The Unofficial Google Data Science Blog

APRIL 17, 2017

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Smarten

NOVEMBER 19, 2018

Today’s self-serve predictive analytics and forecasting tools are designed to support business users and data analysts alike. Predictive analytics is the process of forecasting or predicting business results for planning purposes. No longer is this process the sole responsibility of data scientists or IT staff.

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

Sisense

DECEMBER 3, 2019

Data is the New Oil” was coined by The Economist in May 2017 and became a mantra for organizations to drive new wealth from data. Optimize raw data using materialized views. In-Warehouse Data Prep delivers builders advanced functionality to rapidly transform and optimize raw data using materialized views on cloud data warehouses.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

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. Forecasting (e.g. The other systems were written to do "forecasting at scale," a phrase that means something different in time series problems than in other corners of data science. by STEVEN L.

Forecasting 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. The first is that they are straightforward to optimize using traditional gradient-based optimizers as long as we pre-specify the placement of the knots. There is a robust set of tools for working with these kinds of constrained optimization problems.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

datapine

NOVEMBER 19, 2019

Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. billion in 2017 to $190.61 Blockchain.

Technology 279

Technology Internet of Things IT IoT 279

Smarten

SEPTEMBER 6, 2019

In the meantime, business users have a tool that is sophisticated enough to present clear, accurate, measurable results and allow them to find the source of problems, optimize results and share data to support business decisions. Self-serve tools allow users to leverage knowledge and skill and better perform against forecasts and plans.

Analytics 51

Analytics IT Business Intelligence Testing 51