Episode 211:

From Classroom to Data Science: Career Advice and AI Trends with Angelica ‘Jelly’ Spratley

October 16, 2024

This week on The Data Stack Show, Eric and John welcome Angelica “Jelly” Spratley, a senior data science instructor and content creator at Flatiron School. Jelly shares her journey into data science, her experiences at the Disney Data and Analytics Conference, and her role at Flatiron School. Key topics include the importance of being industry-ready, project-based learning, and self-advocacy in professional development. Jelly also discusses the challenges of working under non-technical managers and the value of technical knowledge in leadership while also data work to business value and continuous learning. Don’t miss this great episode!

Notes:

Highlights from this week’s conversation include:

  • Disney Data and Analytics Conference (1:15)
  • Flatiron School Overview (3:51)
  • Defining Industry-Ready (4:48)
  • Transitioning to Data Science (6:33)
  • Teaching KPIs (8:10)
  • Self-Advocacy in Career Development (11:22)
  • Managerial Credibility (16:11)
  • Bridging Academic and Industry Gaps (19:30)
  • Managing Projects with Non-Technical Stakeholders (21:35)
  • Transitioning from Industry to Teaching (23:38)
  • Relating Data Science to Personal Interests (25:32)
  • Creating Engaging Learning Experiences (28:42)
  • Involvement in Data Literacy Organizations (31:09)
  • Addressing AI Concerns in Education (34:16)
  • Collaboration for AI Safety (38:22)
  • Remote Work Trends (41:09)
  • Productivity in Remote vs. Office Settings (44:11)
  • Final Thoughts and Takeaways (46:13)

 

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Transcription:

Eric Dodds 00:03
Eric, Hi. I’m Eric Dodds and I’m John Wessel. Welcome to the data stack show.

John Wessel 00:07
The data stack show is a podcast where we talk about the technical, business and human challenges involved in data work.

Eric Dodds 00:13
Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show. We are here with Angelica “Jelly”, Spratley. Jelly, Welcome to the show. We’re so excited to have you.

Angelica Spratley 00:35
I’m excited to be here.

Eric Dodds 00:39
Give us a quick background. How did you get into data?

Angelica Spratley 00:44
Yeah, well, currently I’m a senior data science instructor for Flatiron School as well as content developer. Prior to that, I served in the industry as a data consultant working in the higher education and pharmaceutical spaces, and this all started from being a high school engineering teacher, very

Eric Dodds 01:02
cool. Can’t wait to hear more about that.

John Wessel 01:04
So Jelly, we got to talk a little bit before the show today. And one of the things I’m fascinated to hear about is that there’s a, apparently, a Disney and data AI conference. So we got to talk more about that. What are some topics you want to dig into? Yeah,

Angelica Spratley 01:19
I’m excited to talk about that too, as well as how you can maintain your value and why education is so important if you’re trying to enhance yourself as a data professional or transition it into the data world for the first time. Awesome.

Eric Dodds 01:33
Well, let’s dig in. Yeah, it sounds good. One of my favorite things about the show is just going to be calling you Jelly, it just sounds so great. So thank you for bringing that little bit of joy to the show.

Angelica Spratley 01:47
Yes, I love that nickname. So shout out to my mom. Shout out to you, mom. All right,

Eric Dodds 01:51
Okay, we have a lot to do. I really wanted to get into career advice. I want to talk about teaching and all your experience teaching, but you just got back from the Disney, is it data in AI conference,

Angelica Spratley 02:06
Disney data and analytics conference? Yes, data and

Eric Dodds 02:09
analytics. Okay, I’ve never heard of this. It sounds but of course, Disney has, you know, and it’s probably awesome, but what is it? Tell us about it. So

