Remove Data mining Remove Predictive Modeling Remove Risk Remove Visualization
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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? It is frequently used for risk analysis.

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The Impact of Healthcare BI Tools on Decision-Making and Patient Care

FineReport

Healthcare data governance plays a pivotal role in ensuring the secure handling of patient data while complying with stringent regulations. The implementation of robust healthcare data management strategies is imperative to mitigate the risks associated with data breaches and non-compliance.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. appeared first on IBM Blog.

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Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

Smarten

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Data analysis and interpretation The next step is to examine the extracted patterns, trends and insights to develop meaningful conclusions.

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Research shows extensive use of AI contains data breaches faster and saves significant costs

IBM Big Data Hub

Under-deployed tools and solutions that do the minimal that’s “good enough” or that face other barriers like the risk aversion to fully automating processes that could have unintended consequences. With QRadar EDR, security analysts can leverage attack visualization storyboards to make quick and informed decisions.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

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. Partial Dependence Plots (PDPs).

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