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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

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Women on the rise in Data Science

IBM Big Data Hub

IBM Data Science and AI Elite team members Mehrnoosh Vahdat and Rachael Dottle were just one month into their IBM careers when they received their first assignment last July. . The project jettisoned them into the heart of Africa, where their banking client was looking to surface new business opportunities across the subcontinent.

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Keep the train rolling: partner momentum in the data science market

IBM Big Data Hub

How has the newer data science technology such as Watson Studio, Watson Machine Learning and Watson OpenScale been picked up by the business partner community? I mentioned in our previous blog that I was pleasantly surprised at how many IBM Business Partners have established a Data Science practice.

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Data science: Breaking down the silos

IBM Big Data Hub

Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:

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Protecting Big Data, while Preserving Analytical Agility

Thales Cloud Protection & Licensing

The age of Big Data is upon us. And, as more data is available for analytical purposes, more sensitive and private information is at risk. Protecting the confidentiality and integrity and of warehoused data and ensuring that access is controlled is vital to keeping that data secure. respondents.”.