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How Custom Learning Paths can Help Lead to a More Stable “Always Learning Culture”

EPISODE 5

EPISODE 5

EPISODE 5

How Custom Learning Paths can Help Lead to a More Stable “Always Learning Culture”

How Custom Learning Paths can Help Lead to a More Stable “Always Learning Culture”

How Custom Learning Paths can Help Lead to a More Stable “Always Learning Culture”

Meet the Guest

Kedeisha Bryan

Kedeisha Bryan, a former Production Supervisor in the United States Navy turned Senior Data Visualization Specialist, career trajectory has been carved through diverse roles in data science and business analysis across Silicon Valley startups and Fortune 500 giants. 

Kedeisha is also the founder of Data in Motion, a program that provides resources, mentorship opportunities and training to help foster career development for the data community.

Watch the Episode

meet your host

meet your host

Aaron Parry

Aaron Parry

Aaron is a professional analytics consultant and Microsoft Power BI expert, with 10+ years working in business intelligence and marketing analytics. He is an instructor, coach and mentor for aspiring analysts, and has deep experience helping companies develop and implement full-stack BI solutions.

Aaron is a professional analytics consultant and Microsoft Power BI expert, with 10+ years working in business intelligence and marketing analytics. He is an instructor, coach and mentor for aspiring analysts, and has deep experience helping companies develop and implement full-stack BI solutions.

Top Insights from Kedeisha Bryan

Offering learning tools is not enough. To foster an “always learning culture” you need to create an environment that encourages learning by either curated learning paths, cohort learning, or bootcamps. 

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READ THE TRANSCRIPT

Aaron:

I'm Aaron Parry. I'm the Chief Customer Officer here at Maven Analytics, and today I'm extremely excited to kick off this Mavens of Data episode where we'll be talking with Kedeisha Bryan. First off, I'd like to thank you for taking the time to chat with us today.

Kedeisha:

Thanks, Aaron. Very happy to be here.

Aaron:

For those of you who aren't familiar with Kedeisha, she's worked for a bunch of different companies ranging from Fortune 500 companies to Silicon Valley startups. She's authoring a couple of books right now, teaching courses, she's a Navy veteran and she's the founder of Data in Motion, which we'll talk about a little bit more in-depth later.

So welcome, Kedeisha. It's really great to have you here.

Kedeisha:

Glad to be here, too, Aaron.

Aaron:

To jump right in here, I'd love to start at the beginning of your career and talk a little bit about your experience in the Navy, your MOS, what you did and go from there.

Kedeisha:

I was in the Navy. I lived on a boat, served on the Abraham Lincoln, and I was a calibration technician. That was my first experience with data analytics without even realizing I was using data analytics. Basically, we would use benchmarks in terms of, you know, testing equipment, making sure they were in tolerance. And if they were not, we'd have to go into the publications. And that was my first experience with doing research and troubleshooting. And so that's really something that really translated well to this field. Eventually, I moved up to Production Supervisor and there I got a lot more experience making spreadsheets. Every month we’d take the performance of different types of customers that we had on the ship and look to see performance levels, and readiness levels and create spreadsheets and presentations for people above me. And then after that, I got out in 2020, and then I went to school, finished my degree, and then after that, I started analytics internships and ended up getting into a startup, Cox Communications, and now Booz, where I'm at today.

Aaron:

It feels like that's a common or a typical kind of progression for folks that get out of the military and they're either jumping back into college or they're starting college kind of brand new. You were talking about your first foray into data and kind of being exposed to that in the Navy. Is that where it started?

Kedeisha:

No, actually not. When I was in the Navy, everyone kept saying, oh, you're a calibration technician. You can get out and make like six figures. So, I was like, okay, So I got out in 2020, which is obviously a terrible time, and no one was hiring. So, I did not even know what data analytics was at the time, to be honest with you. It wasn't until when I was in school, I was talking to a professor and just talking about different opportunities for my degree because I was just unsure. And so, she mentioned data analytics and that's when I researched the field, and it seemed pretty enjoyable. That's when I started teaching myself and got into it. And that's where the passion grew pretty much that summer.

Aaron:

So, it's interesting. I was in the military a while ago and I found that my career path post-military has been very non-linear. I've had three distinct careers since getting out of the military, thinking about some of the things that you learned, getting out of and going into school, how did your experience within the military inform some of the stuff that you're doing now or what have you been able to draw from that military experience and use in your day-to-day job now?

Kedeisha:

I wasn't formally educated in data analytics. So, I think that drive to constantly learn new things and spend that time is something that translated because, in the Navy or just the military, you constantly had to learn new things. You get out of boot camp, you got to learn a completely new skill and you're just thrown out into the field, thrown out into the fleet, and just expected to work.

