The Unofficial Google Data Science Blog

JULY 18, 2023

by MICHAEL QUINN, JEREMY MILES, KA WONG As data scientists, we often encounter situations in which human judgment provides the ground truth. Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common. Some of this data is human-labeled.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

FEBRUARY 12, 2020

Introduction. This post is for people making technology decisions, by which I mean data science team leads, architects, dev team leads, even managers who are involved in strategic decisions about the technology used in their organizations. If you want a more in-depth technical introduction to Ray, see ?this that work best.

Experimentation 137

Experimentation Modeling Data Science Management 137

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Companies, both big and small, are seeking the finest ways to leverage their data into a competitive advantage.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. finding drivers of sales) are also important.

Forecasting 64

Forecasting Statistics Modeling Software 64

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Introduction. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Of course!!”

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. Introduction. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. What’s a Foo?

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Sisense

DECEMBER 11, 2019

With data growing at a staggering rate, managing and structuring it is vital to your survival. In this piece, we detail the Israeli debut of Periscope Data. Driving startup growth with the power of data. Driving startup growth with the power of data. It’s the aspiration of every startup. What VCs want from startups.

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Data Lake Big Data Sales Data-driven 80

Domino Data Lab

AUGUST 22, 2019

Introduction. Introduction. 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.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Edwin Chen

JULY 30, 2012

I wanted to play around with the data a bit first just to get a rough feel, so I made an app to interact with the network around each node. A couple weeks ago, Facebook launched a link prediction contest on Kaggle, with the goal of recommending missing edges in a social graph. Exploration. What does the network look like?

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Metrics Testing Machine Learning Modeling 81

Depict Data Studio

MAY 8, 2023

All of these classes fall under the broad data communications umbrella. One class focuses on data analysis (cleaning and tabulating our raw datasets to get them ready for graphs). Class 1: Simple Spreadsheets: How to Analyze Data from Start to Finish in Excel Need to make sense of spreadsheets? Not sure where to start?

Dashboards 52

Dashboards Visualization Reporting Interactive 52

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

by ERIC HOLLINGSWORTH A large part of the data we data scientists are asked to analyze was not collected with any specific analysis in mind, or perhaps any particular purpose at all. This post describes the analytical issues which arise in such a setting, and what the data scientist can do about them.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Ontotext

FEBRUARY 2, 2021

Of course, Reddit’s WallStreetBets forum is not a pioneer in the field. However, it is difficult to map the r/WallStreetBets events to the classic financial fraud models. Fraud or Civic Position – the Effect is the Same. 4) that the accused caused the artificial prices. 4) that the accused caused the artificial prices.

Marketing 59

Marketing Measurement Cost-Benefit Risk 59