Angelica Spratley 02:24
The Disney and data analytics conference happens every year. I want to say, at least for the past 13 years, Disney puts it on once a year. It’s a two day conference, and they have vendors and guest speakers from in the industry, as well as within the academic settings. So we were able to hear from companies like Nvidia as well as Disney themselves, along with people who serve in the academic research space. And I would say one of the non technical highlights there was actually having a Broadway opening for each day. So they brought out their great Disney singers on Broadway, and they welcomed us to the conference, as well as some amazing food. But you definitely get hands-on demonstrations about AI agents, as well as some of their robots that they’re making at Disney, as well as data strategy. So definitely full of fun, full of information. I would say some of my favorite presentations came from some academic professors that were just talking about the AI role as it stands in academia, as well as Yaga and people of that nature talking about all of the infrastructure that we need to scale out these nice AI models. So it was very fun. That

Eric Dodds 03:41
sounds awesome. Okay, so you mentioned Flatiron School. We just jumped right in. But tell us about Flatiron School and what you do there.

Angelica Spratley 03:50
Yeah, so at Flatiron School, I’m a senior data science instructor as well as a content developer, so flatiron is really good in the EdTech space and upskilling and reskilling current data professionals at their role, as well as making new data professionals for the industry, not only do they focus on data science, they focus on software engineering, cybersecurity and product design, and these are really intensive Hands on application courses to try to get people industry ready. Because I know many times you may see Eric that there’s a lot of non industry ready curriculum where you’re just putting out Jupyter Notebooks. So we try really hard to make it very applicable to the industry.

Eric Dodds 04:38
Well, let’s dig into that a little bit. So you know, being able to do a couple things in a Jupyter notebook does not make one a data scientist. But let’s talk about that. And I’d love to frame this as career advice. You know, both for those who are thinking it would be interesting to get into a career in data or people in tech. Role, role who may want to change their role. So what does industry ready mean? What are your thresholds for what that means for data science specifically? Yeah,

Angelica Spratley 05:10
so it means not just regurgitating concepts. I would love to know that you know what a p value is, but I also would love to see a project doing AB testing and showing that you can get data, wrangle that data, and actually run an experiment, design an experiment, and relate that to business value. So I’m a big proponent in flatiron as well about project based learning, not only individualized projects that you’re passionate about, but also working on a team, because data science is no longer siloed. I know back 10 years ago, it got the rep of being a programmer in a basement that nobody talked to, but now we know that it’s a very collaborative field, so being able to immerse yourself into communities, whether it is volunteer communities, not for profit, communities like me, being a part of women in data and being able to work together to solve real business problems, using data and deploying that out is really what’s going to set you apart in the industry.

Eric Dodds 06:12
Now, what specifically would you say to someone who is maybe in a semi technical role, or even a data flavored role, like, maybe they’re an analyst, and they want to move more into, you know, form, let’s say formal data science, you know, data science, where you’re building models and doing other things. What would you say to that person?

06:32
I would say,

Angelica Spratley 06:33
track your story. And this may sound weird off the bat, but what I mean by tracking your story is whether you’re in a data role or not in a data role. So for me, being in teaching, I had data on midterm exams. I had attendance data. I gave my students surveys about how they understood topics. All of that was data, but I was necessarily an academic teacher, so even if you’re in a data role, track what data you’re currently using, and measure that with a KPI, so that way you can tell your story. So if it’s saying, Hey, I’m currently pulling customer data every day to analyze the satisfaction that they have with our products, or to analyze how our social media posts are doing. By this I was able to increase engagement by 15% so I can really do self reflection and track your data usage and literacy skills now so you can tell a story about it later, when you’re transitioning into a more technical role.

Eric Dodds 07:37
How do you teach the KPI side of things and the thinking around that, because that is not that’s a really different skill than say, you know, designing an experiment. I mean, it certainly influences that, but it is kind of a different mindset, right? Like being able to run models and write code is a different skill set than trying to understand, how do I relate the results of what I’m doing to some specific problem for a part of the organization that I don’t even work in?

