My entry for HSL "Data Engineer" position.
Jupyter, Python 3.6.13, and data inferring with limited knowledge.
Used libraries:
- jupyter (1.0.0)
- matplotlib (3.3.4)
- numpy (1.19.2)
- pandas_ods_reader (0.1.2)
- pandas (1.1.5)
- utm (0.7.0)
Usage would be to place the .ipynb in a folder and open it in a Jupyter notebook instance. However, due to not including the data file, the .ipynb cannot be ran.
However, you can open the .html in a browser.
4 days
About 3500 rows and 11 columns.
Date/time, location (coordinate system not given), and technical data about two vessels.
There are data patterns that have not been given empirical explanation by the data gatherer.
Inferring is done in a "missing data" style context due to missing knowledge about phenomena that may cause the patterns that are observed and how observing the patterns should be used to inform how to optimize the usage of vessels. One must therefore make assumptions by using knowledge from literature.
It's possible to also see patterns that could be improved with machine learning, but due to missing empirical explanation there is no real meaning to doing modeling, because one cannot know how to infer the data, since one lacks access to the actual empirical knowledge.
Best practice (w.r.t. to literature) and carefulness of not saying things that are not known about are still used to infer "possible phenomena" in the vessel trips. I discover for example that:
- vessels might have been unoptimally loaded on some trips
- speed has not been constant, even when it should be a "best practice" according to literature
- messy data might have been gathered without paying too much attention to DOE (https://en.wikipedia.org/wiki/Design_of_experiments)
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