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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

Ontotext

FEBRUARY 14, 2024

How do you measure its utility? As a statistical model, LLM inherently is random. With things like named graphs, layering the data, testing those what-if scenarios, and checking the state is fantastic. We find that as great as SHACL is at ensuring the data quality, testing the RDF pipelines is another matter.

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

Sisense

JANUARY 21, 2020

This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term. A new drug promising to reduce the risk of heart attack was tested with two groups.

Testing 104

Testing Risk Data-driven Statistics 104

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Representational uncertainty : the gap between the desired meaning of some measure and its actual meaning.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Measuring Online Engagement: What Role Does Web Analytics Play? "Engagement" How Do I Measure Success?

KPI 124

KPI Metrics Measurement ROI 124

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. The doctor uploads the patient’s medical history and recent test results to an AGI-powered medical analysis system. Example : A patient visits a doctor with concerning symptoms.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

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. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. Testing out a new feature. Form a hypothesis.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

DECEMBER 4, 2020

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). Once all packages have been imported, we can move on to loading our test data. TIME – time points of measured pain score and plasma concentration (in hrs). and 3 to 8 hours.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Ontotext

APRIL 3, 2024

So, this is a big driver for the outcome because when you are saving money for the business, you can measure it and see its value. Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. These are not statistical inferences. It’s open.

Technology 59

Technology Risk Digital Transformation Measurement 59

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. The case that might be familiar to you is an AB test. You can make a change to a product and test it against the original version of the product. A complementary Domino project is available. . Introduction.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Alation

SEPTEMBER 28, 2022

Automated testing to ensure data quality. Alation provides robust DataOps solutions that help you foster collaboration, build trusted data solutions, automate testing & monitoring, and visualize data pipelines. Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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

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. provide an opportunity to measure both. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face. On my test set, this approach resulted in~75–95% accuracy and ~.65

Modeling 59

Modeling Metadata Publishing Sales 59

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

O'Reilly on Data

MARCH 23, 2023

Tokens ChatGPT’s sense of “context”—the amount of text that it considers when it’s in conversation—is measured in “tokens,” which are also used for billing. It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test. Tokens are significant parts of a word.

IT 262

IT Modeling Testing Risk 262

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. These measurement-obsessed companies have an advantage when it comes to AI. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

JULY 31, 2016

The analysis can be straightforward, especially when it's safe to assume that individual observations of an outcome measure are independent. At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. When analyzing the outcome measure (e.g.,

Experimentation 42

Experimentation Testing Measurement Metrics 42

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). 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. It is a book about Web Analytics 2.0. Qualitative and quantitative. Clicks and outcomes.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. For example, we might want to stop the process if we measure harmful effects early. What if their uptake rate is not uniform?

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Domino Data Lab

MAY 8, 2019

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Information can get quite distorted after being abstracted that many times.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science. It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

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). ]. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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. We use the diagnostic test results of our regression model to support the reasons why CIs should not be used in financial data analyses.

Statistics 27

Statistics Uncertainty Risk Marketing 27

Occam's Razor

JANUARY 9, 2012

Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns. AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance! One of my biggest learnings?

Marketing 140

Marketing B2B Measurement Risk 140

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. If we don’t like the result of testing after training, we can also select an entirely different learning machine with different internals. We can take this model to other, novel data and perform a final hold-out test assessment.

Testing 79

Testing Modeling Machine Learning Metrics 79

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