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.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

JUNE 28, 2023

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

Digital Transformation 87

Digital Transformation Cost-Benefit Informatics Data mining 87

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. You can start for free with a superb tool: Google's Website Optimizer. For example I am quite fond of the fact that with Offermatica you can "trigger" tests based on behavior. I cannot recommend enough the wisdom of starting with a A/B test.

Testing 112

Testing Experimentation Risk Optimization 112

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). SPC is the continuous testing of the results of automated manufacturing processes. Let’s take a look.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

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

Occam's Razor

MARCH 23, 2021

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). The first component is a gloriously scaled global creative pre-testing program. Matched market tests. The slow music.

Analytics 131

Analytics Scorecard Strategy Testing 131

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 87

Cost-Benefit Modeling Manufacturing Interactive 87

FineReport

DECEMBER 11, 2019

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. By Michael Milton. – Data Divination: Big Data Strategies.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. This has serious implications for software testing, versioning, deployment, and other core development processes. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

DataRobot

JUNE 8, 2021

Because cluster analysis is an unsupervised algorithm, it does not attempt to select groups that optimize business goals. The researchers set out to test whether consumers are more resistant to healthcare delivered by AI providers because of concerns that the AI will neglect the unique characteristics of their case.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

AWS Big Data

JULY 3, 2023

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. Solution overview To set up and test this experiment, we complete the following high-level steps: Create an S3 bucket. Load the dataset into Amazon S3.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests.

Experimentation 61

Experimentation Testing Statistics Metrics 61

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Occam's Razor

SEPTEMBER 19, 2011

We have to do Search Engine Optimization. You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. Then they isolated regions of the country (by city, zip, state, dma pick your fave) into test and control regions. The nice thing is that you can also test that!

Measurement 119

Measurement Advertising Testing Marketing 119

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. You're choosing only one metric because you want to optimize it. Testing out a new feature. Identify, hypothesize, test, react. You don’t need a beautiful beast to go out and test.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

AUGUST 22, 2011

Five Reasons And Awesome Testing Ideas. Lab Usability Testing: What, Why, How Much. Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. Tip #9: Leverage Statistical Control Limits. Tip#1: Statistical Significance. Got Surveys? Got Surveys?

KPI 124

KPI Metrics Measurement ROI 124

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. When hyperparameters are also used for optimization and tuned for a specific dataset, they also impact predictive accuracy. Searching is a very general approach to solving or optimizing problems. We need some sort of well-defined process?

Testing 79

Testing Modeling Machine Learning Metrics 79

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders. evaluate the effects of models on human subjects.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. Length: Highly Variable. Teams new to hiring often make this mistake of creating long multi-stage screening processes.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Usually at least a test. Meanwhile see if you can convince your HiPPO to run a small test while you look for a case study (and a job). ]. This is sad.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

SEPTEMBER 13, 2016

Hypothesis development and design of experimentation. Take this as an example… How do you know that this is a profoundly sub-optimal collection of choices to provide? Ok, maybe statistical modeling smells like an analytical skill. If these 50 pass the sniff test, send the survey. Argumentation and logical thinking.

Modeling 127

Modeling Reporting Strategy Consulting 127

O'Reilly on Data

MARCH 23, 2023

Many of these go slightly (but not very far) beyond your initial expectations: you can ask it to generate a list of terms for search engine optimization, you can ask it to generate a reading list on topics that you’re interested in. It was not optimized to provide correct responses. It has helped to write a book.

IT 260

IT Modeling Testing Risk 260

Occam's Razor

JANUARY 9, 2012

All while constantly optimizing your portfolio via controlled experiments. I told 20 people that Nikon's site is slow and profoundly sub-optimal on mobile. Companies get entrenched in what they know and end up constantly optimizing for what's always worked, meanwhile the world changes and these companies die, albeit slowly.

Marketing 140

Marketing B2B Measurement Risk 140

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. This test set is available at download.tensorflow.org/data/questions-words.txt.]. Note that the final test word in Table 11.2—ma’am—is

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). What's the optimal organization structure (and who should own web analytics!)? What ideas to test first on your site? The book takes a stand on issues, makes choices and cuts through the fog/FUD, in an attempt to make your life a tiny bit easier.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. This allowed them to focus on SQL-based query optimization to the nth degree. But the simplicity ends there. Every transaction, every cent matters.

OLAP 90

OLAP Data Lake Data-driven Snapshot 90