Angelica Spratley 08:10
Definitely, so I would say for me, personally, as a professional, and then I’ll talk about how I educate my students to do it. But anytime I’m having a one-on -one for my manager, I ask those questions. So I’ve done this work this quarter. Can you give me some KPIs that matches our strategic plan, which for flatiron is going to be increased matriculations, increased satisfaction of the product, and kind of talk to me how my direct work relates to that KPI, and I make a note of it, and I do that every one on one based off of that strategic plan. So I would say, understanding your company’s mission, if your company’s mission is something where an education is to help individuals land careers. And when we analyze job placement data, I get information about how many of my students were actually placed in careers within 90 days, 180 days or a year, and I married my performance against that. So as a professional, I would say, self advocate for yourself and your one on ones, take advantage of your manager’s time when you do have it, and really try to tie back anything that you do towards company, mission and strategic value, and that transitions into when I’m educating people to transition into data. So when I’m giving them projects, we talk about business and stakeholders first. Who would be some potential stakeholders that will use your recommendation system when you’re recommending 20 books based off of what they have read before, or other people like them who would be interested in that? That could be somebody similar to a Barnes and Nobles, who’s creating an app, who now needs a recommendation system, or even just a general local bookstore. Now, what metrics would you think they would like to analyze? Do you think so? Are they actually reading that book? If that is an ebook, How long are they staying in that book? How many customers are giving thumbs up for the recommendations that you provide, and then when you do your model, marry it back to that business problem in that stakeholder?

Eric Dodds 10:14
Yeah, I love it. Okay, I have a question for both of you really quick, actually, and this is, I’m just gonna, I’m gonna throw a hard one at you, Jelly and John. Jelly and John. That has a nice ring to it.

10:27
I like it. So

Eric Dodds 10:28
First of all, I couldn’t agree more. If you can tie your work to the company mission and numerically define that and get validation of that from your manager, even get explicit direction from the manager. That’s great. There are a lot of people out there, you know, even who are not early in their career, but that is not always explicitly defined. At every company, right? I mean, they may have a stated mission, but at a lot of companies, they don’t really measure. They don’t have hard measurements against that. So what would you say to someone early or later in their career who heard what you just said, Jelly and are like, I would love to do that, but I don’t have those anchors within the company. In order to do that Jelly, you go first and then John, you’ll get them. Let

11:21
me off the hook. Yeah, whatever she said, I

Angelica Spratley 11:26
like the rink of John and Jelly. Yeah. So go back to your initial job offer and job description and see if you can advocate how to quantify those bullet points. So for me, my job description was to educate students on data science, help with career placement and career workshops, increase matriculations by developing a well renowned curriculum, whatever it may be, because, as you mentioned, it’s very vague. And then on day one on onboarding, I asked that question, how can we measure that I’m actually performing these job responsibilities? Are we actually gathering this information if we’re not gathering satisfaction scores? How can I help advocate that you will do that? Is that another Qualtrics survey? Is that another NPS survey, even though that has its pros and cons, and start with an easy way that we can start tracking this, because I want to be able to track my performance throughout my time here at x company. So if it’s high level, through onboarding, try to find some tools that you can advocate to help track it, and don’t be afraid to use qualitative stories. You can’t quantify everything. I can say qualitatively that when my last cohort of students graduated, they all gave me a shout out at graduation about how prepared they felt career wise, and that just speaks volumes itself in satisfaction, even if I can’t say I increase satisfaction from day one by 15% by the time of graduation, John,

