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Quantifying Data Science

Our friends over at The Data Incubator just released a new series of data-driven ranking reports that showcase the quantitative methodologies the data science fellowship, hiring and training company uses to actively teach their fellows. The idea was to develop a more data-driven approach to what the company should be teaching in their data science corporate training and their free fellowship for masters and PhDs looking to enter data science careers in industry. We hope it will prove 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.

The first reports in the series rank the best R packages for Machine Learning and also Python Packages for Data Science.

 

Since insideBIGDATA’s audience is very engaged with data science and machine learning, we thought it would be helpful to pass along these reports that use data science to quantify data science.

The Data Incubator is a Cornell-funded data science training organization. They run a free advanced 8-week fellowship (think data science bootcamp) for PhDs looking to enter industry. A variety of innovative companies partner with The Data Incubator for their hiring and training needs, including LinkedIn, Genentech, Capital One, Pfizer, and many others. The program is free for admitted Fellows.

 

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