Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

O'Reilly on Data

JANUARY 22, 2019

They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below. Thompson, L.S.

Modeling 133

Modeling Finance Uncertainty Data mining 133

Cloudera

OCTOBER 3, 2018

That team delivered the first production cluster in 2006 and continued to improve it in the years that followed. In 2008, I co-founded Cloudera with folks from Google, Facebook, and Yahoo to deliver a big data platform built on Hadoop to the enterprise market. Yahoo quickly recognized the promise of the project.

Uncertainty 75

Uncertainty IoT Risk Reporting 75

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Holmes, C.

Forecasting 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

MARCH 31, 2016

In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. Data analysis using regression and multilevel/hierarchical models." Cambridge University Press, (2006). [2] bandit problems). ICML, (2005). [3]

Modeling 40

Modeling Statistics Advertising Testing 40