Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

Data Governance 84

Data Governance Machine Learning Metadata Data Science 84

Insight

APRIL 16, 2019

million by 2021 , a more than 300% increase since 2014. from computer science leaders to Fortune 500 executives to the White House?—? Fellows will join a network of ambitious software engineers, systems engineers, IT professionals, and data analysts who share common goals, but wield a diverse set of complementary skills.

Machine Learning 68

Machine Learning Software Risk Reporting 68

Insight

JANUARY 14, 2020

For example, if you notice that, during training, your evaluation metric for the test set suddenly begins to plummet but the metric for the training set remains high, that’s probably a sign that your model is overfitting. The VGG16 architecture was the runner-up in the ILSVRC-2014 challenge. In the next section, we’ll find out!

Metrics 58

Metrics Optimization Modeling Testing 58

The Unofficial Google Data Science Blog

JUNE 30, 2016

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. 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. 2014): 243-263.

Statistics 44

Statistics Optimization Modeling Experimentation 44

CIO Business Intelligence

APRIL 15, 2022

But according to the UK’s Turing Institute, a national center for data science and AI, the predictive tools made little to no difference. MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. In a statement on Oct. In a statement on Oct.

Analytics 145

Analytics Machine Learning Insurance Testing 145

IBM Big Data Hub

NOVEMBER 13, 2023

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

Machine Learning 107

Machine Learning Data-driven Software Testing 107

Data Science and Beyond

OCTOBER 15, 2017

Do you know what data science is? Do you understand what data scientists do? Just so we’re on the same page, what is data science? 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.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Sisense

FEBRUARY 24, 2021

In this article, we explore the role and responsibilities of the chief data officer and the challenges they are facing. The role of the chief data officer. Not all organizations are at the same point in their data journey. This situation can quickly pose a dilemma for the chief data officer.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

Domino Data Lab

DECEMBER 2, 2020

Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. Rather it infers the number of clusters based on the data, and it can discover clusters of arbitrary shape (for comparison, k-means usually discovers spherical clusters).

Metrics 116

Metrics KDD Testing Machine Learning 116

The Unofficial Google Data Science Blog

APRIL 23, 2024

Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments. Multiparameter experiments, however, generate richer data than standard A/B tests, and automated t-tests alone are insufficient to analyze them well. Testing Statistical Hypotheses.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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. At the time—in 2014—the three were colleagues working.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

AUGUST 1, 2021

Joint training, for example, adds an additional “explanation task” to the original problem and trains the system to solve the two “jointly” (see Bahdanau, 2014). After forming the X and y variables, we split the data into training and test sets. layer-wise relevance propagation), model distillation (e.g.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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