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Ways of Converting Textual Data into Structured Insights with LLMs

Analytics Vidhya

Introduction In the era of big data, organizations are inundated with vast amounts of unstructured textual data. The sheer volume and diversity of information present a significant challenge in extracting insights.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? What is machine learning?

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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9 Careers You Could Go into With a Data Science Degree

Smart Data Collective

The average data scientist earns over $108,000 a year. The interdisciplinary field of data science involves using processes, algorithms, and systems to extract knowledge and insights from both structured and unstructured data and then applying the knowledge gained from that data across a wide range of applications.

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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can stay effective over time.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Usually, business or data analysts need to extract insights for reporting purposes, so data warehouses are more suitable for them. On the other hand, a data scientist may require access to unstructured data to detect patterns or build a deep learning model, which means that a data lake is a perfect fit for them.

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The most valuable AI use cases for business

IBM Big Data Hub

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.