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Fundamentals of Data Mining

Data Science 101

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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How Do Super Rookies Start Learning Data Analysis?

FineReport

For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. 6 Key Skills That Data Analysts Need to Master.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

In this blog post, we summarize that paper and refer you to it for details. References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on Knowledge Discovery and Data Mining, 2015. [2] 2] Ron Kohavi, Randal M.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

The statistical effect size is often defined as [ e=frac{delta}{sigma} ]which is the difference in group means as a fraction of the (pooled) standard deviation (sometimes referred to as “Cohen’s d” ). Further assume $Y_i sim N(mu,sigma^2)$ under control and $Y_i sim N(mu+delta,sigma^2)$ under treatment (i.e. known, equal variances).

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. References [1] Kohavi, Ron, Randal M. Henne, and Dan Sommerfield. 2] Scott, Steven L. 2015): 37-45. [3]

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

The dataset and code used in this blog post are available at [link] and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform. References. Data mining for direct marketing: Problems and solutions. Banko, M., & Brill, E.

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

The Unofficial Google Data Science Blog

For more on ad CTR estimation, refer to [2]. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] A machine learning system produces an estimated CTR $t_i$ for each query-ad pair. Our method has four steps: Bin by $t$.

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