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Python for Engineering Data Analysis - From Machine Learning to Visualization

EI04024Specialization in Technology5 ECTSEnglishWintersemester/SommersemesterDepartment Electrical Engineering
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will learn Python as a tool for engineering data analysis: capturing data, cleaning, visualizing, and creating simple models. In the end you will be able to analyze typical lab and research data with Python, generate 2D/3D representations, and assess the possibilities and limitations of simple statistical and ML methods.

What you will be able to do

  • solve algorithmic problems with Python
  • apply fundamental tasks of data analysis and statistics
  • use Python tools for 2D and 3D visualization
  • understand different methods for data modeling (e.g. linear regression, neural networks)
  • recognize the possibilities and limitations of the methods

What the module consists of

  • Tutorienweekly exercises for practical application and supervision
  • Abschlussprojektapplication of a larger part of the methods to a self-chosen research task

Teaching method

  • Inverted Classroomtheoretical foundations are prepared and applied practically in the tutorial
  • electronic materials (presentations, video recordings)support for self-study and preparation for exercises
  • exercises and homeworkpractice the practical application
  • lecturer supervisionclarify questions and provide help with implementation

Dates

Lab coursePython for Engineering Data Analysis - From Machine Learning to Visualization5 groups to choose from

  • ATue10:00–12:0000.5901.049, Computerpool (5901.EG.049)
    13× · 20.10.–02.02.
    • 20.10.
    • 27.10.
    • 03.11.
    • 17.11.
    • 24.11.
    • 01.12.
    • 08.12.
    • 15.12.
    • 22.12.
    • 12.01.
    • 19.01.
    • 26.01.
    • 02.02.
  • BTue14:00–16:0000.5901.049, Computerpool (5901.EG.049)
    14× · 20.10.–02.02.
    • 20.10.
    • 27.10.
    • 03.11.
    • 10.11.
    • 17.11.
    • 24.11.
    • 01.12.
    • 08.12.
    • 15.12.
    • 22.12.
    • 12.01.
    • 19.01.
    • 26.01.
    • 02.02.
  • CThu10:00–12:0000.5901.049, Computerpool (5901.EG.049)
    13× · 22.10.–04.02.
    • 22.10.
    • 29.10.
    • 05.11.
    • 12.11.
    • 19.11.
    • 26.11.
    • 10.12.
    • 17.12.
    • 07.01.
    • 14.01.
    • 21.01.
    • 28.01.
    • 04.02.
  • DWed10:00–12:0000.5901.049, Computerpool (5901.EG.049)
    13× · 21.10.–03.02.
    • 21.10.
    • 04.11.
    • 11.11.
    • 18.11.
    • 25.11.
    • 02.12.
    • 09.12.
    • 16.12.
    • 23.12.
    • 13.01.
    • 20.01.
    • 27.01.
    • 03.02.
  • EWed14:00–16:0000.5901.049, Computerpool (5901.EG.049)
    14× · 21.10.–03.02.
    • 21.10.
    • 28.10.
    • 04.11.
    • 11.11.
    • 18.11.
    • 25.11.
    • 02.12.
    • 09.12.
    • 16.12.
    • 23.12.
    • 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.