Background

Data holds the answers to all manner of questions and analysis methods can extract those answers - linking the two is “data preparation”. Anyone interested in working with data will likely need to know at least some of the principles outlined.

Learning contents

An introduction to the preparation of data for analysis, beginning with the initial production or acquisition through to an analysis ready dataset. The specific case of image data will then be discussed in more detail using examples from satellite-based Earth Observation.

Learning objectives

• An understanding of how to approach a new dataset and the typical steps that will be necessary in a variety of contexts.
• An insight into the preparation of image data and practical experience performing such tasks using Python.

Prior knowledge

• Familiarity with the Python programming language
• Some specific extension packages will be used but prior knowledge of these will not be assumed

Technical requirements

• Own laptop

• Connection to Wifi via eduroam or equivalent access (see: https://www.uni-bremen.de/en/zfn/wifi/overview-wifi)

• Access to Python
  • Please make sure, you have a functional Python environment running before the workshop
  • Participants with access to the Uni Bremen ZfN Services can use Python via JupyterHub (https://jupyter.uni-bremen.de/).
• If you do not have any programming environment (e.g. Python, R, Rstudio, etc.) installed on your computer, you can use web-based options: For example, participants with access to the Uni Bremen ZfN Services can use the Uni Bremen JupyterHub. Alternatively, participants can use the NFDI JupyterHub.

The Lecturer(s)

Dr. Karl Kortum Karl Kortum is a Post-Doctoral Researcher at the German Aerospace Center (DLR).	Dr. Björn Tings Björn Tings is a Research Associate in the team of Synthetic Aperture Radar (SAR) oceanography at the Remote Sensing Technology Institute of German Aerospace Center (DLR) in Bremen.

Find out more about the Data Train lecturers on our website.

This event is organized by