The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. In isolation, the $x_1$-system is optimal: changing $x_1$ and leaving the $x_2$ at 0 will decrease system performance.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

MARCH 23, 2021

From 2006: Is Real-Time Analytics Really Relevant? ). In our in-flight optimization journey thus far, we have worked to identify signals that are believable, and identifying at which point they become believable (ex: statistically significant). You have the start of a fabulous in-flight optimization engine.

Analytics 131

Analytics Scorecard Strategy Testing 131

The Unofficial Google Data Science Blog

APRIL 17, 2017

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. The problems down in the mature bucket, those are optimizations, they aren’t showstoppers. Tukey did this paper. Roll the clock out.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Occam's Razor

MARCH 12, 2012

Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more. The current flawed data org structure, its challenges, and the new optimal org structure to truly bring big action to big data. 01:15 – 04:05 Part 1.

Big Data 127

Big Data Data-driven Unstructured Data Marketing 127

Sisense

APRIL 9, 2019

Originally created in 2006, it’s one of the most popular open source BI tools. Between the language undergirding it and the power of its architecture, Hadoop has found a sizable following, tackling core BI tasks like statistical analytics and Big Data processing, including handling huge volumes of data from fleets of IoT sensors and more!

Analytics 75

Analytics Software IoT Big Data 75

Domino Data Lab

JULY 1, 2021

statistical model-based techniques – Using Machine Learning we can streamline and simplify the process of building NER models, because this approach does not need a predefined exhaustive set of naming rules. The process of statistical learning can automatically extract said rules from a training dataset. The CRF model.

Modeling 111

Modeling Statistics Testing Metrics 111