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Digital Marketing & Text Analytics

MGT001376Specialization in Management3 ECTSEnglishwinter semesterProfessur für Digital Marketing (Prof. Hartmann)
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will learn how text analytics is used for data-driven decisions in digital marketing. In the module you will cover methods from lexica to machine learning to deep learning, applications such as sentiment analysis, and ethical and practical challenges. At the end you will be able to analyze unstructured text data with R and/or Python and derive relevant, actionable insights.

What you will be able to do

  • Understand text analytics for data-driven decision making
  • Apply various text analytics methods in R and/or Python
  • Evaluate suitable methods depending on the application context
  • Develop an end-to-end solution from unstructured data to structured insights

What the module consists of

  • SeminarIntroductory lecture, discussions, interactive content, practical examples and code examples
  • Einzelarbeit (Case Study)Application of the learned methods in an individual case study

Teaching method

  • Vorlesung / SeminarIntroduction to applied text analysis and imparting theory
  • Diskussionen und interaktive MaterialienDeepening through exchange and practice-oriented development
  • Codebeispiele in R und PythonPractical implementation and skill training
  • Remote und Präsenz-CoachingSupport in implementing the case study and learning

Dates

SeminarDigital Marketing & Text Analytics (MGT001376, englisch) (Limited places)5 groups to choose from

  • ATue09:30–16:002418, Seminarraum/Bibliothek (0504.02.418)once on 20.10.
  • BThu13:00–17:00Online: Videokonferenzonce on 05.11.
  • CWed09:30–17:300544, Seminarraum (0505.EG.544)once on 11.11.
  • DWed14:30–15:00Online: Videokonferenzonce on 14.10.
  • EMon09:30–16:002418, Seminarraum/Bibliothek (0504.02.418)once on 19.10.

From the current semester, not binding. You attend one of several groups; the timetable automatically suggests the one with the fewest clashes.

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Official page in TUMonline · Details are not binding.