IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

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 141

Data mining Data-driven Statistics OLAP 141

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.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

Birst BI

OCTOBER 30, 2018

The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. Consider all customer interactions and their data sources as potential sources for predicting future customer behavior. Integrate the data sources of the various behavioral attributes into a functional 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

AWS Big Data

MARCH 26, 2024

Unified customer profile Graph databases excel in modeling customer interactions and relationships, offering a comprehensive view of the customer journey. Strategize based on how your teams explore data, run analyses, wrangle data for downstream requirements, and visualize data at different levels.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71