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Data engineering for Mobility Data Science (with Python and DVC)

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Data engineering for Mobility Data Science (with Python and DVC)
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17
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production PlaceWageningen

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Abstract
This session introduces MovingPandas and DVC for Mobility Data Science. MovingPandas is a Python library for the analysis and visualization of movement data. It is built on top of GeoPandas and provides functions to analyze, manipulate and plot trajectories. To get a better idea of the type of analytics that MovingPandas supports, visit: https://movingpandas.org/examples DVC is a data version control (and machine learning experiment tracking) library. It follows a similar logic to source code version control systems (such as Git) and is typically used together with Git to keep track of data and experiments while Git keeps track of the source code. In this session, we will use DVC to keep track of our movement data analytics workflow. Participants are expected to come prepared with a working MovingPandas & DVC Python environment. Basic previous experience with (Geo)Pandas and version control systems (i.e. how pull, commit, push works in Git) is expected.