Agile in analytics, time series forecasting, visualizing dataframe transforms
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I enjoy Taylor Browlow's writing on data science in industry. How to make agile actually work for analytics, discusses what we all probably struggle with: Agile for data science teams. I've leveraged Agile (mostly Scrum) on my data science and data engineering teams for many years now. It's not a perfect fit, but the benefits have outweighed the costs.
The article reimagines the four core Agile tenets
- Individuals and interactions over processes and tools
- Working software over comprehensive documentation
- Customer collaboration over contract negotiation
- Responding to change over following a plan
in an analytics context
- Decisions over dashboards
- Functional analysis over perfect outputs
- Sharing data over gatekeeping data
- Individuals and interactions over processes and tools
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This advice from James Clear: revise, revise, revise.
"The difference between good and great is often an extra round of revision.
The person who looks things over a second time will appear smarter or more talented, but actually is just polishing things a bit more.
Take the time to get it right. Revise it one extra time."
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There is a sizable market (and not enough expertise) in time series forecasting, so I'm always interested in what's developing in that space. ETNA is a new framework from the Tinkoff.ru Artificial Intelligence Center that I'm looking at.
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VS Code continues to improve python support. They just released a new Python Extension.
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A great read from meta on how they use AI to animate children's drawings.
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Very straightforward guide to setting up your new MBPro with Conda and TensorFlow.
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This came from a best-of list, and it's a wonderful list of 10 skills to develop with applications to both engineering and life. The note at the end about looking for systems struck me as a hard skill to develop, but impactful if you can learn it.
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I don't know if it's actually useful, but this visualization of pandas dataframe transformations sure looks cool.
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Overall I disagree with this author's premise -- namely that Web3 is Bullshit, but there are some good points, specifically that there are real issues regarding data ownership. It is a good, short read disputing all the Web3 hype.
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Finally, this has nothing to do with data science or data engineering, but it was the best thing I read all week so I felt the need to share it. A plus-sized Jewish lady redneck died in El Paso on Saturday.