I would say more so like the soft skills in terms of work ethic. Ever since I was on a ten-month deployment where I would work over 80 hours a week every week and just be awake and have to be up on your game constantly. And that type of environment definitely translates over here where it's not that hard to be a high performer kind of thing. So, it's more so in the softer skills and then also just being able to learn new things, new complex things quickly, or break down complex things into simpler forms. And also, just being able to present in front of leadership. Present complex things in simple terms, in front of leaders who may not really understand the inner workings of what your job is.

Aaron:

It's funny you mentioned training. The military does a pretty good job at making sure everybody's trained and it ends up being one of the larger opportunities at different companies where there's always room to improve on training frequency or curriculum or the scope or how well the team members are trained. So if you think about some of the companies that you've been at, how well would you rate them at their ability to train or create environments that foster that ability to want to learn or to be able to learn?

Kedeisha:

I would say the larger the company, the more resources they have to really provide those types of resources or types of opportunities. I would say one company I was at was very large. Their main platform to provide everyone was LinkedIn Learning, but it was just like, okay, you have LinkedIn Learning and not really much guidance on what to do. Here at Booz, they have a lot of resources, but they actually provide a lot of structure. They have a lot of learning paths. They provide a lot of opportunities to do in-person training on Booz’s dime. And one thing that I like is that you're doing it as a group, so kind of fostering that community learning experience versus just you learning in a bubble by yourself.

So, it's nice that you say, okay, you work here, and you get free courses on this platform, but you're kind of by yourself pretty much, and you're not really given that many opportunities during the workday to actually use it versus actually taking the time to go to a three-day training. And it's hands-on. And not only that, you're a group of 40 of your peers. So, I think in terms of rating, you have steps. It's one thing offering, say, a platform of resources, like you have this resource, and then also going a step further and fostering some sort of community learning environment so that you actually have some sort of motivation to learn it. You have motivation to learn and you're not learning it by yourself.

Aaron:

Yeah, that cohort-based learning can be very powerful, right? You can talk with and commiserate with everybody else that you're in the group with. So, that cohort-based learning, is it programs that are developed by the company internally or are they giving you access to external training?

Kedeisha:

So, it's a bit of both. Here at Booz, they use Udemy Business. I have data engineering and data science curated paths of courses on Udemy for these particular jobs and they also end with badges where you take a test and then you get this badge on your profile. And then they also utilize other companies who provide that external training or internal training over the cloud, Excel, data science programs, and a lot of in-demand skills.

So, it's a blend of both, but I find it pretty impressive that they have people here who took the time to curate certain learning paths, utilizing the Udemy courses because, for example, if you just say Hey you have free Udemy courses, then you're still lost.

Aaron:

That's really interesting because that's one of the biggest pain points that we were solving when we initially built Maven was that the most common feedback was, I don't know where to start or I don't know what to do next, right? And I love hearing that the company is doing that for you where they're helping to build like, all right, this is your role. We've got this learning path built for you at the end of the path as a badge. You can share that internally on your profile. And it's cool because it creates a little bit of gamification for the individual. But then you can also start looking at other coworkers’ profiles and they're like, oh, they've got that badge. That's really cool. What did they do to earn that? And, you know, it makes it fun and interactive and it really just helps really promote that learning mentality and environment, which is hopefully becoming more common. But you still see a lot of companies aren't super successful with it.

Kedeisha:

It's interesting. I do think community-based pretty much everything is going to be something that's going to get a lot more focus within the next ten years or so.

Aaron:

Yeah, absolutely. It's interesting. I have been getting a lot of feedback from some of our team customers, the B2B customers that they're interested in smaller bite-sized trainings. We've got some longer courses that take 15 to 25 hours to work through a single course. And then we've got paths of the courses where it could take you a couple of months to work through depending on the pace at which they're able to kind of consume the material. And it's an interesting balance between learning how to complete a specific task like I want to learn how to do X in Python versus I want to learn this foundational skill set and really hone and develop that. So, it's always fascinating to think about what it takes to really build a strong foundation in data and analysis and critical thinking and all of that stuff that really factors into a well-rounded data professional.

Kedeisha:

Yeah, I completely agree. Those are definitely the things that people miss out on. You know, there are plenty of those 20-hour courses on YouTube. I don't recommend doing it all straight.

Aaron:

Yeah. Right there with you. So, are we going to see any of that thought process in the books that you're writing?

Kedeisha:

Yeah. So, I know one just got announced, so Becoming a Data Analyst is definitely something I'm pretty proud of. It's definitely something that focuses on not just the technical skills, we talk about building a project, charter problem solving, the data lifecycle, and the role of a data analyst in each step. We talk about not just tools, tools, tools, but best practices, data visualization presentations, and designing dashboards.

