Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Occam's Razor

SEPTEMBER 19, 2011

How do you get over the frustration of having done attribution modeling and realizing that it is not even remotely the solution to your challenge of using multiple media channels? You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. ask for a raise. It is that simple.

Measurement 119

Measurement Advertising Testing Marketing 119

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Features: DistilBERT Text Embeddings Once I had a raw dataset, I could begin engineering features and building a natural language processing (NLP) model.

Modeling 59

Modeling Metadata Publishing Sales 59

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

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

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 94

Cost-Benefit Modeling Manufacturing Interactive 94

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

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

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. Maybe later.

Technology 59

Technology Risk Digital Transformation Measurement 59

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

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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!]. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. How Do I Measure Success? What's The Fix?

KPI 124

KPI Metrics Measurement ROI 124

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). You can use a regression model. model = OLS(X['Y'], X[['D', 'intercept']]) result = model.fit() result.summary().

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

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

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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. This is a much more proactive and scalable model. And more recently, we have also seen innovation with IOT (Internet Of Things).

Insurance 150

Insurance Risk IoT Cost-Benefit 150

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

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

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

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment.

Statistics 27

Statistics Uncertainty Risk Marketing 27

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

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Implications of Complexity.

Analytics 93

Analytics Metrics Strategy Measurement 93

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters.

Testing 79

Testing Modeling Machine Learning Metrics 79

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

MARCH 23, 2023

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. with specialized training.

IT 262

IT Modeling Testing Risk 262

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? Matthew’s company is not alone in this situation.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

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. Our mental model has not shifted enough to the existing reality. Allocate some of your aforementioned 15% budget to experimentation and testing.

Marketing 140

Marketing B2B Measurement Risk 140

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