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Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

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Data Visualization Inspiration: Analysis To Insights To Action, Faster!

Occam's Razor

Like a vast majority on planet Earth, I love data visualizations. A day-to-day manifestation of this love is on my Google+ or Facebook profiles where 75% of my posts are related to my quick analysis and learnings from a visualization. Data visualized is data understood. Be it looking at 1.1 Be it looking at 1.1 More useful.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Imagine generating complex narratives from data visualizations or using conversational BI tools that respond to your queries in real time. Tableau, Qlik and Power BI can handle interactive dashboards and visualizations.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

A common task for a data scientist is to build a predictive model. If it does, you suspect that the variable you’re trying to predict has mixed in with the variables used to predict it. You might say that the outcome of this exercise is a performant predictive model. That’s sort of true.

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Top Data Science Tools That Will Empower Your Data Exploration Processes

datapine

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. provides the user with visualizations, code editor, and debugging. Not to forget various areas of data scientists employed in, from academia to IT companies.

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Data Insights Assure Quality Data and Confident Decisions!

Smarten

Today, organizations look to data and to technology to help them understand historical results, and predict the future needs of the enterprise to manage everything from suppliers and supplies to new locations, new products and services, hiring, training and investments. But too much data can also create issues.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes. Because let’s face it, your customers don’t care where the problem originated—they want it fixed and fast. What is Data in Use?

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