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6 AI tools revolutionizing data analysis: Unleashing the best in business

Data Science Dojo

To address this challenge, businesses need to use advanced data analysis methods. These methods can help businesses to make sense of their data and to identify trends and patterns that would otherwise be invisible. In recent years, there has been a growing interest in the use of artificial intelligence (AI) for data analysis.

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Using Generative AI for Data Analysis and Visualization

ODSC - Open Data Science

From uncovering hidden patterns to providing actionable recommendations, generative AI’s proficiency in data analytics heralds a new era where innovation spans the spectrum from artistic expression to informed business strategies. So let’s take a brief look at some examples of how generative AI can be used for data analytics.

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Enterprise fraud management, AI and data visualization

Cambridge Intelligence

While it has always been a data-intensive process, enterprise fraud management today is more complex and more important than ever. This blog post explores the evolving role of enterprise fraud management, examines the impact of AI and showcases the importance of visual data analysis. What is enterprise fraud management?

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Log data visualization for enterprise observability

Cambridge Intelligence

In this blog post, Ill show how effective log data visualization improves your enterprise observability workflows. Youll see how KronoGraph, our timeline visualization SDK , can work alongside your log table, or replace it entirely. With KronoGraph log data visualization, observing log sources becomes instantaneous.

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AI and data visualization: Powering the intelligence cycle

Cambridge Intelligence

Artificial intelligence is changing the way organizations think about data analysis. AI and data visualization can do a lot of the heavy lifting for analysts and investigators working with connected data – helping them detect, understand and even predict risks and threats.

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Announcing new Tableau Data Literacy curriculum for higher education

Tableau

Working with data is a necessity for most jobs, and analytical skills can be a huge differentiator for success across all kinds of work—whether corporate, nonprofit, or academic. Without closing the data skills gap, these challenges would continue into their professional lives.

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5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists

ODSC - Open Data Science

You can perform data analysis within SQL Though mentioned in the first example, let’s expand on this a bit more. SQL allows for some pretty hefty and easy ad-hoc data analysis for the data professional on the go. Imagine combining the data power of SQL with your preferred scripting program.

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