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 61

Modeling Experimentation Knowledge Discovery Testing 61

Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. The content of these data sets may introduce problematic bias into a model’s results.

Deep Learning 85

Deep Learning Cost-Benefit Risk Data-driven 85

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Occam's Razor

APRIL 8, 2013

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Occam's Razor

APRIL 25, 2011

The issues of course include people and jaded mental models and bureaucracy and a lack of time and the missing desire to be great and org structures, and bosses. " I'd postulated this rule in 2005, it is even more true in 2011. Doing anything on the web without a Web Analytics Measurement Model. The 10/90 rule.

Marketing 126

Marketing Measurement Metrics Testing 126

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars. Adjustments for effects: holiday, seasonality, and day-of-week effects.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Occam's Razor

OCTOBER 21, 2013

In late 2011, Google announced an effort to make search behavior more secure. I've gone through the five stages in the Kubler-Ross model. Bonus: For more on next steps and attribution modeling please see: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models. ]. want to protect user privacy.

Optimization 154

Optimization Reporting Experimentation Metrics 154

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere. credit cards).

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Edwin Chen

AUGUST 14, 2014

In an ideal world, we'd be able to run experiments – the gold standard for measuring causality – whenever we wish. This is where propensity modeling, or other techniques of causal inference, comes into play. Propensity Modeling. So suppose we want to model the effect of drinking Soylent using a propensity model technique.

Modeling 75

Modeling Measurement Sales Testing 75

datapine

APRIL 15, 2020

The term “agile” was originally conceived in 2011 as a software development methodology. Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. In the traditional model communication between developers and business users is not a priority.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? It is hard to account for such tweaking in measurement systems. Some relate to inherent issues with what is being measured.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

OCTOBER 9, 2019

Printing the K-means objects displays the size of the clusters, the cluster mean for each column, the cluster membership for each row and similarity measures. > It is measuring the gap between reality and expectation. . > set.seed(278613) > wineK3 <- kmeans(x=wineTrain, centers=3). > wineK3.

Statistics 100

Statistics Machine Learning Metrics Measurement 100

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

Rita Sallam

AUGUST 28, 2022

For the vendors that participate in the Bake-Off, it is in equal measure fun and extremely stressful. At a global level the number of people without reliable access to electricity spiked in 2011. Several of the KPIs measured around promoting inclusive and sustainable economic growth and employment for all are highly correlated.

Analytics 52

Analytics Data Analytics Cost-Benefit Measurement 52

datapine

AUGUST 29, 2022

He founded the project Apache Storm in 2011, which turned to be “one of the world’s most popular stream processors and has been adopted by many of the world’s largest companies, including Yahoo!, Microsoft, Alibaba, Taobao, WebMD, Spotify, Yelp” according to Marz himself.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Occam's Razor

JUNE 28, 2011

Perhaps your client did not provide you with the all important Web Analytics Measurement Model. By 2011 as web experiences have become richer and more frequently and more complex I am so mad that our life is not dominated by pan-session metrics. So why measure Followers and Likes and Posts? So do this, migrate to above.

Metrics 126

Metrics Measurement Key Performance Indicator Reporting 126

Domino Data Lab

SEPTEMBER 25, 2019

We see it when working with log data, financial data, transactional data, and when measuring anything in a real engineering system. 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.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Selection bias and survivorship bias When we slice a population by some factor or combination of factors and compute a metric over the new sub-populations, we are implicitly specifying a model (the metric is a function of the slicing factors) and testing a hypothesis. What is to be done?

Experimentation 61

Experimentation Testing Statistics Metrics 61

AWS Big Data

APRIL 5, 2023

The serverless model was also compelling because we did not have to pay for server instances nor license fees per reader. Because QuickSight is serverless and uses a session-based pricing model, Showpad expects to see cost savings. With QuickSight, we pay for usage. This makes it easy for us to provide access to everyone by default.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

Edwin Chen

JANUARY 7, 2013

— Sohaib Athar (@ReallyVirtual) May 1, 2011. We’ll describe a novel real-time human computation engine we built that allows us to find search queries as soon as they’re trending, send these queries to real humans to be judged, and finally incorporate these human annotations into our backend models.

Statistics 73

Statistics Machine Learning Advertising Metadata 73

O'Reilly on Data

MAY 11, 2020

Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. To understand the menagerie of models that are fundamentally altering our individual and shared realities, we need to build a typology, a classification of their effects and impacts.

Modeling 190

Modeling Advertising Recreation/Entertainment Metrics 190