Data science for Effective Operations
Abstract
Gathering telemetry data is key to operating reliable distributed systems at scale.
Data Science is the art of extracting information from large amounts of data.
In this workshop, we will cover a range of analysis methods, that are know to
work well for IT operations data. You will deepen your understanding of the
inner workings of these methods, and get to apply them with a modern
data science environment based on the Python/Jupyter toolchain.
The course will be split 50 / 50 into lectures and labs. In the lectures I'll explain
mathematical aspects on the whiteboard, walk through some Jupyter notebooks
and demo some live examples from our own monitoring. In the labs, you will have
get your hands dirty analysing provided datasets with Python/Jupyter yourself.
Topics include
- Visualising Data
- Mean Values
- Deviation Measures
- Outliers
- Percentiles
- Histograms
- Regressions
Learning Goals
- Get a good overview of how you extract value from operations data
- Deeper understanding of aggregation and analysis methods, commonly
found in monitoring tools (averages, percentiles, regressions), and their pitfalls
- Get started with the Python/Jupyter data science toolchain
Intended Audience
- Developers with mathematical Interest
- Operations Engineers with mathematical Interest
- People interested in learning data science by playing with operations data
Expected Prior Knowledge
- Read and write Python code
- Interest in Mathematics. You will only need basic arithmetic for the exercises.
But you will have to endure more complex calculations presented on the whiteboard.
Warning
- This course contains mathematics