Analytics Data Mining Power BI 
Data Science Blog
JULY 23, 2023
Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Process Mining offers process transparency, compliance insights, and process optimization. Each applications has its own data model.
Analytics Vidhya
SEPTEMBER 30, 2021
This article was published as a part of the Data Science Blogathon Introduction I have been associated with Analytics Vidya from the 3rd edition of Blogathon. The post Guide For Data Analysis: From Data Extraction to Dashboard appeared first on Analytics Vidhya.
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Dataconomy
MAY 10, 2023
Open source business intelligence software is a game-changer in the world of data analysis and decision-making. It has revolutionized the way businesses approach data analytics by providing cost-effective and customizable solutions that are tailored to specific business needs.
Dataconomy
MARCH 5, 2025
As the demand for data expertise continues to grow, understanding the multifaceted role of a data scientist becomes increasingly relevant. What is a data scientist? A data scientist integrates data science techniques with analytical rigor to derive insights that drive action.
IBM Journey to AI blog
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 Blog
NOVEMBER 15, 2023
New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.
Dataconomy
JUNE 13, 2023
At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.
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