IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. ” “Data science” was first used as an independent discipline in 2001.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Occam's Razor

APRIL 25, 2011

This post is to solve that problem. I'm going to present a cluster of what I think are digital "crimes against humanity." " A mighty term, used in a very unmighty sense here, but I hope it makes you sit up and take note. How many of these things is your company currently doing. There are 6.9 billion of them actively use 4.3

Marketing 126

Marketing Measurement Metrics Testing 126

AWS Big Data

OCTOBER 11, 2023

Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities. These metrics help agents improve their call handle time and also reallocate agents across organizations to handle pending calls in the queue.

Management 104

Management Metadata Analytics Dashboards 104

Domino Data Lab

JULY 22, 2019

He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Paco Nathan: Thank you, Jon [Rooney]. I really appreciate it. There’s a balance. It’s about the attendees.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

O'Reilly on Data

MAY 27, 2020

Algorithms tell stories about who people are. The first story an algorithm told about me was that my life was in danger. It was 7:53 pm on a clear Monday evening in September of 1981, at the Columbia Hospital for Women in Washington DC. I was exactly one minute old. You get two points for waving your arms and legs, for instance.)

Risk 356

Risk Testing Measurement Reporting 356

Domino Data Lab

MAY 20, 2021

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. Generation of artificial examples.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136