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Data Science for Effective Operations

This workshop has been held at differnt occasions with slightly different content. Please look into the corresponding sub-folders to access the persented material.

Bootstrap

If you have access to a machine with docker installed, you can boostrap an interactive working environment with a single command:

$ ./docker.sh --run
[...]
#
# Data Science 4 Effective Operations
#
# starting jupyter notebook&lab ...
done
#
# Notebook:
# * local url: http://0.0.0.0:9999/?token=F2AlHtJBvHIqoLFEVfbMnUVFkcpFlJuZ
# * public url: http://11.22.33.192:9999/?token=F2AlHtJBvHIqoLFEVfbMnUVFkcpFlJuZ
#
# Lab:
# * local url: http://0.0.0.0:9998/?token=F2AlHtJBvHIqoLFEVfbMnUVFkcpFlJuZ
# * public url: http://11.22.33.192:9998/?token=F2AlHtJBvHIqoLFEVfbMnUVFkcpFlJuZ

Datasets

Also check out my writings on Statistics for Enginners

Further Reading and References

Name Description Size
API_latencies.csv 4 nodes. Measured once per minute 8436 records/371kb
WebLatency.csv 5 nodes. Measured once per minute 10498 records/455kb
api_latency_histogram:[email protected] 1 node. Distribution histogram. Once per minute 2020 records/900kb
api_latency_samples:[email protected] 1 node. Raw samples. Once per minute. 2020 records/6.9Mb
cluster_cpu_idle:[email protected] 10 nodes 10080 records/1.0Mb
http_durations:[email protected] 15 nodes. Measured once per minute 10080 records/1.2Mb
sycalls:[email protected] 1 node. 152 syscalls 1441 records/2.2Mb
web_request_rate:[email protected] Request rate on a single web server 8060 records/153kb

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