Master Data Science: Unterschied zwischen den Versionen

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There could be 2-4 core areas that the study is covering mostly; this will typically cover different structures in the study plan.
There could be 2-4 core areas that the study is covering mostly; this will typically cover different structures in the study plan.


Here is an example: Track "Machine Learning, Statistics, Data Visualization".
Here is an example:
 
== Track "Machine Learning, Statistics, Data Visualization" ==
The classes taken from Extension Modules and free electives were meant to cover these areas as well as possible.
The classes taken from Extension Modules and free electives were meant to cover these areas as well as possible.
The overarching theme is to analyse data with statistical methods, build (machine learning) models, and visualize data in exploratory phases, as well as being able to present results using visualizations tailord to a non-technical audience.
The overarching theme is to analyse data with statistical methods, build (machine learning) models, and visualize data in exploratory phases, as well as being able to present results using visualizations tailored to a non-technical audience.






[[Kategorie:Studienplanung]]
[[Kategorie:Studienplanung]]

Version vom 9. Dezember 2019, 15:01 Uhr

Work in progress.

The goal of ths page is to present a few possible "Tracks" through the Master Programme Data Science. A Track here is a certain collection of classes taken in order to fulfill the study plan. The idea is to document actually taken classes, not all possible choices. There could be 2-4 core areas that the study is covering mostly; this will typically cover different structures in the study plan.

Here is an example:

Track "Machine Learning, Statistics, Data Visualization"

The classes taken from Extension Modules and free electives were meant to cover these areas as well as possible. The overarching theme is to analyse data with statistical methods, build (machine learning) models, and visualize data in exploratory phases, as well as being able to present results using visualizations tailored to a non-technical audience.