Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Honestly, KDD has been promoting data science way before data science was even cool. KDD 2020 is a dual-track conference, offering distinct programming in research and applied data science. 1989 to be exact. The details are below. 22-27, 2020.

KDD 81

KDD Knowledge Discovery Data mining Data Science 81

KDnuggets

DECEMBER 12, 2019

ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, Knowledge Discovery and Machine Learning for 26 th Annual Conference in San Diego.

KDD 53

KDD Knowledge Discovery Data Science Data mining 53

Analytics Vidhya

FEBRUARY 20, 2023

Introduction We are living in an era of massive data production. When you think about it, almost every device or service we use generates a large amount of data (for example, Facebook processes approximately 500+ terabytes of data per day).

Knowledge Discovery 288

Knowledge Discovery Data mining Analytics KDD 288

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). This data alone does not make any sense unless it’s identified to be related in some pattern.

Data mining 77

Data mining KDD Data Science Forecasting 77

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. This work has also resulted in advances for item #1, as it helped us define more useful objectives for A/B tests in Search Ads which include long-term impact of experimental treatments.

Modeling 63

Modeling Experimentation Knowledge Discovery Testing 63

Domino Data Lab

DECEMBER 2, 2020

Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the most well-known density-based clustering algorithm, first introduced in 1996 by Ester et.

Metrics 116

Metrics KDD Testing Machine Learning 116

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3] References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2]

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KDD Testing Machine Learning Measurement 40