The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 63

Modeling Experimentation Knowledge Discovery Testing 63

Domino Data Lab

SEPTEMBER 25, 2019

One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). These models are well represented in R and are fairly easy to work with. AR models can be thought of as linear regressions of the current value of the time series against previous values.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

CIO Business Intelligence

AUGUST 2, 2022

Reyes has been with AES since 2007, working his way up the organization ladder from an SAP integration lead in Buenos Aires to application security manager, IT project director, and director of digital transformation today. If it doesn’t work, and we don’t understand why, then, we pivot to a different model and a hypothesis.

Data Quality 97

Data Quality Digital Transformation Machine Learning Predictive Analytics 97

Sisense

APRIL 2, 2019

The DevOps movement started to come together sometime between 2007 and 2008. This is when IT operations and software development communities started to talk about problems in the software industry, specifically around the traditional software development model. It’s also shaping the way BI and Analytics are deployed.

Software 80

Software Testing Enterprise IT 80

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

Ontotext

MAY 26, 2021

Consider an example in which our first data source says that Microsoft invested $240 million in Facebook and the second – that on October 24, 2007 Microsoft invested in Facebook. But, before we can have any larger scale implementation of these rules, we have to test their validity. However, this is not always so straightforward.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

Occam's Razor

FEBRUARY 7, 2011

The best option is to hire a statistician with experience in data modeling and forecasting. Brian Krick: Best way to measure and communicate "available demand" from available channels (social, search, display) for forecast modeling. Post Testing: Visits 30k. Please see the advice above. the web is so insanely fluid, 2.

Analytics 94

Analytics Measurement Metrics Predictive Analytics 94

DataRobot

JUNE 10, 2021

It was introduced in 1980 but open-sourced in 2007, which created its widespread use. DataRobot’s AutoML uses different feature engineering techniques and a variety of machine learning algorithms to identify the best model for multilabel classification. The best model for this dataset is a Keras-based neural network.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. the curse of dimensionality).

Statistics 45

Statistics Optimization Modeling Experimentation 45

Occam's Razor

OCTOBER 15, 2012

I spend 70% of my time in the US and for those discussions I'm primary looking at speed (connection above), mobile penetration (yes, 2007 was the year of mobile!), Think of it as attribution modeling. :). and mobile intent to purchase (I love this data).

Advertising 137

Advertising Marketing Strategy Insurance 137

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The first step in developing any model is gathering a suitable source of training data, and sentiment analysis is no exception. Sentiment analysis models.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test. After the test period finishes, the campaigns in the treatment group are reset to their original configurations.

Advertising 43

Advertising Testing Sales Statistics 43

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. EC2 was a more evolved version of a Virtual Machine.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

The Unofficial Google Data Science Blog

JULY 18, 2023

Parametric measurements Thus far, we’ve been discussing non-parametric approaches to measuring reliability and validity, but in this section we’ll present an approach to get the same measurements from parametric models. Each item receives multiple labels, and each person labeling this data assesses multiple items.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Edwin Chen

MARCH 13, 2011

This is a summary of Bell and Koren’s 2007 Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights paper. Another way of thinking about this is that we are simply building a regression model. I’m now (very slowly) trying to clean up my notes and put them online. Bob likes Kate Winslet).

Optimization 40

Optimization Modeling Cost-Benefit Measurement 40

CIO Business Intelligence

OCTOBER 29, 2024

since 2007. Training a single AI model emits as much as five average cars over their lifetimes. By aligning your infrastructure and operations with ESG frameworks, we identify key opportunities to drive innovation, enhance operational efficiency and gain a competitive edge. Research indicates that the circular economy presents a $4.5

Enterprise 122

Enterprise Cost-Benefit Metrics Strategy 122

datapine

DECEMBER 28, 2021

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. In 2007, Colgate was ordered by the Advertising Standards Authority (ASA) of the U.K. 3) Misleading statistics in advertising.

Statistics 363

Statistics Advertising Visualization Data mining 363