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The history of ESG: A journey towards sustainable investing

IBM Big Data Hub

It refers to a set of metrics used to measure an organization’s environmental and social impact and has become increasingly important in investment decision-making over the years. In response, asset managers began to develop ESG strategies and metrics to measure the environmental and social impact of their investments.

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The Newest FIFA World Cup Referee: Human-in-the-Loop Machine Learning

Cloudera

C (Cloudera is headquartered in the US, but we also recognize the superiority of the metric system). The second notable fact about the 2022 World Cup is that this is only the second World Cup to be held entirely in Asia, the first being the 2002 tournament held in South Korea and Japan.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. In their 2002 paper Chawla et al.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

The most poignant for me was a simple approach for measuring noise within an organization. Measure how these decisions vary across your population. Then calculate the variance divided by the mean to construct a metric for noise in decision-making. For kicks, try calculating this kind of metric within your own organization.

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Customer science: A new CIO imperative

CIO Business Intelligence

Should reducing or eliminating customer rage become an IT metric? Science is defined by many as the rigorous and systematic identification and measurement of phenomena. Does your organization measure customer experience? What gets measured and what gets rewarded drive behavior. This has to change.

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Unintentional data

The Unofficial Google Data Science Blog

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Looking at metrics of interest computed over subpopulations of large data sets, then trying to make sense of those differences, is an often recommended practice (even on this very blog).