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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 13: Digital Sales Enablement is a gamechanger in the post-COVID era

bridgei2i

My name is Aruna Babu, and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business technology and user needs. And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. It’s streamlining the forecast process.

Sales 93
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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 13: Digital Sales Enablement a gamechanger in the post-COVID era

bridgei2i

My name is Aruna Babu, and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology and user needs. And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. It’s streamlining the forecast process.

Sales 52
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In their 2002 paper Chawla et al. propose a different strategy where the minority class is over-sampled by generating synthetic examples. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling. 2002) provide an example that illustrates the modifications. Chawla et al., Chawla et al.,

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Public cloud vs. private cloud vs. hybrid cloud: What’s the difference?

IBM Big Data Hub

Internet companies like Amazon led the charge with the introduction of Amazon Web Services (AWS) in 2002, which offered businesses cloud-based storage and computing services, and the launch of Elastic Compute Cloud (EC2) in 2006, which allowed users to rent virtual computers to run their own applications.

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Unintentional data

The Unofficial Google Data Science Blog

The process of constructing synthetic controls is made easy by the CausalImpact package in R , which uses Bayesian structural time series to build a forecast based on a time series from a population that received the intervention with one that did not. In this way, it can be thought of as a more rigorous differences-in-differences approach.