The Unofficial Google Data Science Blog

JULY 22, 2020

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. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

IBM Big Data Hub

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. Uber’s prowess as a transportation, logistics and analytics company hinges on their ability to leverage data effectively. But the simplicity ends there.

OLAP 90

OLAP Data Lake Data-driven Snapshot 90

Octopai

MAY 2, 2022

Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. Just-in-Time” manufacturing increases production while optimizing resources. Issue detected?

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. ” AGI analyzes relevant code, generates a draft function with comments explaining its logic and allows the programmer to review, optimize and integrate it.

Cost-Benefit 92

Cost-Benefit Modeling Manufacturing Interactive 92

FineReport

DECEMBER 19, 2019

Generally, companies will store data in local databases or public clouds. and others will use big data storage format like HBase and Parquet. Python and R are the two most widely used programming languages in the field of data analysis. Data Analysis Libraries. Most database systems use SQL. Programming Languages.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. But it is not routine. The result?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15

Snapshot 86

Snapshot Data Lake Testing Strategy 86

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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Perhaps if machine learning were solely being used to optimize advertising or ecommerce, then Agile-ish notions could serve well enough. PSL models are easy to use and fast.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105