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Data Mining Use Cases

The Data Administration Newsletter

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 […].

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Fundamentals of Data Mining

Data Science 101

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.

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KDD 2020 Opens Call for Papers

Data Science 101

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
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KDD 2020 Call for Research, Applied Data Science Papers

KDnuggets

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

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Human Participation - Still an indispensable element in Business Analytics

DataFloq

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.

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Education Trends 2022: Data Science in schools

DataFloq

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.

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Business Intelligence System: Definition, Application & Practice

FineReport

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.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

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.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

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
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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

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

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

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.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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?

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

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.

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LSOS experiments: how I learned to stop worrying and love the variability

The Unofficial Google Data Science Blog

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 $k$.