AWS Big Data

MARCH 12, 2024

AWS Glue Data Quality reduces the effort required to validate data from days to hours, and provides computing recommendations, statistics, and insights about the resources required to run data validation. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 91

Data Quality Measurement Testing Visualization 91

datapine

JANUARY 6, 2022

In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. trillion gigabytes!

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Occam's Razor

MAY 14, 2012

Bonus One: Read: Brand Measurement: Analytics & Metrics for Branding Campaigns ]. There are many different tools, both online and offline, that measure the elusive metric called brand strength. I love using this tool to measure " unaided brand recall." Now you can answer those objections/scenarios.

Measurement 128

Measurement Analytics Advertising Marketing 128

AWS Big Data

APRIL 3, 2024

Many organizations already use AWS Glue Data Quality to define and enforce data quality rules on their data, validate data against predefined rules , track data quality metrics, and monitor data quality over time using artificial intelligence (AI). The metrics are saved in Amazon S3 to have a persistent output. onData(df).useRepository(metricsRepository).addCheck(

Data Quality 91

Data Quality Visualization Metadata Metrics 91

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. Identification We now discuss formally the statistical problem of causal inference. we drop the $i$ index.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98