Hochschule München

HM Business School (FK 10)

Modulbeschreibung

Stand: WiSe 2023

Name Big Data and Artificial Intelligence
Katalog-Nummer FK 10#PPM#M4.4
Zugehörigkeit zu Curriculum
Master Betriebswirtschaft | M4.4 | 5 Leistungspunkte
Modulverantwortung
Anderl, Eva (Prof. Dr.)
Lehrende
Anderl, Eva (Prof. Dr.)
Weber , Kim Corinna (LB)
Prüfung(en)
Prüfungsform: ModA
Detailangaben: will be provided in class
Hildsmittel: n.a.
Prüfende: Anderl, Eva (Prof. Dr.) , Brehm, Lars (Prof. Dr.)
Lehr- und Lernform(en)
| 4 SWS | SU - 1 Angebot(e)
Arbeitsaufwand
Präsenzzeit: 0 Stunden
Selbststudium, Vor- und Nachbereitung, Prüfungsvorbereitung: 0 Stunden
Voraussetzungen
Verwendbarkeit
Inhalt / Lernziele

English Version
Name Big Data and Artificial Intelligence
Katalog-Nummer FK 10#PPM#M4.4
Zugehörigkeit zu Curriculum
Master Betriebswirtschaft | M4.4 | 5 Leistungspunkte
Modulverantwortung
Anderl, Eva (Prof. Dr.)
Lehrende
Anderl, Eva (Prof. Dr.)
Weber , Kim Corinna (LB)
Prüfung(en)
Prüfungsform: ModA
Detailangaben: will be provided in class
Hildsmittel: n.a.
Prüfende: Anderl, Eva (Prof. Dr.) , Brehm, Lars (Prof. Dr.)
Lehr- und Lernform(en)
| 4 SWS | SU - 1 Angebot(e)
Arbeitsaufwand
Präsenzzeit: 0 Stunden
Selbststudium, Vor- und Nachbereitung, Prüfungsvorbereitung: 0 Stunden
Voraussetzungen
Verwendbarkeit
Inhalt / Lernziele
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.