Online or onsite, instructor-led live Anaconda training courses demonstrate through interactive hands-on practice how to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.
Anaconda training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Munich onsite live Anaconda trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Anaconda, previously Continuum Analytics, is also known as Anaconda Distribution or Anaconda Individual Edition.
NobleProg -- Your Local Training Provider
Munich
NobleProg München , Landshuter Allee 8-10, München, germany, 80637
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks t...
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks to good transport connections, our spacious training rooms are easy to reach and offer optimal training conditions.
Directions
The NobleProg training facilities are conveniently located not far from the A95 and A96 motorways. The underground station Rotkreuzplatz and the S-Bahn station Donnersbergerbrücke are within walking distance.
Parking spaces
There are numerous free parking spaces around our training rooms.
Local infrastructure
Not far from Landshuter Allee you will find various restaurants and in the nearby Arnulfstrasse there are several hotels.
This instructor-led, live training in Munich (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.
By the end of this training, participants will be able to:
Install and configure Anaconda components and libraries.
Understand the core concepts, features, and benefits of Anaconda.
Manage packages, environments, and channels using Anaconda Navigator.
Use Conda, R, and Python packages for data science and machine learning.
Get to know some practical use cases and techniques for managing multiple data environments.
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