Alation

SEPTEMBER 28, 2022

DataOps as a term was brought to media attention by Lenny Liebmannin 2014, then popularized by several other thought leaders. Automated testing to ensure data quality. Quality must be monitored continuously to catch unexpected variation cases and produce statistics on its operation. Source: Google Trends.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

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. 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

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. e-handbook of statistical methods: Summary tables of useful fractional factorial designs , 2018 [3] Ulrike Groemping.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Depict Data Studio

NOVEMBER 4, 2022

Can I hire you to help me prep for the Excel tests that I’ll have to take as part of the hiring process?” ” I’d been a formal statistics tutor and Spanish tutor in college through a small invite-only program. People either think the Excel test is way too hard or way too easy. I didn’t create the test!!

Dashboards 75

Dashboards Testing Software Consulting 75

Data Science and Beyond

OCTOBER 15, 2017

Here are my thoughts from 2014 on defining data science as the intersection of software engineering and statistics , and a more recent post on defining data science in 2018. Do you understand what data scientists do? Just so we’re on the same page, what is data science? No one knows for sure.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

datapine

FEBRUARY 23, 2023

2) Charts And Graphs Categories 3) 20 Different Types Of Graphs And Charts 4) How To Choose The Right Chart Type Data and statistics are all around us. That said, there is still a lack of charting literacy due to the wide range of visuals available to us and the misuse of statistics. Table of Contents 1) What Are Graphs And Charts?

Visualization 177

Visualization Dashboards Sales Measurement 177

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] At the time—in 2014—the three were colleagues working. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. Relative to extrinsic evaluations, intrinsic tests are quick.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Depict Data Studio

APRIL 10, 2023

and implications of findings) than in statistical significance. Apply the Squint Test In these before scatter plot on the left, the cluttered appearance distracts us from the data. Apply the Squint Test. I like to test my drafts ahead of time to make sure they’ll still be legible even if they’re printed in grayscale.

Visualization 105

Visualization Dashboards Testing Reporting 105

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. .”

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

in 2008 and continuing with Java 8 in 2014, programming languages have added higher-order functions (lambdas) and other “functional” features. In the past decade, a lot of ideas and technologies have come out of the DevOps movement: the source repository as the single source of truth, rapid automated deployment, constant testing, and more.

Machine Learning 363

Machine Learning Software Testing Technology 363