Remove Interactive Remove Statistics Remove Uncertainty Remove Visualization
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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

Decision support systems definition A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. Commonly used models include: Statistical models. Dashboards and other user interfaces that allow users to interact with and view results.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The AGI would need to handle uncertainty and make decisions with incomplete information. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

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Take Advantage Of The Best Interactive & Effective Data Visualization Examples

datapine

Table of Contents 1) The Benefits Of Data Visualization 2) Our Top 27 Best Data Visualizations 3) Interactive Data Visualization: What’s In It For Me? 4) Static vs. Animated Data Visualization Data is the new oil? ” – David McCandless Humans are visual creatures. No, data is the new soil.”

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Crucially, it takes into account the uncertainty inherent in our experiments. Figure 4: Visualization of a central composite design.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. You know, these are probabilistic systems.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

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Product Management for AI

Domino Data Lab

This talk will describe how you can navigate all these challenges that you’re going to face and build a business where every product interaction benefits from your investment in machine learning. You need to have these windows into the data and into your models and be able to test and change them visually.