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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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A CDO’s Guide to the Data Catalog

Alation

In 2002, Capital One became the first company to appoint a Chief Data Officer (CDO). The same business metrics may have different values depending on which team you ask. In addition, data curators can certify data sets and metrics within the data catalog so all business users use data in a consistent manner.

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The Data Visualization Design Process: A Step-by-Step Guide for Beginners

Depict Data Studio

Surrounding myself with a variety of chart types, all of which have been used in different reports and for different groups of people, helps me create brand new charts easily. Put your easiest-to-follow chart in your final presentation or report. Consultants, this means the report will look like it came from the client.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

Auxiliary techniques like relying on card holders to report fraudulent transactions have unfortunately proven to be ineffective [1]. from sklearn import metrics. With this criterion in mind, we can define a distance metric to the top left corner of the curve and find a threshold that minimises it. from datetime import datetime.

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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).