IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

Data mining 126

Data mining Data-driven Statistics OLAP 126

FineReport

JULY 28, 2021

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Birst BI

OCTOBER 30, 2018

The foundation of predictive analytics is based on probabilities. To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. Add the predictive logic to the data model.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Andrew White

JANUARY 11, 2021

As such any Data and Analytics strategy needs to incorporate data sovereignty as per of its D&A governance program. Coding skills – SQL, Python or application familiarity – ETL & visualization? See recorded webinars: Emerging Practices for a Data-driven Strategy. Link Data to Business Outcomes.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52