Remove Descriptive Analytics Remove Risk Remove Visualization
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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. Data visualization and visual analytics are two terms that come up a lot when new and experienced analytics users alike delve into the world of data in their quest to make smarter decisions.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI).

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What Is The Difference Between Business Intelligence And Analytics?

datapine

While BI tells you what has happened in the past and what is happening now (descriptive analytics), BA tells you what will happen in the future (predictive analytics). Descriptive analytics : As its name suggests, this analysis method is used to describe and summarize the main characteristics found on a dataset.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Today, the most common usage of business intelligence is for the production of descriptive analytics. .

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

There is a risk of injecting bias. It’s worth noting that there is a landscape of proprietary tools dedicated to producing descriptive analytics in the name of business intelligence. Data visualization blog posts are a dime a dozen. As a result, exploratory analysis is inherently iterative, and difficult to scope.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. Clearly in these situations, becoming model-driven addresses an existential risk and that’s the point Nick Elprin made in his keynote. Because of compliance.

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Decoding Data Analyst Job Description: Skills, Tools, and Career Paths

FineReport

Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue.