Smart Data Collective

NOVEMBER 18, 2020

The Data Scientist profession today is often considered to be one of the most promising and lucrative. The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. What is Data Science? Definition: Data Mining vs Data Science.

Data Mining 144

Data Mining Data Mining Data Mining Data Science 144

KDnuggets

DECEMBER 24, 2019

This article is about Market Basket Analysis & the Apriori algorithm that works behind it.

Algorithm 395

Algorithm Data Mining Data Mining Data Mining 395

Smart Data Collective

FEBRUARY 19, 2020

Lenders are tightening their actuarial criteria and employing data driven decision making capabilities. If a company is looking to borrow money, they need to understand how big data has changed the process. They need to adapt their borrowing strategy to the new big data algorithms to improve their changes of securing a loan.

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Big Data Big Data Data Mining Data Mining 118

Dataconomy

JULY 4, 2023

Gartner coined the term “hyper automation” in 2019 to describe the integration of multiple automation technologies ( Image Credit ) What is hyper automation? ML algorithms enable systems to identify patterns, make predictions, and take autonomous actions.

Natural Language Processing 192

Natural Language Processing Big Data Big Data Machine Learning 192

Smart Data Collective

JUNE 24, 2023

In 2018, researchers used data mining and machine learning to detect Ponzi schemes in Ethereum. In 2019, another team tested the new fraudulent behavior Honeypot in Ethereum. This technology relies on some of that same machine learning algorithms used to fight other forms of fraud. How Do Crypto Wallets Work?

Machine Learning 98

Machine Learning Machine Learning Artificial Intelligence Artificial Intelligence 98

The MLOps Blog

FEBRUARY 27, 2025

Adhering to data protection laws is not as complex if we focus less on the internal structure of the algorithms and more on the practical contexts of use. To keep data secure throughout the models lifecycle, implement these practices: data anonymization, secure model serving and privacy penetration tests.

Machine Learning 64

Machine Learning Machine Learning AI AI 64