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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

While quantitative analysis, operational analysis, and data visualizations are key components of business analytics, the goal is to use the insights gained to shape business decisions. Business analytics is a subset of data analytics. The discipline is a key facet of the business analyst role.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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What is data science? Transforming data into value

CIO Business Intelligence

For most organizations, it is employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like. Data science gives the data collected by an organization a purpose. Data science vs. data analytics.

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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? Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g.,

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

IBM Big Data Hub

With QRadar EDR, security analysts can leverage attack visualization storyboards to make quick and informed decisions. Advanced analytics help detect known and unknown threats to drive consistent and faster investigations every time and empower your security analysts to make data-driven decisions.

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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data.

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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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