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You may even call it post-doctorship, as the level is beyond the traditional PhD degree. It is not a degree, not competing with university programs, but instead, akin to a fellowship or apprenticeship to learn doing state-of-the-art applied research, discover ground-breaking results or applications, and translate your discoveries into seminal material suitable for a broad audience. It is intended for professionals with substantial experience, perhaps to people who already have a PhD in a different field. It is mentored by well connected, world-class recognized scientists (not necessarily affiliated with a university) with broad domain of expertise in many environments. The focus is on real-world problems and applications to help you get a high-level position in the industry or as an independent researcher.

The idea to create such a program stems from the fact that a PhD degree is sometimes perceived negatively when looking for an Industry job, and some candidates hide it in their resume. This is because a PhD degree is supposed to prepare you for Academic research, but due to the large supply of PhD's and a shrinking job market for tenured positions in Academia, many are left in precarious situations.

Source for picture: Women in data science

This doctorship is designed to make you a leading scientist in the Industry, to become for instance, an executive data scientist or chief scientist. It does not involve writing and defending a thesis, nor publishing esoteric papers in scientific journals read by few, but instead to quickly disseminate your research, explained in simple English, to a broad audience of practitioners.  For instance, possibly to attract VC funding if you want to create a start-up out of it. The standards are by no means inferior to that of traditional PhD programs, they are just very different. The length of this "mentorship" could be as short as two years; it could be carried out part-time, remotely, while having a full time job at the same time.

Features of the Proposed Doctorship

  • Short duration (2 years)
  • Publication in niche media outlets (like DSC), not in scientific journals
  • No teaching load
  • Done remotely, part-time
  • Cross-disciplinary research
  • Not attached to a university program
  • No thesis, no defense
  • Focus on Industry problems
  • Research not influenced by grant money or politics
  • Candidate gets an apprenticeship in the corporate world, related to her research
  • Not a degree
  • Not meant as a substitute to PhD programs

Example of Research 

I have one example in mind. This is what I would offer if I can find the time to start such an endeavor. The research in question is at the intersection of data science, dynamical systems, stochastic processes, computer science, and number theory, with applications to cryptography, Fintech, Blockchain, security, and high performance / high precision computing. Side projects could include the design of continuous random number generators, replacing standard statistical tests and p-value by better tools, or proving that the digits of some mathematical constants, are randomly distributed (this would be a fundamental result.) See here for details. I believe this research could lead to ground-breaking discoveries and nice applications.

I don't even have a PhD in data science, such PhD's did not exist back then. Instead my PhD was in computational statistics. Do I qualify to run such a program? Of course it is not for me to answer that question. You can check my career path here and judge by yourself.

The problem is, how many qualified experts are willing to take the challenge to offer this type of mentorship? How many professionals are willing to join such a program? Are there any companies interested in joining as partners?

Related article

For related articles from the same author, click here or visit www.VincentGranville.com. Follow me on LinkedIn.

DSC Resources

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Comment by Vishal Lala on August 22, 2019 at 12:30pm

Been teaching marketing analytics and more recently data science type courses for over ten years as a tenured Professor. More on me on LinkedIn

Comment by Edgar Isusquiza on June 21, 2018 at 9:26am
Dear Vincent,

I'm interested about the details and how to apply.
Comment by Dr S Kotrappa on June 8, 2018 at 1:39am

I am interested , I am Professor and PhD in Software Engineering. I am working on Data Science Ecosystem since 2 years . 

Comment by Pradeep Naulia on June 7, 2018 at 7:57pm

I love to be part of it. hope it is out soon.

Thanks Vincent for being pioneer in this area.

Comment by Vincent Granville on June 6, 2018 at 2:07pm

I might be able to start offering this doctorship to qualified candidates (not for students hoping to make a quick buck, but for professionals with years of quantitative experience outside of data science, and interested in becoming an executive data scientist.) I have a few projects in the pipeline, connections to make you work in a true corporate environment, and a project to start: see my new book. More to come soon, as I am working on my next doctorship endeavor, which is about "statistics and machine learning for data scientists."

Comment by Dr. Najeeb Ullah on May 1, 2018 at 7:32pm

Dear Vincent,

I am also interested.  I did my PhD in Software Engineering.  I will work as a part time because currently I am working as Assistant Professor in a university. Pls let me know the details how to apply.

Najeeb

Comment by Renato Azevedo Sant Anna on March 31, 2018 at 5:50pm

I'm interested about the details and how to apply. 

Comment by Raj Thakar on March 29, 2018 at 7:58pm

Great opportunity to learn, I am very much interested. Please let me know.

Comment by Ali Awadh on March 29, 2018 at 12:40am

I am interested.

Comment by peter on March 28, 2018 at 11:54pm

good morning... as much as i am interested, i really agree with what Dan Butorovich said. i suggest that while you go about soliciting for funding and partners, we can start with professionals who already are working on data intensive projects with a supervisor or a company; this way, you can liaise with these companies or supervisors since what we are doing is to their benefits. another way to look at this is focus on students who have data related projects and also have access to labs and internet access, you can review them and grant something like a partial scholarship. this idea can be reformed to your taste sir. thanks for your love for data scientists

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