CIO Business Intelligence

APRIL 22, 2022

The exam covers topics including Scrum, Kanban, Lean, extreme programming (XP), and test-driven development (TDD). 11, 2001, terrorist attacks to address issues of cyberterrorism and the information security of nations at large. Candidates can also take a self-test to benchmark their knowledge. Certified ScrumMaster (CSM).

Management 107

Management Cost-Benefit Testing Risk 107

Jet Global

APRIL 13, 2020

In 2001, a group of software developers got together at a ski resort in the Wasatch mountains of Utah and drew up a document they called the “Agile Manifesto.” They rejected the classic waterfall model of software development in favor of an iterative approach in which initial prototypes are delivered and tested early in the process.

Manufacturing 98

Manufacturing Reporting Finance Forecasting 98

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere. It’s a mess.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. the curse of dimensionality).

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists. Across the board, organizations struggle with hiring enough data scientists. Sci Foo 2019.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71