The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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. Crucially, it takes into account the uncertainty inherent in our experiments. Figure 5: Treatment effect vs parameter number 4.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

Peter James Thomas

AUGUST 12, 2018

These normally appear at the end of an article, but it seemed to make sense to start with them in this case: Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. A number of factors can play into the accuracy of data capture. Honesty of Data that is captured. Timing issues with Data.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

JUNE 23, 2019

Skomoroch advocates that organizations consider installing product leaders with data expertise and ML-oriented intuition (i.e., Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. A few highlights from the session include.

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

Jen Stirrup

NOVEMBER 15, 2022

Why do organizations get stuck with their data? Often, this problem can be due to the organization concentrating solely on technology and data. However, organizations can be supported by a synergistic approach by integrating systems thinking with the data strategy and technical perspective. It is such a fundamental question.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145