O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Identifying the problem.

Marketing 361

Marketing Experimentation Metrics Testing 361

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. For example, imagine a fantasy football site is considering displaying advanced player statistics. Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

APRIL 21, 2021

Historically, fraud detection has been performed by analysts who are responsible for identifying and tracing suspicious patterns in the data. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smart Data Collective

JANUARY 31, 2022

Understanding the Role of Data-Driven SEO and SEO. billion websites in existence, any website owner interested in improving their visibility in the search results and optimal usability needs to take data seriously during the design process. Types of Data Used in Web Design. Also, considering that there are over 1.8

Data-driven 101

Data-driven Big Data Statistics Optimization 101

Insight

AUGUST 5, 2020

In the context of Maslo’s products, presumption of mood through audio recording data can not only provide interesting mood-related insights but also supplement and validate current mood prediction pipelines at Maslo. Summary statistics (i.e. Humans show a myriad of explicit and implicit emotional signals in our behaviors.

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

CIO Business Intelligence

JULY 20, 2022

The problem: She had no formal training and experience in computer science. Now a benefits engineer as part of Gusto’s Engineering Product Design and Data department, McGrath puts time in mentoring other women interested in making similar career changes. What might be a hurdle for many did not stop McGrath or her employers at Gusto.

IT 132

IT Software Cost-Benefit Strategy 132

Domino Data Lab

MAY 8, 2019

Admittedly, throughout large swaths of computer science, reductionism serves quite well. Admittedly, throughout large swaths of computer science, reductionism serves quite well. For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Rita Sallam

APRIL 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. The Bake-Offs are fast-paced, fun and informative sessions that let you see leading vendors side-by-side using scripted demos and a common data set in a controlled setting.

Optimization 52

Optimization Machine Learning Insurance Risk 52

The Unofficial Google Data Science Blog

JULY 18, 2023

Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common. Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JULY 17, 2021

The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Unlike fingerprint detection, retinal scans or face recognition, speaker recognition just uses a microphone to record a person’s voice thereby circumventing the need for expensive hardware.

Modeling 64

Modeling Testing Machine Learning Measurement 64

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

by ERIC HOLLINGSWORTH A large part of the data we data scientists are asked to analyze was not collected with any specific analysis in mind, or perhaps any particular purpose at all. This post describes the analytical issues which arise in such a setting, and what the data scientist can do about them.

Experimentation 61

Experimentation Testing Statistics Metrics 61

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

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. A complementary Domino project is available. . Introduction. Chapter Introduction: Causal Inference.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

The Unofficial Google Data Science Blog

MAY 31, 2016

Observational data such as paid clicks, website visits, or sales can be stored and analyzed easily. However, it is generally not possible to determine the incremental impact of advertising by merely observing such data across time. When it comes to online ads, there is also a fundamental need to estimate the return on investment.

Advertising 42

Advertising Testing Sales Statistics 42

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. author of the Harry Potter series?—?sales

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

OCTOBER 23, 2019

It covers questions to consider as well as collecting, prepping and plotting data. Collecting and prepping data are core research tasks. While the most ideal situation is to start a project with clean well-labeled data, the reality is that data scientists spend countless hours on obtaining and prepping data.

Statistics 59

Statistics Machine Learning Testing Modeling 59

The Unofficial Google Data Science Blog

MARCH 31, 2016

Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. by NICHOLAS A. hi-fly-airlines 123.com 1 1 1 1.10

Modeling 40

Modeling Statistics Advertising Testing 40

Insight

MARCH 13, 2020

Learn how the best teams adapt to interviewing while they WFH At Insight , we partner with companies to build and scale their tech teams. In the last few weeks, teams that we work with have been transitioning to remote work to keep their teams safe and do what they can to limit the spread of coronavirus. Interested in more advice?

Scorecard 85

Scorecard Statistics IT Testing 85

Andrew White

JANUARY 11, 2021

It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. The fill report is here: Leadership Vision for 2021: Data and Analytics. This was in fact an overview of a piece of research we update every year. It really does.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

The Fundamental Problem of Causal Inference The primary goal of many data analysis efforts is to estimate the effect of an intervention on the distribution of some outcome. To do this, you have a data set at the person level containing, among other variables, an indicator of ad exposure, and whether the person bought the truck.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

JULY 31, 2016

At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. For example, you might compare the average test score among students exposed to the new curriculum to the average score among the controls. However, this is not always the case.

Experimentation 42

Experimentation Testing Measurement Metrics 42

Domino Data Lab

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. A complementary Domino project is available. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111