CIO Business Intelligence

JUNE 7, 2022

Generally, the output of data analytics are reports and visualizations. Data analytics describes the current or historical state of reality, whereas data science uses that data to predict and/or understand the future. Data analytics and data science are closely related. Data analytics vs. business analytics. Data analytics salaries.

Data Analytics 131

Data Analytics Diagnostic Analytics Management Analytics 131

The Unofficial Google Data Science Blog

MARCH 31, 2016

Random Effect Models We will start by describing a Gaussian regression model with known residual variance $sigma_j^2$ of the $j$th training record's response, $y_j$. Often our data can be stored or visualized as a table like the one shown below. arXiv preprint arXiv:1506.04416 (2015). [6] 5] Anoop Korattikara, et al.

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

AUGUST 1, 2021

In this article we’ll use Skater , a freely available framework for model interpretation, to illustrate some of the key concepts above. Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. layer-wise relevance propagation), model distillation (e.g. 2015) for additional details.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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