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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

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.

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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? This post will dive deeper into the nuances of each field.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? When used strategically, text-mining tools can transform raw data into real business intelligence , giving companies a competitive edge. How does text mining work?

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3 new steps in the data mining process to ensure trustworthy AI

IBM Big Data Hub

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. Data risk assessment. Detecting and defining bias and unfairness isn’t easy.

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How to unlock a scientific approach to change management with powerful data insights

IBM Big Data Hub

Grasping these opportunities at IBM, we’re increasingly building our specialism in process mining and data analysis tools and techniques we believe to be true ‘game changers’ when it comes to building cultures of continuous change and innovation. It can show whether perceptions are real, as well as unearthing unexpected insights.

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Embeddable AI saves time building powerful AI applications

IBM Big Data Hub

So, it’s crucial for companies that want to add specific AI capabilities to their applications or workflows to do so without expanding their technology stack, hiring more data science talent, or investing in expensive supercomputing resources. Users can then mine the data using simple keyword searches to find the information they need.