John Wessel 12:59
I think that’s really good. I want to address one hang up. I know I had that I think a lot of people have, is that you can easily get in a spot, especially early in your career, where you think, Well, my boss should be doing these things. And you have a list, right? You have that mental list of like, well, they should be, like, promoting my work. They should be, you know, helping me, they should be defining what my goal is, and, like, what should be measured, like that first, like, you have to get past that, right? Because you may that is true. Like, ideally, if you had the perfect boss, they could help lay out. Like, here are the metrics, and here’s, like, how we want to track and etc, but a lot of people are not in that situation, and you have to get past that. Like, you will probably end up at some point in your career, quote, doing your boss’s job for them, and that you’re constantly bringing clarity to these things, and you just have to get over that. Like, that’s fine, that like, like, it’s okay, like you can do that. Like, that’s a way you can be helpful to the company, to your boss, and like, that’s a really positive thing. So when you get past that, then totally agree. You’re, you know, you’re like, okay, like, what do we want to measure? And then you may get a vague answer of, like, well, we want to measure engagement. I was like, Okay, how do we want to measure engagement? And you might get, I’m not really sure, or let me get back to you, like, you have to keep pushing. Like, okay, well, here’s three options. Like, you just kind of kind of did, like, Okay, well, maybe this, you know, like you, you might have to, like, get past some further barriers to get that but you just, like, keep marching that way. Don’t be afraid of iterating on whatever it is, and don’t get frustrated if, like, you come back and it’s like, well, we agreed on this, and then it’s like, Well, I think we want to tweak that for this reason, for the measurement like that will probably happen, but as long as you can keep like that positive motion forward, I think that’s the right way to do it.

Angelica Spratley 14:50
Yeah, be a self advocate, like, if you don’t have or you’re working on self advocacy skills, that is, if I knew what I knew now, do. Back then in my data role I was a self advocate, advocate for professional development, advocate for transparency, advocate for you, understanding your job role and how you can even get credit for things that you do on the job. So self advocacy is something that you’ll really start to pick up on and hopefully get better at as you transition within a company, or as you transition company. So definitely,

Eric Dodds 15:23
one thing you mentioned when we were chatting before the show Jelly was you were talking about, you know, sort of teaching and mentoring, and then also being a manager and mentor, you know, mentoring. And you said, which I wanted to dig into. But of course, we had to hold off until we started recording the show. So excited to hear from you. You use this phrase something along the lines of, you know, one thing that I think made me a good manager was that I can go build that Tableau dashboard myself, like I have the ability to do that. And that kind of reminded me of this old Steve Jobs quote, where he talks about the best managers are really good individual contributors who don’t really want to be managers, but they know that no one’s going to do as good a job of them as being a manager. And that really struck me, because I’ve had the same experience right where it’s like, okay, if my boss can do the work at a very high level, they tend to hold me to a really high standard and really push me to do excellent work. But can you dig into that from the manager perspective and the teacher perspective, and what are the differences?

Angelica Spratley 16:35
Yeah, so back in my teaching days, I used to always joke that the principal was never a teacher. It kind of knocks your credibility. And nowadays, when you need so much buy-in from people within your organization and outside of your organization, you want to be deemed as credible as possible. You want to build a trusting relationship as quickly as possible. So within my managerial experience, when I was managing a team of associates to do analytics work for higher education, I was able, on a weekly basis, or biweekly basis, say, let’s come up with a professional development plan together, and let’s come up with what it looks like. Because I know what that technical project, what done looks like. So I’m able to translate that to them, because in some of my non technical managerial experiences, they can’t tell me what done looks like whenever the model is done, and I guess, validate it, right? It’s very vague. So I can help them understand what done looks like, help them grow professionally, and they can easily see that I’m accessible. So if I know that for some reason this Tableau dashboard is breaking, I have my manager that can actually help me troubleshoot push, you know, worst case scenario if the rest of the team can’t, and it helped them view me as a credible manager by understanding the work that they do on the front lines and not being afraid to say, hey, you take that PTO, you take that mental health day, because Jelly can take on your work as well. And I think that just makes the whole environment better. And the same way in my teaching role now with my students. Students love industry professionals, I can look at an academic curriculum and be like, Hey, let’s actually put in this use case. Let’s not talk about probability in terms of flipping a coin or drawing a card out of a deck, right? Oh, but let’s talk about if Jelly goes to Walmart and she wants to buy a king size candy bar versus a small size candy bar. Give me some type of conditional probability for Bayes theorem. What is the likelihood that she’ll go to Walmart to buy this king size chocolate bar versus going to the 711 right? And they’re able to see, huh? I kind of understand this, and then I’m able to fill in those gaps. A lot of curriculums might not understand roadblocks that you experience in the industry. I know that probably less than 5% of models will be deployed. It’s just the reality of it. And then students get so happy, like, I want to implement this and implement this. There are some constraints, like, you can’t just run a K nearest neighbors model on a 2 million observation data set. You don’t have computational resources, and if you keep tapping into that EC for two hours on Amazon, you’re going to run the bill up, right? So solve this with just a basic t test, right? So it’s some of those gaps. So they appreciate a lot of that industry experience that I’m grateful to have had before coming back into academia to kind of translate their projects into real world scenarios, for them to assess potential roadblocks, for them to understand exactly what that learning curve may look like, transitioning from an academic setting into the professional world. Yeah,

