Episode 17: (Data Bites) Bridging the Theoretical and Practical Data Science Divide

How many times have you heard that your data team is very good technically, but that they cannot bridge the gap to practical application?

On this Data Bites episode we consider the gulf that exists between all of the data theory and the relevant data practice necessary to be successful in your organization! Here’s a big hint: it takes industry knowledge FROM YOUR DATA TEAM to successfully bridge the gap. That is, not only does your data and analytics team have to possess all the relevant data profession skills, but also industry-specific (business-specific) knowledge to truly be successful!

To keep up with the podcast be sure to follow us on twitter @datacouturepod and on instagram @datacouturepodcast. And, if you’d like to help support future episodes, then consider becoming a patron at patreon.com/datacouture!

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

Show Notes:

Welcome to data couture, the podcast about data culture at work at home,
and on the go. I’m your host, Jordan Bohall.
If you like what you hear, be sure to subscribe lows, get the latest episodes wherever you get podcast. And if you’d like to stay up to date on everything data couture, be sure to follow us on Twitter at data couture
pod. Finally, if you’d like to help support this in future episodes,
consider becoming a patron of the podcast through our
Patreon page@patreon.com. forward slash data couture.
Now on to the show.

Welcome to the tour. I’m your host Jordan. And on today’s data bytes, we’re going to be talking about that connection between data practice and data theory. So what I often find in both my students and sometimes with my team is connecting those dots between what the theory says and stats and coding and calculus, and how to actually apply that to problem solving for the business.
So stay tuned.

And so something I find quite often with my students is they’re lacking and the actual practice piece of dating do quite interesting research to do quite interesting theory. But they’re having a real difficult time bridging that gap. Do you have any suggestions for that?
Yeah, I mean, there are. It’s a question. First of all, that comes up a lot. In fact, I just did a presentation here at Peggle world. And someone asked that question. I think the good news is that there are ton of resources available and you know, learning in this field is more accessible than you know ever before. But there still is this still our challenges. Kind of getting out of the book and into actually applying. We’ve tried to one of the ways that we’ve tried to address that and community that has formed around the the tomo podcast is through study groups that we do and the meetups
that you offer, we do meetups,
our meetups are typically discussions of academic research papers, okay, then we do study groups, which are folks that are working through coursework together. And it typically online courses are the most popular in our community are produced by a group called fast.ai. Okay, and what’s really interesting about their approach, and their course, of course, is they’ve got a part one and part two deep learning course, and machine learning course.
ebooks is that what I saw is that
are these these are separate from the the books that we’ve that we create. These are, these are produced by a company called fast AI fast that AI was founded by Jeremy Howard and Rachel Thomas, Jeremy me, was the chief scientist of cargo, cargo being the kind of online community where folks can participate and compete in data science contest. So
sure, money to while you’re at it. Yeah.
And so, you know, fast forward a few years in a bunch of companies later, and he’s doing this fast at AI. Company, and they produce this course, or these courses, and kind of the hallmark of their approach is a top down approach to learning. So as opposed to like a traditional, you know, machine learning course, where you know, your first exposure might be an optimization equation or something like that. Their approach is much more, your first experience is typing some code into a notebook, okay? On a data set that you pull down from category or some someplace else. And by the end of that first session, you build a world class, deep learning model to, for example, recognize, you know, faces in an image or something like that.

So we just heard from Sam Charrington, and he said that, there’s a number of ways that we can stem that gap between data theory and data practice. His suggestion was to use one of the many online sources, one from his buddy, fast study I, which is very excellent, in case you haven’t checked it out. But there are so many other excellent options out there from the various Coursera and Udacity options, not a sponsor, let me say to any other that you might find online. The point is, what really helped what I think that what really will help my students, what really might help my own team members is a dedicated effort to learning the business that you’re in. And so in my case, I lead the data team at a credit union. And for those that aren’t in the banking or financial services world, it’s a very nuanced and very risk driven and very compliance driven industry. And so even though you might have the various technical, technical skills, or the various visualization skills or storytelling skills at the end of the day, you’re also going to need to know things about compliance things about collections, things about risk, knowledge about mortgages and knowledge about underwriting and knowledge about the various retail aspects of banking, like opening a checking account in a savings account, or even knowing about the challenges that it faces or perhaps your finance team.

And so really, the best latest in the gap between theory and practice is to become an expert on some small part of your own business. And then on top of that, learn from these various online sources from various college sources from books from however you choose best to learn following up on our Wednesday episode, and then applying it of course and the relevant iteration for your own particular industry. That’s it for the show. Be sure to like and subscribe wherever you get your podcasts.

That’s it for the show. Thank you for listening. And if you liked what you’ve heard, consider leaving a comment or like down below. Stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod. consider becoming a patron@patreon.com forward slash data couture. Music for the podcast. It’s called foolish game. God don’t work on commission by the artist spinmeister used under say Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is by your host Jordan

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