Business Analytics Data Science Predictive Modeling 
datapine
AUGUST 14, 2019
Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.
Smarten
SEPTEMBER 14, 2018
Just Simple, Assisted Predictive Modeling for Every Business User! No matter the market or type of business, there is no room in today’s business landscape for guesswork. And, with Assisted Predictive Modeling , you can make these tasks even easier. No Guesswork!
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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.
CIO Business Intelligence
JUNE 7, 2022
More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics vs. data analysis.
Cloudera
NOVEMBER 18, 2020
Experian sifts through hundreds of millions of records daily to serve both consumers and businesses with vital credit score, report, and credit comparisons. To ensure maximum momentum and flawless service the Experian BIS Data Enrichment team decided to use the power of big data by utilizing Cloudera’s Data Science Workbench.
Smarten
JANUARY 27, 2023
Smarten has announced the launch of Predictive Model Mark-Up Language (PMML) Integration capability for its Smarten Augmented Analytics suite of products. Simply create the predictive model, using your favorite platform, export the model as a PMML file and import that model to Smarten.
Cloudera
MAY 12, 2021
Use cases could include but are not limited to: optimizing healthcare processes to save lives, data analysis for emergency resource management, building smarter cities with data science, using data and analytics to fight climate change, tackle the food crisis or prioritize actions against poverty, and more.
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