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In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to datascience and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.
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This article serves as a detailed guide on how to master advanced Python techniques for datascience. It covers topics such as efficient data manipulation with Pandas, parallel processing with Python, and how to turn models into web services.
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Introduction The field of datascience is evolving rapidly, and staying ahead of the curve requires leveraging the latest and most powerful tools available. In 2024, data scientists have a plethora of options to choose from, catering to various aspects of their work, including programming, big data, AI, visualization, and more.
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In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.
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In this article, Im going to share datascience project ideas that will actually help you stand out. These are creative projects that solve problems with data, and Ive included source code and tutorials to help you replicate them.
This article is excerpted from the book, “Winning with DataScience: A Handbook for Business Leaders,” by Howard Friedman and Akshay Swaminathan with permission from the publisher, Columbia Business School Publishing. The article covers how to avoid 8 data-related mistakes on data projects
Linear algebra is a cornerstone of many advanced mathematical concepts and is extensively used in datascience, machine learning, computer vision, and engineering. This article breaks down the concept […] The post What is an Eigenvector and Eigenvalues? But what exactly is an eigenvector, and why is it so important?
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In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes.
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