Data Science and Beyond

APRIL 5, 2021

I’ve been working remotely with Automattic since 2017, so I was pretty covid-ready as far as work was concerned. The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. Remote work.

Experimentation 61

Experimentation Testing Publishing Data Collection 61

Ontotext

MAY 18, 2020

The capacity and performance of supercomputers is measured with the so-called FLOPS (floating point operations per second). As of 2017, the fastest computers have reached a speed of 93 PetaFLOPS, which is: 93×1015, or 93,000,000,000,000,000 operations per second. There are four types of data sources that the team will work with.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

erwin

DECEMBER 20, 2018

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. The Top 6 Benefits of Data Governance.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

The Unofficial Google Data Science Blog

APRIL 17, 2017

First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. They can arise from data collection errors or other unlikely-to-repeat causes such as an outage somewhere on the Internet. Accessed on 20 March 2017.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. The first step to collecting all of the data is to figure out which data source to collect first, and where to get it. ggtitle("NBA Teams 2016-2017 Faceted Plot"). The R-squared value of.282

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? The ability to measure results (risk-reducing evidence).

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

Domino Data Lab

AUGUST 1, 2021

but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. See Wei et al. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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

Occam's Razor

OCTOBER 12, 2017

Ask them what they worry about, ask them what they are solving for, ask them how they measure success, ask them what are two things on the horizon that they are excited about. The Future of Life Institute hosted a conference in Asilomar in Jan 2017 with just such a purpose. This is not the future, it is Nov 2017.

Marketing 138

Marketing Analytics Machine Learning Strategy 138