Smart Data Collective

AUGUST 5, 2020

The point is that the American courtrooms have faced government pressure to reduce prison quantity but avoiding risks in crime rising. Specifically, they apply the examination of historical crime statistics and face-recognition technology to improve staff effectiveness. To make the above-mentioned reality, U.S.

Risk 107

Risk Statistics Machine Learning Big Data 107

BizAcuity

APRIL 28, 2019

is delinquent as of June 30th, 2017. By clubbing various techniques like data mining, machine learning, artificial intelligence and statistical modelling, it makes predictions about events in the future. Their issue at hand was to decrease the Portfolio at Risk. Out of which, $400 billion is delinquent for more than 90 days.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Smarten

NOVEMBER 19, 2018

Predictive analytics employs various analytical and modeling techniques, leveraging historical data and business results to identify crucial relationships, opportunities and risks so that business managers can more accurately predict growth, and competitive and market changes and identify trends and patterns.

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

O'Reilly on Data

FEBRUARY 18, 2020

Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. There’s plenty of security risks for business executives, sysadmins, DBAs, developers, etc., After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage.

Data-driven 342

Data-driven Software Statistics Marketing 342

Peter James Thomas

JUNE 20, 2019

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. © Scott Adams (2017) – dilbert.com. In 2017, a survey estimated that 68 percent of American offices had low or no separation between workers. Source: Google Trends.

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

Smarten

NOVEMBER 22, 2017

Your business users can perform advanced analytics, using sophisticated tools in an easy-to-use, drag and drop interface, with no advanced skill requirement for statistical analysis, algorithms or technical knowledge.

Analytics 40

Analytics IT Business Intelligence Visualization 40

The Unofficial Google Data Science Blog

APRIL 17, 2017

Such a model risks conflating important aspects, notably the growth trend, with other less critical aspects. In other words, there is an asymmetry of risk-reward when there exists the possibility of misspecifying the weights in $X_C$. This imbues the forecasting routine with two attractive properties.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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. And we can keep repeating this approach, relying on intuition and luck.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? Wes McKinney (2017). Aurélien Géron (2017). How do options such as mentoring programs fit into this picture, both for organizations and for the individuals involved? In business terms, why does this matter ?

Data Science 56

Data Science Machine Learning Reporting Visualization 56

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

MAY 8, 2019

Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Adrian Weller (2017-07-29). “ That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. Challenges for Transparency ”. Riccardo Guidotti, et al.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. And it can look up an author and make statistical observations about their interests. What Are the Risks?

IT 271

IT Modeling Testing Risk 271

Domino Data Lab

AUGUST 8, 2019

The probabilistic nature changes the risks and process required. Another key point: troubleshooting edge cases for models in production—which is often where ethics and data meet, as far as regulators are concerned—requires much more sophistication in statistics than most data science teams tend to have. machine learning?

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Peter James Thomas

AUGUST 12, 2018

Integrity of statistical estimates based on Data. Having spent 18 years working in various parts of the Insurance industry, statistical estimates being part of the standard set of metrics is pretty familiar to me [7]. The thing with statistical estimates is that they are never a single figure but a range. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. In our case, it turns out that the monotonicity regularizer allows us to increase the number of knots without incurring much risk of overfitting there are fewer ways for the model to go wrong. linear regression, trees) can be too rigid in their functional forms.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics.

Statistics 27

Statistics Uncertainty Risk Marketing 27

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

JANUARY 25, 2021

Observability” risks becoming the new name for monitoring. Our data shows that Chef and Puppet peaked in 2017, when Kubernetes started an almost exponential growth spurt, as Figure 4 shows. And that’s unfortunate. If you think observability is merely a more fashionable term for monitoring, you’re missing its value.

Machine Learning 363

Machine Learning Software Testing Technology 363