Unofficial Google Data Science Blog
unofficialgoogledatascience.com · Company blog · on HN since 2015
Google data scientists write unofficially on data analysis, forecasting, machine learning, and internal tooling.
What HN saysCommenters praise the blog and often cite its post on replacing Sawzall with a Go-based DSL, and its advice on exploring data first.
23HN people shared it
5linked it in comments
26stories submitted
636story points
1 year agolast shared
2015people per year2026
Most-shared posts
- 1Practical advice for analysis of large, complex data sets6 people · 345 points · first shared 2016 · 32 comments on HN
- 2Replacing Sawzall – a case study in domain-specific language migration5 people · 48 points · first shared 2015 · 5 comments on HN
- 3Next generation tools for data science3 people · 20 points · first shared 2016 · discussion on HN
- 4Humans-in-the-loop forecasting: integrating data science and business planning2 people · 62 points · first shared 2019 · 6 comments on HN
- 5How to get a job at Google as a data scientist2 people · 3 points · first shared 2015 · discussion on HN
- 6Causality in machine learning1 people · 127 points · first shared 2017 · 65 comments on HN
- 7Experiment Design and Modeling for Long-term Studies in Ads1 people · 9 points · first shared 2015 · discussion on HN
- 8An Introduction to the Poisson Bootstrap1 people · 3 points · first shared 2019 · discussion on HN
- 9Unofficial Google data science blog1 people · 2 points · first shared 2015 · discussion on HN
- 10Crawling the internet: data science within a large engineering system1 people · 2 points · first shared 2018 · discussion on HN
- 11Compliance bias in mobile experiments1 people · 2 points · first shared 2018 · discussion on HN
- 12On procedural and declarative programming in MapReduce (2015)1 people · 2 points · first shared 2020 · discussion on HN
- 13Why model calibration matters and how to achieve it1 people · 2 points · first shared 2021 · discussion on HN
- 14Unofficial Google Data Science Blog1 people · 2 points · first shared 2016 · discussion on HN
- 15Our quest for robust time series forecasting at scale1 people · 2 points · first shared 2017 · discussion on HN
What HN people wrote when they linked it
mikecb · 2016-11-09
I love this blog. They had a great post describing a privacy preserving query proxy: http://www.unofficialgoogledatascience.com/2015/12/replacing-sawzall-c…
ebikelaw · 2018-08-29
I guess you meant outside of google? Google's logs analysis is often done in "Lingo" which stands for Logs In Go. I assure you that logs analysis is very CPU bound.
http://www.unofficialgoogledatascience.com/2015/12/replacing-sawzall-c…
shoyer · 2017-02-16
Before you try any sort of ML, explore your data [1, 2]. Exploratory data analysis may very well tell you that there is absolutely no point in making a fancy predictive model at all. If a few heuristics get you 90% of the way to an optimal solution, then don't even bother to start on machine learning, unless that last 10% is going to provide significant value.
[1] https://en.wikipedia.org/wiki/Exploratory_data_analysis
[2] From one of my mentors: http://www.unofficialgoogledatascience.com/2016/10/practical-advice-fo…
danmux · 2016-08-26
And this : http://www.unofficialgoogledatascience.com/2015/12/replacing-sawzall-c… replacing Sawzall with Golang :|
in9 · 2023-12-19
Lately, I've been enjoying a lot of posts in this blog right here:
https://eugeneyan.com/writing
My company just hired some new VPs from Amazon, and the writings in that blog convey a bit of the Amazon WOW. It's pretty insightful stuff.
Also, many posts from Julia Evans :D
More concretely, here are a few blog posts I go back to occasionally:
https://doordash.engineering/2019/02/20/experiment-rigor-for-switchbac…
https://www.unofficialgoogledatascience.com/2019/12/humans-in-loop-for…
https://www.unofficialgoogledatascience.com/2016/10/practical-advice-f…
https://eugeneyan.com/writing/ml-design-docs/
https://eugeneyan.com/writing/what-i-do-before-a-data-science-project-…
https://www.fharrell.com/post/addvalue/
https://www.fharrell.com/post/stat-ml2/