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. Since you already understand the definition of causal effect and the difference between association and causation, do not expect to gain deep conceptual insights from this chapter.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Occam's Razor

AUGUST 22, 2011

Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. " The Definitive Guide To (8) Competitive Intelligence Data Sources! Tip #9: Leverage Statistical Control Limits. Tip#1: Statistical Significance. Web Analytics Standards: 26 New Metrics Definitions.

KPI 124

KPI Metrics Measurement ROI 124

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. This is significant and definitely worth watching over the next few months. It’s more concerning that workflow reproducibility (3%) is in second-to-last place. Bottlenecks to AI adoption.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Ontotext

APRIL 3, 2024

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. That will definitely stick. It’s a great use case because semantics can both accelerate things and make them compliant. What is a customer? What is fraud?

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. What you’re seeing is a general rule: when two variables share a common cause, they will be correlated (or, more generally, statistically dependent) even when there’s no causal relationship between them.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. The team did the normal modeling to ensure that the results were statistically significant (large enough sample set, sufficient number of conversions in each variation). * ask for a raise. It is that simple. Okay, it is not simple.

Measurement 119

Measurement Advertising Testing Marketing 119

Occam's Razor

APRIL 8, 2013

The word hypothesis means a lot of different things, but in this context I like this definition from Wikipedia the best: People refer to a trial solution to a problem as a hypothesis, often called an "educated guess”, because it provides a suggested solution based on the evidence. The result? The graph is impressive, right?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Occam's Razor

JUNE 4, 2018

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount. Nudging has plenty of different definitions. I don’t mean: BUY IT NOW OR ELSE! Sizing the Opportunity. It will be heartbreaking, and now you are ready for progress! Welcome to Nudging.

Strategy 124

Strategy Cost-Benefit Testing Sales 124

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. This article is a short summary of my understanding of the definition of data science in 2018. Recent definitions by Hernán, Hawkins, and Dubossarsky.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Corinium

APRIL 25, 2019

Yes, definitely! For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. It definitely depends on the type of data, no one method is always better than the other. What do you recommend to organisations to help them overcome these?

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Occam's Razor

JULY 24, 2018

Ignore the metrics produced as an experimental exercise nine months ago. You’ll certainly need new talent, you’ll need a stronger strategic leader (less ink, more think ), you’ll need to identify specific analytics projects to deliver those metrics, and you’ll most definitely need funding.

Analytics 93

Analytics Metrics Strategy Measurement 93

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). The book, like this blog, rips up that definition and provides a expanded and more realistic business focused world view.

Analytics 60

Analytics Metrics Marketing Dashboards 60

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. Ideally, data provenance , data lineage , consistent data definitions , rich metadata management , and other essentials of good data governance would be baked into, not grafted on top of, an AI project. But what kind? or function-as-a-service designs.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

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

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

In contrast, some researchers who are exploring the definitions, possibilities, and limitations of model interpretation point toward more comprehensive views. There’s no one-size-fits-all definition of what’s required across all verticals. 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

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. and an error term ??

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

By definition, it is hard to disentangle causes and effects in these settings because it is rarely possible to isolate all relevant factors. 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

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

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. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Part of it fueled by some Consultants.

Analytics 118

Analytics Consulting Metrics Experimentation 118

CIO Business Intelligence

JULY 6, 2023

After completing MTech from Indian Statistical Institute, I started my career at Cognizant. What do you do to foster a culture of innovation and experimentation in your employees? Only experimentation can help to improve this index. Overall, my career has spanned 20 years. This is what makes the job most interesting.

Digital Transformation 98

Digital Transformation Experimentation Recreation/Entertainment Technology 98

Depict Data Studio

MARCH 27, 2023

About the Instructor: Stefanie Posavec According to Stefanie’s website , she is “a designer, artist, and author exploring experimental approaches to communicating data and information to all ages and audiences.” I was on my treadmill, walking 10 miles during each of the classes, so my brain was definitely on Beast Mode. and Canada. —I

Visualization 52

Visualization Consulting Dashboards Experimentation 52