Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

Rocket-Powered Data Science

JULY 6, 2021

Techniques that both enable (contribute to) and benefit from smart content are content discovery, machine learning, knowledge graphs, semantic linked data, semantic data integration, knowledge discovery, and knowledge management.

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

FineReport

DECEMBER 19, 2019

For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. From Google. There are two points here.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Domino Data Lab

JUNE 15, 2021

These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical data science journey for a company starts with a small team that is tasked with a handful of specific problems.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

JULY 22, 2020

A/B testing isn’t simple just because data is big — the law of large numbers doesn’t take care of everything! Even with big data, A/B tests require thinking deeply and critically about whether or not the assumptions made match the data. Henne, and Dan Sommerfield. 2] Scott, Steven L. 2015): 37-45. [3] ACM, 2017. [4]

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

We could do this but in our big data world, we would avoid materializing such an inefficient structure by reducing the regression to its sufficient statistics. When solved with an intercept term, regression coefficients for the binary predictors are maximum likelihood estimates for the experiment effects under assumption of additivity.

Experimentation 40

Experimentation Metrics Statistics Measurement 40