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Python and Advanced Data Science

MGTHN0094Electives in Management6 ECTSEnglishsummer semesterLehrstuhl für Digital Marketing (Prof. Meißner)
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will learn how Python is used for Data Science and advanced machine learning techniques. By the end you will be able to prepare, analyze and visualize data with Python, as well as practically implement and evaluate ML models (including deep learning and NLP fundamentals).

What you will be able to do

  • Apply fundamental and advanced Python data structures
  • Process data from files and relational databases with SQL
  • Clean, transform and exploratory analyze data (EDA)
  • Visualize data and present insights in an understandable way
  • Apply supervised and unsupervised machine learning
  • Understand and deploy ensemble methods and regularization
  • Know the basics of AutoML, Deep Learning (NN, RNN, CNN) and NLP
  • Evaluate, optimize and practically implement models in projects

Teaching method

  • VorlesungErläutert Konzepte und gibt Übersicht über Methoden
  • ÜbungenErmöglichen praktische Anwendung und Übung von Python-Code
  • GruppenarbeitFördert gemeinsames Arbeiten an Projekten und Problemstellungen
  • PräsentationenUnterstützen Reflexion und Vermittlung der Ergebnisse

Dates

LecturePython and Advanced Data Science (MGTHN0094)2 groups to choose from

  • AWed10:15–11:45L.0.13, Seminarraum (1902.EG.013)
    13× · 14.10.–03.02.
    • 14.10.
    • 21.10.
    • 04.11.
    • 11.11.
    • 18.11.
    • 25.11.
    • 02.12.
    • 09.12.
    • 16.12.
    • 13.01.
    • 20.01.
    • 27.01.
    • 03.02.
  • BWed14:15–15:45L.1.10, Seminarraum (1902.01.110)
    13× · 14.10.–03.02.
    • 14.10.
    • 21.10.
    • 04.11.
    • 11.11.
    • 18.11.
    • 25.11.
    • 02.12.
    • 09.12.
    • 16.12.
    • 13.01.
    • 20.01.
    • 27.01.
    • 03.02.

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.