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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below. References.

Modeling 206
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Paul Martin: CIOs don’t retire, they go work on boards

CIO Business Intelligence

Two years of pandemic uncertainty and escalating business risk have sharpened the focus of corporate boards on a technology trend once dismissed as just another IT buzzword. I bring the tech and cyber expertise to those boards, and also the digital piece,” adds Martin, a member of the CIO Hall of Fame since 2017. “It

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My 10-step path to becoming a remote data scientist with Automattic

Data Science and Beyond

Ideally, I wanted a well-paid data science-y remote job with an established distributed tech company that offers a good life balance and makes products I care about. While data wrangler may sound less sexy than data scientist , reading the job ad led me to believe that the position may involve interesting data science work.

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Cloudera + Hortonworks, from the Edge to AI

Cloudera

Cloudera offers the Cloudera Data Science Workbench (CDSW) and Workload Experience Manager (Workload XM). In the meantime, each of us also has unique product offerings. Hortonworks offers its Hortonworks DataFlow, or HDF, product for streaming and IoT workloads. Forward-Looking Statements.

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Perform time series forecasting using Amazon Redshift ML and Amazon Forecast

AWS Big Data

Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. and Karra Taniskidou, E.

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

The above chart compares monthly searches for Business Process Reengineering (including its arguable rebranding as Business Transformation ) and monthly searches for Data Science between 2004 and 2019. © Scott Adams (2017) – dilbert.com. Here we come back to the upward trend in searches for Data Science.

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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. by STEVEN L.