Data Science 101
JANUARY 15, 2020
Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Bias can cause a huge error in experimentation results so we need to avoid them. Validity: Valid data measures what we actually intend to find out.
The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
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CIO Business Intelligence
FEBRUARY 14, 2024
The same issues were revealed when Microsoft launched Delve, and before that when the FAST integration brought powerful search to SharePoint in 2010. Introduce gen AI capabilities without thinking about data hygiene, he warns, and people will be disillusioned when they haven’t done the pre work to get it to perform optimally. But it was.
Ontotext
DECEMBER 30, 2022
9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010.
Ontotext
DECEMBER 30, 2022
9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010.
Domino Data Lab
AUGUST 5, 2019
In 2010, Netflix cancelled their second recommendation contest after a privacy lawsuit. Also, data science work is experimental and probabilistic in nature. The associated paper, “ Robust De-anonymization of Large Sparse Datasets ” by Avrind Narayanan and Vitaly Shmatikov. data munging, building models, etc.).
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected. Make experimentation cheap and understand the cost of bad decisions. This leads to the proliferation of post hoc hypotheses. Consider your loss function.
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