Episode 46: (Data Bites) Interdisciplinary Approaches to Improving Data Science Academic Programs

On this episode of Data Bites we deliver a solution that may vastly improve the overall quality of data science education.

The idea is to take a traditional academic interdisciplinary approach. Stay tuned to find out what that means!

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Music for the show: Foolish Game / God Don’t Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: Snowflake

Transcripts:

Welcome to data couture the podcast about data culture at work at home. And on the go. I’m your host, Jordan Bohall. To stay up to date with everything data controller, be sure to like and subscribe down below. Furthermore, be sure to follow us around the internet at data to her pod on Twitter, at data couture podcast on Instagram, and at data couture pod on Facebook. Of course, if you’d like to help keep the show going, then consider becoming a patron at patreon. com forward slash data couture.
Now, no onto the show!

Welcome to data couture, I’m your host Jordan on today’s database, we’re going to talk about the fix for what we talked about Monday, namely how graduate schools can truly improve the quality of their programs as well as the preparedness of their students. Before we get to that, of course, and don’t worry, this is almost over. But we have a Kickstarter going on. And you can help be a backer of the show a supporter of the show by heading over to data couture.org forward slash Kickstarter. I promise I won’t keep badgering you guys with this for too much longer. So I appreciate everyone who supported so far. And I appreciate anyone who’s planning on doing so in the near future. Now, let’s get to the show.

Welcome back. So as a quick recap, on Monday, I spoke about how these various graduate programs and data science are failing their students because they’re not giving them the true picture of what it looks like to be a data scientist or to perform the various data science projects are implementing them from start to finish. And so I rambled through a bunch of the steps, it actually takes as well as point out how these programs are starting to change. Today, what I want to talk about is really kind of a state change for data science programs. So at the moment, these are treated very much like a professional program, just like you would treat MBA or any sort of engineering graduate school or I don’t know, farming or any of the similar types of programs that are very much focused on life and industry. And I think this is really the the biggest problem with these programs. So what do I mean by that?

Well, and professional programs, you learn a bunch of skill sets, and then there are a lot of resources to help you, as a student, get into the world, get into the working profession, really start your career. And granted, this works quite often. But then again, the students enter the workforce without some very necessary skill sets. So how do I propose changing this? Well, I propose treating it instead of like a professional program more like a traditional academic program. So to give you an example, whenever someone asked me, usually they’ll say, Oh, Jordan, you know, my brother or sister is in college, and they’re going to get an undergraduate degree in data science. So they’re going to get an undergraduate degree and computer science with a focus on machine learning, or AI or predictive analytics, that kind of thing, or they’re considering grad school and considering doing a data science masters or PhD, and every single time without pause, I push back, I say, look, there’s no reason to do that.

There’s no reason to do that. Because really, the skill sets that you need to be a functioning proactive, capable data scientists isn’t confined to just a single program. Instead, students should be looking at a very multi disciplinary approach to their education. And so my usual go to his well consider being a triple major, maybe a math philosophy and computer science, or at least be a major in one of those areas, and then take quite a few classes. And the other two, and of course, it doesn’t just have to be math, philosophy, and CS, it can be cognitive science and English or, you know, it can be some collection of human that are mathematical and analytical based, as well as some way to get exposure to coding and programming and some of those topics. And so how does this apply to graduate programs and data science?

Well, there should be a strong critical thinking component, there should be a strong, creative thinking component. And on top of that, of course, all the math and stats and coding that you would otherwise learn. And so what these programs could truly benefit from is a deeply interdisciplinary approach, multidisciplinary approach, and maybe that’s hard to wrap into a single program, because what it’s eight classes, sometimes 10 or 12, classes, how can you really become an expert in a field? Well, to be frank, and data science, it’s so new, it’s so young, as I’ve spoken about and other episodes, that right now what we really need is someone with the ability to to make connections across very what seems to be disparate bits of information, and then produce something that can actually help a business or help with research or what have you.

And so by having this deeply inner disciplinary connection, this interdisciplinary approach, it would, in my opinion, truly help the students out and prepare them for what life is going to be like outside of the Academy. If you have different thoughts, be sure to leave them in the comments below. And so next time until next time, have a good day. That’s it for the show. Thank you for listening. And if you liked what you’ve heard, then consider leaving a comment or like down below.

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at data couture pod to consider becoming a patron@patreon.com forward slash data couture. Music for the podcast. It’s called foolish game. God don’t work on coming Michigan by the artist spin by Sir. us under the Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is by your host, Jordan Bohall.

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