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Data science is an emerging, multidisciplinary field of scientific methods, processes, algorithm development and technology to extract knowledge or insights in ingenious ways from structured or unstructured data.
At the heart of data science is data. Buckets loads of it, streaming in and stored in enterprise data warehouses. According to IBM, globally, we currently generate over 2.5 quintillion bytes of data every single day.
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Here’s our recommendations for performing data validation using Python. All of the software is free and open source goodness.
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This article examines the best Python microframeworks. Micro means the framework is small, with little or no tools and libraries.
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A framework “is a code library that makes a developer’s life easier when building reliable, scalable, and maintainable web applications” by providing reusable code or extensions for common operations.
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Here’s our recommendations for performing data analysis using Python.
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Python has a fantastic range of packages to produce mesmerizing visualizations. Here’s the finest free and open source Python visualization tools.
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Deep Learning is a subset of Machine Learning that uses multi-layers artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Think of Machine Learning as cutting-edge, and Deep Learning as the cutting-edge of the cutting-edge.
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Great libraries and tools that are ideal for mathematicians working with Python.
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There are some excellent alternative Python shells that extend on the basic shell. They each offer a good interactive Python experience.
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Here’s our recommended Python-based content management systems.
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