Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Tag your mobile website.

Metrics 141

Metrics Reporting Analytics Marketing 141

CIO Business Intelligence

MAY 16, 2023

higher [in 2022] than in 2017.” The inherent capabilities of AI–to process vast amounts of data and use learned intelligence to make decisions with extraordinary speed–enable opportunities uncovered through digital listening. McKinsey & Company’s 2022 Global Survey on AI says , “AI adoption globally is 2.5x

Advertising 92

Advertising Recreation/Entertainment Experimentation Enterprise 92

Domino Data Lab

AUGUST 1, 2021

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

OCTOBER 23, 2019

The first step to collecting all of the data is to figure out which data source to collect first, and where to get it. Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. 35,vjust=1).

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? Katherine Twomey, Gert Westermann (2017). This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. HBR Review May/June 2017.

Metadata 64

Metadata Data Governance Dashboards Software 64