The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Ontotext

APRIL 3, 2024

Joseph Hilger : People are starting to understand that knowledge graphs are not just a tool for storing data and information. I need something that defines what those entities are and can align them with the data.” The other use case where graphs are exploding is what Gartner calls a data fabric. What are your thoughts about it?

Technology 59

Technology Risk Digital Transformation Measurement 59

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. It is also important to point out that I am keeping the data simple purely to keep communication of the story straightforward. Here's the outcomes data for the control version of the experiment. ask for a raise.

Measurement 119

Measurement Advertising Testing Marketing 119

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Occam's Razor

JULY 24, 2018

The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects ( OMG we have to get offline attribution! ). Diving a bit deeper into the x-axis… While most data can be collected in real-time now, not all metrics are useful in real-time. Assess, reality.

Analytics 93

Analytics Metrics Strategy Measurement 93

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Yes, definitely! The last 10+ years or so have seen Insurance become as data-driven as any vertical industry.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). The book, like this blog, rips up that definition and provides a expanded and more realistic business focused world view.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Without large amounts of labeled training data solving most AI problems is not possible.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

In contrast, some researchers who are exploring the definitions, possibilities, and limitations of model interpretation point toward more comprehensive views. For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. This instant. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118