Remove Predictive Modeling Remove Testing Remove Visualization
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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

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. Even basic predictive modeling can be done with lightweight machine learning in Python or R.

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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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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

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

DataKitchen

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. What is Data in Use?

Testing 173
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Why you should care about debugging machine learning models

O'Reilly on Data

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. 6] Debugging may focus on a variety of failure modes (i.e.,

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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. That’s enterprise-wide agile curiosity, question-asking, hypothesizing, testing/experimenting, and continuous learning.

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Business analytics also involves data mining, statistical analysis, predictive modeling, and the like, but is focused on driving better business decisions.