Rocket-Powered Data Science

JULY 7, 2019

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). They cannot process language inputs generally. Industry 4.0 Examples: (1) Automated manufacturing assembly line. (2)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

Experimentation 103

Experimentation Testing Optimization Measurement 103

Ontotext

FEBRUARY 14, 2024

How do you measure its utility? As a statistical model, LLM inherently is random. Experimentation is important, but be explicit when you do. There’s a famous saying by a statistician, George Box, “All models are wrong, but some are useful.” ” So, how do you know whether your model is useful?

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

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

DataRobot Blog

JULY 28, 2022

For example, data measured by sensors can contain all kinds of noise due to sensor malfunctions, environmental changes, etc., If the various observed values measured by sensors in the equipment could be predicted, it would be possible to control manufacturing parameters and reduce fuel costs. which can lead to large prediction errors.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

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

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. They measured both the blood pressure of the participants and if they had a heart attack or not.

Testing 104

Testing Risk Data-driven Statistics 104

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. With A/B testing, we can validate various hypotheses and measure the impact of our product changes, allowing us to make better decisions. This could create confusion.

Testing 58

Testing Experimentation Measurement Modeling 58

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

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. What is a customer?

Technology 59

Technology Risk Digital Transformation Measurement 59

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.). TIME – time points of measured pain score and plasma concentration (in hrs). We can group by study arm and calculate various statistics as mean and standard deviation. pain_df.TIME.==

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

Cost-Benefit 87

Cost-Benefit Modeling Manufacturing Interactive 87

Data Science and Beyond

DECEMBER 23, 2018

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. As the authors show, even with randomised experiments, the analysis often requires using observational causal inference tools due to factors like selection and measurement biases.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Occam's Razor

APRIL 8, 2013

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. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You’ll measure this effect by looking at a quantity called the average treatment effect (ATE). What you really want to measure is the difference in outcomes. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

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

Alation

SEPTEMBER 28, 2022

In DataOps, data analytics performance is primarily measured through insightful analytics, and accurate data, in robust frameworks. Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. Value working analytics. Embrace change. One key application of DataOps is customer-centricity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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. One way that we can get around this is to use the proportion of tags that fall into a given class as a measure of our degree of confidence in that class association. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Occam's Razor

JULY 24, 2018

To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0. During a discussion around planning for measurement, a peer was struggling with a unique collection of challenges. You see more digital metrics because digital is more measurable. Especially for the non-obvious problem #2 above.

Analytics 93

Analytics Metrics Strategy Measurement 93

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

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. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

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. The outcome measure we care about is an average of the students' test scores, and so the unit of observation is a student. To figure this out, let's consider an appropriate experimental design.

Experimentation 42

Experimentation Testing Measurement Metrics 42

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

MAY 31, 2016

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. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

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.

Management 110

Management Machine Learning Experimentation Metrics 110

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). If you have fifteen years of experience you'll still learn loads from chapters that cover holistic search analytics (internal, SEO, SEM/PPC) and Statistical Significance and Multi Channel Marketing Analytics and Advanced Conversion Rate measurement and more.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. And it can look up an author and make statistical observations about their interests. Tokens are significant parts of a word.

IT 260

IT Modeling Testing Risk 260

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

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

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. Part of it fueled by some Consultants. I suppose the rational is: self preservation before all else. It has simply not had a break to catch a breath and mature.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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

Occam's Razor

FEBRUARY 8, 2021

From observing behavior closely, and from my own experimentation and failure, I've noticed consistent patterns in what great employees do and great bosses do. They find the external author of the statistical algorithm I want them to use, and ask them for guidance. They do more research than is required. Invest in yourself.

Strategy 132

Strategy Statistics Cost-Benefit Marketing 132

Cloudera

NOVEMBER 25, 2020

And soon also sensor measures, and possibly video or audio data with the increased use of device technology and telemedicine in medical care. How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data?

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57