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External Data Supports More Accurate Planning

David Menninger's Analyst Perspectives

So, it is essential to incorporate external data in forecasting, planning and budgeting, especially for predictive analytics and machine learning to support artificial intelligence. At the end of the season, the vendor brings in a consultant to advise on pricing for the coming year.

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Proposals for model vulnerability and security

O'Reilly on Data

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

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

CIO Business Intelligence

Even basic predictive modeling can be done with lightweight machine learning in Python or R. In life sciences, simple statistical software can analyze patient data. The results of these models are then combined using a simple algorithm to determine the best-performing model for a given item, which is then used for prediction.

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The curse of Dimensionality

Domino Data Lab

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

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IT leaders get creative to fill data science gaps

CIO Business Intelligence

Data scientists have extensive academic backgrounds — often in computer science, statistics, and mathematics. They specialize in building powerful algorithms, and analyzing, processing, and modeling data so they can then interpret the results to create actionable plans. Expanding data science teams. TruStone Financial Credit Union.

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3 Things Citizen Data Scientists Need in Predictive Analytics!

Smarten

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ Descriptive Statistics. Access to Flexible, Intuitive Predictive Modeling. Trends and Patterns. Forecasting.