github.com-brohrer-academic_advisory_-_2019-11-24_03-02-52

github.com-brohrer-academic_advisory_-_2019-11-24_03-02-52

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Collected opinions and advice for academic programs focused on data science skills.

Industry recommendations for academic data science programs

List of authors

As industry data scientists, we are grateful for the growing number of academic programs in the field. It is a challenge to find enough candidates to fill out our teams and solve the problems we face. We welcome new entrants to the field, whether it is their first career or a second, and would like to help them get the best preparation possible.

Several of us have been invited to participate on the advisory boards of academic data science programs, and we want to share our collected insights as a resource to such programs anywhere.Two of the most common questions are indirectly about how to best prepare students: What precisely do industry data scientists do? and What makes someone a good data scientist?We’ve attempted to answer both of these in a way that can inform how you construct your degree program and your course material.We address the third most common question as well: How can we partner with companies?

A work in progress

It is the authors’ hope that this serves as a summary of an ongoing conversation. If you are part of an academic data science program and have other questions you would like to see answered here,reach out to Brandon Rohrer (brohrer@fb.com) or one of the other authors.If you work as a data scientist and would like to contribute, we welcome your insights.

To restore the repository download the bundle

wget https://archive.org/download/github.com-brohrer-academic_advisory_-_2019-11-24_03-02-52/brohrer-academic_advisory_-_2019-11-24_03-02-52.bundle

and run:

 git clone brohrer-academic_advisory_-_2019-11-24_03-02-52.bundle 

Source: https://github.com/brohrer/academic_advisory
Uploader: brohrer
Upload date: 2019-11-24