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 4: Visualization of a central composite design.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

datapine

SEPTEMBER 4, 2023

Table of Contents 1) The Benefits Of Data Visualization 2) Our Top 27 Best Data Visualizations 3) Interactive Data Visualization: What’s In It For Me? 4) Static vs. Animated Data Visualization Data is the new oil? ” – David McCandless Humans are visual creatures. This very notion is the core of visualization.

Interactive 264

Interactive Visualization Cost-Benefit Dashboards 264

Timo Elliott

JANUARY 18, 2021

It’s very visual and something somebody can grasp really quickly. And so not only are we making this data available to everybody, we make the underlying data available and we’re always looking in looking for new visualizations that we think will present things in a different way.

IT 71

IT Dashboards Digital Transformation Data-driven 71

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. You know, these are probabilistic systems.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Data Science and Beyond

JANUARY 7, 2019

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Don’t compare confidence intervals visually. Pitfall #1: Inaccurate confidence intervals.

Statistics 77

Statistics Uncertainty Testing Modeling 77

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. We do this by using the attributions as a (soft) window over the image itself.

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.

Forecasting 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

Modeling 40

Modeling Statistics Advertising Testing 40

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Note also that this account does not involve ambiguity due to statistical uncertainty. Another concern is that the Google results page sometimes contains visual elements, such as images, that may create sharp changes in user attention. To roughly control for this effect, we compared click patterns on devices of different height.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. You need to have these windows into the data and into your models and be able to test and change them visually.

Management 110

Management Machine Learning Experimentation Metrics 110