The Data Administration Newsletter

JUNE 15, 2022

Given that the global big data market is forecast to be valued at $103 billion in 2027, it’s worth noticing. As the amount of data generated […].

Data mining 82

Data mining Knowledge Discovery Forecasting Marketing 82

Data Science 101

OCTOBER 31, 2019

Today we are generating data more than ever before. Over the last two years, 90 percent of the data in the world was generated. This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

Data mining 65

Data mining KDD Knowledge Discovery Forecasting 65

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool.

KDD 72

KDD Knowledge Discovery Data mining Statistics 72

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. 2019 Dec Events Applications CA KDD KDD-2020 Research San Diego

KDD 40

KDD Knowledge Discovery Data mining Machine Learning 40

DataFloq

FEBRUARY 10, 2021

Business Analytics was designed for addressing the need for deriving intelligence out of ‘data’, which is nowadays referred to affectionately by many as the ‘crude oil’ or ‘gold ore’ of modern times. Business Analytics synergizes the strengths of various sciences including data mining, knowledge discovery, machine learning, pattern recognition, statistics, neurocomputing, and artificial intelligence. Big Data TechnicalBusiness Analytics has evolved a lot.

Business Analytics 204

Business Analytics Knowledge Discovery Analytics Data mining 204

DataFloq

JULY 4, 2022

Data Science is a growing field that has emerged in many key areas of our world. Data Science has become a global phenomenon and has significantly improved the performance of many industries. Data Science has even incorporated education under its umbrella. Data is everywhere.

Data Science 239

Data Science Knowledge Discovery Statistics Predictive Analytics 239

FineReport

JULY 16, 2021

Among these problems, one is that the third party on market data analysis platform or enterprises’ own platforms have been unable to meet the needs of business development. With the advancement of information construction, enterprises have accumulated massive data base. Data Warehouse.

Business Intelligence 83

Business Intelligence Informatics OLAP Data Warehouse 83

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. 1988), E-state data (Hall et al., The unreasonable effectiveness of data. Data mining for direct marketing: Problems and solutions.

Knowledge Discovery 108

Knowledge Discovery Metrics Data mining Measurement 108

Domino Data Lab

AUGUST 1, 2021

For example, article 22 of the General Data Protection Regulation (GDPR) introduces the right of explanation – the power of an individual to demand an explanation on the reasons behind a model-based decision and to challenge the decision if it leads to a negative impact for the individual.

Modeling 110

Modeling Testing Knowledge Discovery Deep Learning 110

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. This post will discuss how to use data from a MAB to get unbiased estimates. Data science

Experimentation 80

Experimentation Statistics Testing Knowledge Discovery 80

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A small but persistent team of data scientists within Google’s Search Ads has been pursuing item #2 since about 2008, leading to a much improved understanding of the long-term user effects we miss when running typical short A/B tests. This knowledge has influenced our decision-making way beyond the concrete cases we studied in detail. We use this knowledge to define objective functions to optimize our ads system with a view towards the long-term.

Data Science 54

Data Science Experimentation KDD Testing 54

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Data Science 41

Data Science Experimentation Statistics Metrics 41

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

These decisions are often business-critical, so it is essential for data scientists to understand and improve the regressions that inform them. Thus, the data scientist’s job is to work with a huge black box that can change at any time. Empirical Bayes methods find a prior such that when we add Poisson noise, we fit the distribution of our observed data. We also need to make sure our model fits the data.

Data Science 40

Data Science KDD Testing Modeling 40

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. We can remove its effect if we employ an estimator $mathcal{E}_2$ that takes into account the fact that the data are sliced: [ mathcal{E}_2=sum_k frac{|T_k|+|C_k|}{|T|+ |C|}left( frac{1}{|T_k|}sum_{i in T_k}Y_i - frac{1}{|C_k|}sum_{i in C_k}Y_i right) ] Here, $T_k$ and $C_k$ are the subsets of treatment and control indices in Slice $k$.

Data Science 40

Data Science Experimentation Metrics Statistics 40