DataRobot

JANUARY 13, 2022

Many companies find that they have a treasure trove of data but lack the expertise to use it to improve ROI. AI can help them wrangle the data that’s collected and quickly answer important questions. But they often don’t have enough data scientists with the full range of domain expertise to realize its full value.

ROI 52

ROI Machine Learning Experimentation Data Science 52

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. How can good data lead to faulty conclusions? How does this happen? 9/10 = 90%.

Testing 104

Testing Risk Data-driven Statistics 104

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. Hernán and Robins are both epidemiologists, which means they often have to deal with data with strong limitations on sample size and feasibility of experiments.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

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

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

DataRobot Blog

OCTOBER 27, 2022

Data drift is a phenomenon that reflects natural changes in the world around us, such as shifts in consumer demand, economic fluctuation, or a force majeure. The key, of course, is your response time: how quickly data drift can be analyzed and corrected. Change is inevitable. Growth is optional. John Maxwell.

Data Science 52

Data Science Experimentation Visualization Modeling 52

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. ML model interpretability and data visualization. St Paul’s from Madison London.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

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.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

IBM Big Data Hub

SEPTEMBER 25, 2023

The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Consider the magnitude of Uber’s footprint.

OLAP 94

OLAP Data Lake Data-driven Snapshot 94