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 54

Machine Learning Data Science Statistics Deep Learning 54

Alation

MARCH 29, 2022

The request model started to fray. As Business Objects founder Bernard Liautaud notes in e-Business Intelligence: Turning Information Into Knowledge Into Profit (McGraw-Hill, 2001), the lack of ad hoc data access causes IT staff to drown in requests. Subscribe to Alation's Blog. appeared first on Alation.

Business Intelligence 96

Business Intelligence Reporting Data mining Data-driven 96

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

This blog post provides a concise session summary, a video, and a written transcript. 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.” Session Summary. Transcript.

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. 2] Lawrence Brown, Tony Cai, Anirban DasGupta (2001). Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4]

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

JANUARY 6, 2019

And my favorite topic: what are some of the best books, blogs, podcasts, etc., Also, clearly there’s no “one size fits all” educational model for data science. The Berkeley model addresses large university needs in the US. What are the projected risks for companies that fall behind for internal training in data science?

Data Science 56

Data Science Machine Learning Reporting Visualization 56