Data are becoming the new raw material of business
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Ranked: 15 Python Packages for Data Science

Cover of Python Packages for Data Science

At The Data Incubator we pride ourselves on having the latest data science curriculum. Much of our course material is based on feedback from corporate and government partners about the technologies they are looking to learn. However, we wanted to develop a more data-driven approach to what we teach in our data science corporate training and our free fellowship for
Data science masters and PhDs looking to begin their careers in the industry.

This report is the second in a series analyzing data science related topics, to see more be sure to check out our R Packages for Machine Learning report. We thought it would be useful to the data science community to rank and analyze a variety of topics related to the profession in a simple, easy to digest cheat sheet, rankings or reports.

This report ranks Python packages for Data Science, and we’re hoping to stir the pot a bit and get our colleagues to join the discussion. Our discoveries here aren’t final, but rather serve to showcase the depth, and the breadth, of knowledge available to the data science community.

Python, along with R, is one of the most popular tools in a data scientist’s arsenal mostly for it’s simplicity and ease of use- most concepts can be expressed in fewer lines of code in Python, than in other languages.

Rankings for PythonDownload The Full Report To Get The Insights & Methodology!

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In the attached report you’ll find a ranking of all the Python packages that are useful for Data Science, based on Github and Stack Overflow activity, as well as PyPI (The Python Package Index) downloads. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, numpy is 2 standard deviations above average in Stack Overflow activity, while tensorflow is close to average. See the attached full report for details of the methods.

This project began as a ranking of the top packages for all data scientists, but we soon found that the scope was too broad. Data scientists do many different things, and you can classify most Python package as helping a data scientist. To get a complete breakdown of the rankings, our methodology, and additional tech resources be sure to download the report in it’s entirety.

 

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