I run these challenges so I see dashboards all the time and I just want to get people more quickly up to speed on how to develop a very good visual for a stakeholder, because I think out of all the skills and data visualization and dashboards, that's something that really uses both sides of the brain.

And I think that's a very unique type of skill set that I just want to get people up to speed a lot quicker. So, it's gonna be a pretty good book. It's going to go over, like I said, problem-solving for the analytics. So, creating a project charter, racing matrices, and things like that, sequel, statistics, EDA, and then data storytelling and visualizations.

Aaron:

That sounds fascinating and right up my alley. So, I'll have to check it out once it's published. What was the title again, or has it been confirmed?

Kedeisha:

Yes, Becoming a Data Analyst. It's already up on Amazon, actually.

Aaron:

Oh, perfect. I'll have to check that out. That's great. It's interesting, two things kind of always come to mind when you're starting to talk about folks that are just getting into data analytics. Typically the question is which tool is the best and which one do I learn first, right?

So, it's always focused on the tech. Oh, I've got to learn Power BI, oh I've got to learn R, or I can't become a data scientist or something like that, right? Versus really understanding the core foundational concepts and the things that you really need to be able to apply. You know, when you're using insert tool here, right? It doesn't matter what the tool is.

I remember I went to the Open Data Science conference one year and I was going in and sitting through some of their Python sessions to try to start learning that and one of the first sessions I went into, they said, All right, everybody open up Jupyter Notebook, type in this code. And then it just started downloading like, you know, importing packages and all this stuff without getting into any of the foundational stuff.

And it was such a jarring experience that it really stuck with me. You really got to teach those kinds of foundational, core concepts in those fundamentals before you get into stuff. So, I love that that's the approach that you're taking with that Becoming a Data Analyst book, that's awesome. The other side is really the data visualization, you know, side of things. That's one of my weaker areas. Because it very much to your point is both sides of the brain. It's a mix of art and science. You start talking about color palettes and design choices and pre-attentive attributes. And there's a lot of actual science-based stuff that factors into it, but it also needs to be presented well.

And that's where that art form and the creativity side comes into play. So that's really cool that you're focusing on that and passionate about that.

Kedeisha:

I don't feel like a dashboard is just a dashboard. I feel like you're building an actual product, you know, it’s an actual data product. That's something that people are actually going to use, or you'd like them to use it. So it's definitely just more than a bunch of charts on a laid-out canvas.

It's something that's like the most fun part in my opinion. Everything else can be highly technical, with a lot of troubleshooting, but it's really nice to have that one thing that is really a culmination of your entire work. And your stakeholders are not going to see they're not going to ask you about the data cleaning process at all.

Aaron:

Oh no, they're not. They're going to ask if that specific number is right if they have a question about it. And there's another book that you're working on as well?

Kedeisha:

Yes. It’s going to be primarily working with my coauthor, who's also a veteran, Tamara Ransom. It's going to be called Cracking the Data Engineering Interview. So basically, it's not going to be like A to Z, but an overview of key concepts that you should know before that engineering interview. It's going to have at least 50 or so mock interview questions in pretty much each chapter and also talks about how to build a portfolio, and how to build a good presence on LinkedIn, for example. So, it's definitely going to be something that's just not like a plain old interview prep book.

Aaron:

That's great. I think there's a big need for that in the community, you know, folks that are looking for the mock interviews or some of the case studies that they could expect, like the types of questions that they're going to run into, how to handle them, prep for it, navigate.

That's great. You've got a solid LinkedIn following, do you get a lot of questions from people within the community about, how do I approach the analyst interview or data engineering interview or how do I present my work, kind of hinting at the project portfolio stuff?

Kedeisha:

Yes, all the time. It’s one of the reasons why I made my community. I used to be in my DMS all the time, even scheduled to meet people. And I was like, how do I scale my help? But it's interesting, especially when you're kind of glued in or in tune with the industry and resources. A lot of the stuff is already available but still people are still struggling.

So how do you present some sort of solution or resource where people can get their answers in a pretty clear-cut way? But yeah, everyday people are still struggling with these same things. It's quite astonishing, actually.

Aaron:

It is. Yeah, it is. There's no shortage of people that are looking for help and support on this kind of stuff, and correct me if I'm wrong here, but that's what the Data in Motion is basically aimed at, right?

Kedeisha:

Yes.

Aaron:

Can you explain that a little bit more, kind of what the company is? The mission and what you’re setting out to do with it?

