In the interest of making data science processes accessible to non-specialists, I’ve written a collection of functions for doing a particularly common task in the exploratory phase of data analysis: the detection of outliers. Why care about outliers? There are a couple of reasons:
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Visualizing data that looks like it came straight out of Statistics 101 text book is nice and all — for teaching and learning purposes. You gotta learn to stand before you can run a marathon.
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Do you miss AXFR technique? This tool allows to get the subdomains from a HTTPS website in a few seconds. How it works? CTFR does not use neither dictionary attack nor brute-force, it just abuses of Certificate Transparency logs. For more information about CT logs, check www.
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Snug is a tiny toolkit for writing reusable interactions with web APIs. Key features: Writing reusable web API interactions is difficult. Consider a generic example:
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slideshow Installation – requires Python 3.6 preferably within a virtualenv: pip3 install graphql-example to run the web-app (after pip install) graphql_example runserver to run tests git clone http://ift.tt/2HQAvMH pip3 install .[dev] fab test In the beginning…
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In an era increasingly defined by the proliferation of misinformation and polarized politics, it’s important for internet users to have context for what’s on their screen. This microservice uses natural language processing to analyze patterns of bias on any news website in real time.
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Using OpenSources? Help us out by taking this survey! Who we are: OpenSources is a curated resource for assessing online information sources, available for public use. Websites in this resource range from credible news sources to misleading and outright fake websites.
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