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In the first full episode of data couture, Jordan goes into the weeds somewhat while talking about his background, the inception of the show, gear used, and the regular format and release schedules of the show.
We promise not to get into the deep production information involved in this episode in any future episodes, but we think that it might be useful to have some general information about the officious aspects of the show and your host, Jordan Bohall.
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Welcome to data couture, the podcast about data culture at work at home. And on the go. I’m your host Jordan Bohall.
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Welcome to data couture. I’m your host Jordan and on today’s episode, we’re talking about everything to do with the podcast, we’re going to learn about the inception, the format release schedules creation of the show the gear that I use, and how you can stay up to date with everything we’re releasing across the internet. I promise not to get so into production weeds on future episodes. But I think we should get to know each other a little bit before taking off on this journey together.
The show is split up into three acts, and future episodes. The first act will give a historical story about the problem and question then we’ll move on to the second act where we’re going to talk about how this applies to your everyday life at work at home and on the go. And then the third act, we’re going to talk about ways that this can be solves if it’s a problem, and it’s a serious problem. Maybe you don’t know how to deal with it. So we’ll get into that. And other episodes will be interviewing professionals across the industry will talk about a particular problem, how they solved it, and how you too can solve it in your own job.
I hate talking about me. Well, in any case, I am the Vice President of analytics, financial services institution. And I lead a team of DBS data engineers and people who are slowly becoming data scientists. It’s a fairly unique team within the financial services industry, just because you know, lots of places don’t want to invest in what it actually takes to create a high functioning team over my team.
They’re pretty wonderful. And it’s a good group, and we have a lot of fun. But at the end of the day, we get the job done. And we we well, we attempt to service the needs of our internal customers. Beyond that, I hold a few degrees, I’ve got a PhD and philosophy of mathematics or as I like to call it to sound a bit more fancy. I’m a PhD in mathematical philosophy. I also hold a master’s in mathematics and masters in philosophy and a master’s in logic outside of my bachelor’s. And beyond that I, you know, you can’t really escape the academy once you get so far into it. And so part of who I am is an educator. And so I’ve taught a variety of institutions from the University of Colorado Boulder, when I was an undergraduate to San Francisco State University where I got my first masters to the University of Illinois, where I got the rest of my degrees.
And I ended up teaching at the School of Information Sciences there. And now I will, I’m about I should say, to teach Western Illinois University, specifically at the Quad City Campus, I’ll be in the economics department got really called themselves, they call themselves the School of Economics and Decision Sciences. And so I’ll be teaching graduate classes and you guessed it, Decision Sciences.
Outside of that I am starting this podcast because I’ve noticed a problem both in my day job at the credit union, but also in the university. Namely, lots of companies are attempting to do a so called data driven cultural transformation, because they see data is the new oil, right? You’ve all probably heard this by now. However, recent Harvard Business Review study has shown that 72% of companies attempting to do a so called David data driven cultural transformation are failing. And that’s to do with a number of problems. That’s everything to do from a lack of critical thinking skills to communication skills to storytelling skills. To just a fear of what data can actually do for people’s jobs, will it? Will automation take away their jobs? South Park? I truly don’t think so.
And this podcast is an attempt to talk about all these things that go on in the data professions and attempt to demystify them, right. And so when I’m teaching, I see the same problem, we teach all the technical skills, we talked about R and Python, and SQL and MATLAB and SAS, c++, HTML, CSS, those technical pieces, we talked about statistics, we talked about best practices with visualization, we talk, God all just about everything, all the technical, all the theoretical parts necessary to be a good so called Data Scientist, right? However, my students, when they exit, they don’t have the missing skill. So they don’t, they don’t have that data culture at heart, they just know, the technical pieces. And so this podcast is in part to hopefully change that situation.
Right? Given that I’ve got a deep interest in diving into all things in the data professions and data management. I hope you too, will want to take this journey with me.
Now it’s time for the second act, we’re going to talk about all the gear that I’m using, don’t worry, I’m not gonna do this more than once. But I know that some people are fairly nerdy about audio gear, and I’m very much an audio file. So bear with me. I’m certainly not sponsored by any of these people. However, what I’m using is a Shure Beta 58, a microphone, that’s going into a Mackie Pro FX 4 version two mixer, the audio user the other interface I’m using rather, it’s called a FocusRite and it’s just the single input version.
From my portable version, my portable interview version, I’m using a Zoom H5 with the portable battery of course, because that thing just eats up batteries. I’m using Movo pro six set of a lapel mics on top of that for all my sound effects. And for some of my music, I’m using a MIDI keyboard, it’s the Akai MP k Mini, all my cabling is through model price, because why pay a lot of money for cabling, I personally can’t hear the difference. Even though I’ve got cables that cost quite a bit more money than I’m willing to admit.
But all that’s going into a MacBook Air, I’m using an iPad Pro for a bunch of my notes, and then I’m using I or I’m using excuse me in garage band to record all of this. Okay, the painful parts over, let’s move on to the third.
And now we enter the third and final act. Every episode will have a third and final act. And this one just happens to be the format release schedules and outlets for finding the show. So that you know what to expect in future episodes. The format it it’s going to follow a fairly regular schedule. On Mondays, I’ll be releasing drive time level episodes. So we’re talking 20-25 minutes max, which I don’t know about you. But that’s pretty much average for my drive time into work. And then on Wednesdays and Fridays will be releasing short form episodes, I’m going to be calling these “Data Bites” because they’re only five to seven minutes max in length, and they’re just following up on the various topics of the week.
On this coming Monday, I’m releasing three full length episodes, plus wednesday and friday episode just to make sure that you have enough content to stay up to date with what’s going on. Over following that we’re going to switch to the standard format of Mondays drivetime episodes, Wednesdays and Fridays with the short form data bytes episodes. All the upcoming episodes, well, we’re going to cover a lot of topics including lots of areas of artificial intelligence, machine learning, predictive analytics, that kind of thing, topics and critical thinking, what it’s like to be a chief analytics officer, the notion of ethics and data management robots. Since our robots are going to be impacting quite a few of the areas of our lives.
We’ll be talking about automation, but the working at home, what it’s like to be an autodidact, the education necessary to be a true analytics professional. The difference between analytics and report writing, because God knows I get so sick of being called a Report Writer when what I’m doing is advanced analytics, right? We’ll be talking about the natural storage of data and our regular environment, talking about trees, people, we’re going to be talking about music as it’s developed by artificial intelligence sometimes and a live setting will be talking about sports that are data driven, as well as sports that are created by AI will be talking about data and farming. I live in the Midwest, so please forgive me but it’s all around me. We’ll be talking about data driven cultural transformation, of course, and much much more.
I put together quite a few different outlets for you to find a tour on the internet. There’s our website, data tour.org we have our show posted on bus Brown, which is our audio hosts. We have a Patreon site which again, helps support the show and future episodes at patreon. com forward/datacouture or on Twitter @datacouturepod. We’re also on LinkedIn because we’re talking about data culture at work, right? And that one LinkedIn com/company/datacouture. Of course, you can find the podcast wherever you get your podcasts, so don’t worry, we’re around before I kick in the intro to the show. I just want to say thank you so much for listening and I look forward to your comments and your your notes and whatever you want to listen to whatever you want to hear. Please don’t be weird about reaching out to me. I love it. I want to connect with you. I look forward to speaking with you.
See you soon.
That’s it for the show. Thank you for listening. And if you liked what you’ve heard, think 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 to consider becoming a firstname.lastname@example.org forward slash data couture music for the podcast. It’s called foolish game. God don’t work on commission by the artist spin Meister us under a Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is by your host, Jordan.