Kedeisha:

Yeah. So basically, the community itself has been very good to me. I've gotten a lot of resources, a lot of free help, a lot of free mentorship, and friends through the community. And I wanted to provide a resource because I'm into it. I see everyone we all have like, you know, five different subscriptions. People go through a boot camp, people will go through college and they're still struggling. So, I wanted to provide something that was a solution that's like in between watching a free YouTube tutorial and say like, an $8,000 boot camp that ends at some point. And basically, just a community-based learning platform where there are community learning events, live learning events, a lot of community collaborative projects, and basically like a supportive resource.

And that actually complements pretty much every other platform that's out there too, because you can go to Udemy or YouTube or whatever it is, and you're still kind of like learning in a bubble because all these platforms actually have this pretty cool community that has all these community events and all this networking.

And so that's very much what I want to do. I want to create a platform like no other that's just focused on community-based learning, a lot of practice materials and a lot of projects, and a lot of live community events.

Aaron:

Yeah, I love it. I mean, again, that really ties into the cohort-based learning that you're doing at Booz right now too. And you see a lot of success with that, and it absolutely makes sense. And I think that there's a lot to be said for having a central place where data professionals in any vertical can come and talk about, hey, this is the process that I'm working through. Does anybody have any similar experience with this? Or, hey, I've got this interview coming up. Can you guys help me prep for these kinds of things? So, there's a ton of value there for the individual. Do you think that there's an opportunity to evolve the interview process? So, if you were interviewing somebody to join your team, right? And HR laid out like, here's how we like to run this. It's kind of like a, you know, dress, right? Dress like we're going to follow the rules. You know, in thinking about a typical interview, do you think there's an opportunity to evolve it to better assess a candidate's skills?

Kedeisha:

Yeah, I’ve never been one who has been in those six-round interviews that take weeks. So, what I would do personally, especially now in the age of ChatGPT optimized resumes where everyone looks good on paper, everyone does their best job at, you know, providing a very perfected version of themselves and even sometimes in the interview.

So, what I would do personally if I'm hiring for a data analyst, I would do a live case study. So like how you have to take home assignments, I would do something that could be designed and set up where they can be completed in like an hour, under an hour, not like a super in-depth deliverable, but something where I can evaluate their thinking process and how they approach a project and something that they've never learned before in real-time. That's something I would do because I'd be able to evaluate if someone's good for the job fairly quickly, rather than taking them for their word and just hoping that they perform well the first 90 days.

Aaron:

I think that's great advice for hiring managers and HR Teams, where you know, figuring out if somebody is a great fit for your organization, you're kind of hiring for fit and personality and you're assessing those critical thinking skills and abilities and, you know, do they have a basic understanding of data structures or, you know, things like that? Some of those kinds of foundational skills. Then from there, you can train as long as they have the aptitude for it.

I think that's a really intuitive way to go about hiring somebody that's going to be the kind of right fit for the role in the team, in the company.

Kedeisha:

And it's not really something that you can’t like perfectly prepared for too, you know? Versus like, what are the interview questions and what should I expect? That live assessment is something that really shows exactly who you are and then you'll see the subconscious types of behaviors as well.

They could easily pick up and see if that's a perfect fit for your team.

Aaron:

What's one piece of advice that you would give to somebody that's going out there into an interview? You know, maybe it's something that you wish you had known for one of your first analytics roles.

Kedeisha:

First, write out all your strengths and write out the things that you do very well. Ask yourself, why should this person hire me? Why should that person hire me and literally write it all down, and write down examples of some of the great things that you've done in your past, whether it's data related or not. Definitely relate it to data, but doing that, it gives you a sense of confidence. It gives you evidence of who you are and your capabilities. I know a lot of us, we struggle with imposter syndrome, especially folks who are transitioning from fields that they're like, how do I prepare when I have no data experience? But I tell people, you're not as junior as you think you are, but you have to literally have a lot of evidence of who you are, your capabilities.

When you know how to craft your own story, honestly, everything else is this easy, in my opinion. You're in the interview. They know you have those skills. They expect you to have those skills like the next person. If you are able to craft your story in a way that shows your uniqueness, what you bring to the table, your work, you're going to pretty much get any job that you want, in my opinion.

Aaron:

Yeah, I agree. And one of the things that I love about that exercise is it does everything that you just outlined. It also gives you the confidence to talk about areas that you might be lacking in, right? Where you're like, hey, I recognize that this is a deficiency. I want to work on it. It's a goal of mine. And then you're spinning that potential deficiency into a positive. And then you're saying, hey, I've got a lot that I can still work on, but I'm excited, you know, and kind of craft that story that way.

Kedeisha:

So that's one of the reasons why I tell people to make sure you do a portfolio. It's more than just to get a job. Actually, it's straight up evidence which you can see of your actual skills.


Aaron:

Yeah, I love it. Well, again, thank you very much, Kedeisha for taking the time and chatting today. It's been a pleasure.



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