Hochschule München

HM Business School (FK 10)

Modulbeschreibung

Stand: WiSe 2024

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.)
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.)
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.