Jen Stirrup

JUNE 20, 2020

My analysis is based on the Financial statements put forward by PASS using some basic metrics; until you do that piece, you can’t move forward to compare and contrast it with other data since you have not done your ‘descriptive statistical analysis’ first to ensure that the comparison is valid. Current Ratio.

Metrics 104

Metrics Business Analytics Data-driven Statistics 104

datapine

SEPTEMBER 16, 2022

But often that’s how we present statistics: we just show the notes, we don’t play the music.” – Hans Rosling, Swedish statistician. Data visualization, or ‘data viz’ as it’s commonly known, is the graphic presentation of data. That’s a colossal number of books on visualization. Data visualization: What You Need To Know.

Visualization 304

Visualization Dashboards Data-driven Statistics 304

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. 05) in predicting changes in attendance.

Statistics 59

Statistics Machine Learning Testing Modeling 59

datapine

AUGUST 29, 2022

And with that understanding, you’ll be able to tap into the potential of data analysis to create strategic advantages, exploit your metrics to shape them into stunning business dashboards , and identify new opportunities or at least participate in the process. It was lately revised and updated in January 2016.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. As a result, selecting knots according to the quantiles of the input data (or even linearly across the domain), and then steadily increasing their number as long as the metrics improve works well in practice. linear regression, trees) can be too rigid in their functional forms.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Human brains are not well suited to visualizing anything in greater than three dimensions. Visualizing data using t-SNE. Example 11.6 Detecting collocated bigrams with more conservative thresholds. Joulin, A., arXiv: 1607.01759. Bojanowski, P.,

Deep Learning 111

Deep Learning Modeling Metrics Testing 111