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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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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

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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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Data Insights for Everyone — The Semantic Layer to the Rescue

Rocket-Powered Data Science

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. What is a semantic layer? That’s a good question, but let’s first explain semantics. What a nightmare that would be! Now it is a reality.

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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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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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