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Data mining

Dataconomy

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Businesses across various sectors are leveraging data mining to gain a competitive edge, improve decision-making, and optimize operations.

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Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

You may not even know exactly which path you should pursue, since some seemingly similar fields in the data technology sector have surprising differences. We decided to cover some of the most important differences between Data Mining vs Data Science in order to finally understand which is which. What is Data Science?

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Understand Text Mining Using No Code Tool Orange

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Text Mining is also known as Text Data Mining or Text Analytics or is an artificial intelligence (AI) technology that uses natural language processing (NLP) to extract essential data from standard language text.

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Mastering the 10 Vs of big data 

Data Science Dojo

To avoid such consequences, it’s important to be mindful of the information we share online. Visualization With a new data visualization tool being released every month or so, visualizing data is key to insightful results. Both Data Mining and Big Data Analysis are major elements of data science.

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Navigate your way to success – Top 10 data science careers to pursue in 2023

Data Science Dojo

Data Analyst Data analysts are responsible for collecting, analyzing, and interpreting large sets of data to identify patterns and trends. They require strong analytical skills, knowledge of statistical analysis, and expertise in data visualization.

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6 Ways Business Intelligence is Going to Change in 2017

Dataconomy

Data-driven businesses are five times more likely to make faster decisions than their market peers, and twice as likely to land in the top quartile of financial performance within their industries. The post 6 Ways Business Intelligence is Going to Change in 2017 appeared first on Dataconomy.

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Enterprise fraud management, AI and data visualization

Cambridge Intelligence

Fraud prevention The third stage of the visualization-AI intelligence cycle is prevention – where data science teams use new information to train their models. This might include larger-scale data mining to gain insights on wider trends from multiple investigations.