O'Reilly on Data

MAY 27, 2020

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. The report has pages of careful caveats, but in the end it treats these risk-adjusted ratios as a good measure of a surgeon’s performance.

Risk 355

Risk Testing Reporting Measurement 355

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists. The probabilistic nature changes the risks and process required.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

The Unofficial Google Data Science Blog

JUNE 30, 2016

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. For a random sample of units, indexed by $i = 1.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But importance sampling in statistics is a variance reduction technique to improve the inference of the rate of rare events, and it seems natural to apply it to our prevalence estimation problem. High Risk 10% 5% 33.3% 2] Lawrence Brown, Tony Cai, Anirban DasGupta (2001). Statistical Science. 16 (2): 101–133. [3]

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112