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. How can we understand causal lifts in the absence of an A/B test? This is where propensity modeling, or other techniques of causal inference, comes into play. Propensity Modeling.

Modeling 75

Modeling Measurement Sales Testing 75

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. 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. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

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. We went through user research, development, and beta tests in a span of 6 months, which was a fast turnaround and a big win for us,” says Minnaert. With QuickSight, we pay for usage.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

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

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. 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. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

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. is one of the greatest on the market.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests. In addition to the issues above, does the conclusion pass the smell test?

Experimentation 61

Experimentation Testing Statistics Metrics 61

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