2018/January: Difference between revisions
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* Data engineering scenarios | * Data engineering scenarios | ||
** Basic workflow, dealing with large amounts of data and doing machine learning on it | ** Basic workflow, dealing with large amounts of data and doing machine learning on it | ||
** See also: [[Google Cloud/Review]] | ** See also: [[Google Cloud/Review]] | ||
* Follow a single scenario, continuously... | |||
* One reactor, and the many aspects of that one reactor that can be studied... | |||
* Memo style: overarching goal, gather data, analyze trends, create model, optimize | |||
* Data engineering: gathering data process, analysis process, modeling process | |||
[[2018/January/Data Engineering]] | [[2018/January/Data Engineering]] | ||
Revision as of 10:42, 7 January 2018
Task list for January:
- Shore up notes:
- Experiment design
- Linear models
- Rubiks cube
2018/January/Notes Repositories
- Data engineering scenarios
- Basic workflow, dealing with large amounts of data and doing machine learning on it
- See also: Google Cloud/Review
- Follow a single scenario, continuously...
- One reactor, and the many aspects of that one reactor that can be studied...
- Memo style: overarching goal, gather data, analyze trends, create model, optimize
- Data engineering: gathering data process, analysis process, modeling process
- Rubiks Cube:
- Calculating the order of a permutation (see https://math.stackexchange.com/questions/332146/efficient-method-to-determine-the-order-of-a-permutation-in-s-n)
- Blog posts:
- Knuth permutation generation
- Google Data Engineering Certification blog post and notes highlights
- Concepts: data engineering vs. data science
- Elevator pitch: what is data engineering
- Data engineering scenario rollouts
- Genealogy
- Plan two chapters, possibly more
- Historical planning
- Index cards/Microscope style?
- Bot