon completion of the class, students should be able to
  • Explain key terms and the standard data analytics process
  • Discuss prerequisites and potential challenges of big data analysis and AI
  • Select adequate data analysis methods for a given problem
  • Apply selected methods and tools
  • Derive the business implications of a big data/AI project

Contents
  • Importance of data analysis in the field of digital business
  • Basic concepts and techniques of applied data science in Python
    • Supervised vs. unsupervised learning
    • Regression
    • Classification
    • Similarity and clustering
    • Causality vs. correlation
    • Avoiding overfitting
    • Analysis of model performance
  • Data-analytic thinking

Applied methods in Economics and Business administration
  • Analysis models and methods:
    • Operation research methods
    • Marketing research methods
  • Quantitative empirical methods:
    • Data analysis methods, e.g. classification, regression, clustering
    • Artificial Intelligence

Teaching and Learning Styles
  • Seminar-teaching
  • Project work
  • Group work

Literature
  • O'Neil, C. and Schutt, R. (2014), Doing Data Science: Straight Talk from the Frontline, O'Reilly, Sebastopol.
  • Provost, F. and Fawcett, T. (2013), Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking, O'Reilly, Sebastopol.
Further indicative reading will also be provided in module materials.
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