John Wessel 19:54
I, this is funny, like I’m laughing at myself over here with the Steve Jobs quote, because he calls it. Yes, the no bozos policy.

Eric Dodds 20:04
Forgot about that. Yeah,

John Wessel 20:06
the no bozos policy. So I totally agree with all that. What if you get stuck in a situation where you have a non technical manager and, yeah, how I, which I love, because it’s that like player coach, and there’s, there’s a lot of these. I think even companies I’ve been thinking about this a lot, where more and more there are these, even CEOs that are, you know, more involved in the day to day and have more technical knowledge, versus just being, quote, like a leader or figurehead. So I think that’s a great trend, but you still have those situations where, like, Oh, my manager has no idea what I do. Like, for example, like, how would you handle that? Yeah,

Angelica Spratley 20:42
I recommend to my students, when I’m talking about professional skills, is that whole quote, your emotional intelligence will keep you on the job, even if your IQ gets you hired for the job, because your IQ will get you higher, but your EQ will get you fired because you don’t have enough professional skills and development in order to maintain that job, and one of those is project management, because that’s exactly what you’re talking about. Because if you look at your non technical manager as a stakeholder to buy into your project, you’re going to have to know in and out how to manage that project and how to translate that technical work into non technical communication for them. So if not something as simple as a risk analysis, because that’s what they’re going to be concerned about, how much risk do we have going down staff? How much return of value is this project going to give me? And you’ve actually managed and planned out that project. So when you sit with your manager, you can get them to buy into your project, to go so understanding what you’re doing, not just at a technical jargon level, but at that non technical level that you can communicate to get anyone to buy into what you’re doing, and actually be able to quantify your projects and things that They care about money, they care about saving time, quantify your projects as such. So definitely take a project management course. It has helped me out tremendously, and focus on some of those professional skills of that nature, to get those non technical stakeholders to buy into your work.

John Wessel 22:16
Yeah, I love that. I think it’s so easy to get caught up again. Like, just, you have to get over the like, well, I don’t feel valued because my manager is not technical. My manager doesn’t know what I do. Like, you really have to get past that, right, to get into the mindset that you’re describing, of, like, okay, like, here’s what I can map out. I can project manage this. Like, I can basically wear two hats. And I have to maybe kind of switch hats depending on what I’m doing. My project manager likes to accomplish these things, and I like technical data science to do these things. And I think if you can develop that skill set, it’s super valuable.

Angelica Spratley 22:54
And for my teachers out here, I’ll just put this in here, you have a great skill of anticipating questions. So I always say the appendix in my slide deck is longer than the slide deck itself, because that appendix is a slide for every anticipated question that a technical or non technical stakeholder may ask me. So they’re going to ask me, why can’t we do this in Excel? You slide every meeting anticipating that question to answer the answer, why do we need infrastructure that you’re proposing? Yeah, definitely. That’s a good professional skill. Be able to anticipate questions. Yeah,

Eric Dodds 23:33
and this is just good career advice for everyone, in general, but Jelly talks a little bit about going from so you taught, you went into industry, and then you went back to teaching. What drew you back? I

Angelica Spratley 23:49
I think it’s purposeful. I’ve always heard this quote back when I was in eighth grade that said the purpose of life is a life of purpose, not saying that my previous roles weren’t purposeful. They weren’t as purposeful as I found with teaching, because when I really thought about what gives me a day to day satisfaction, so I don’t feel like I’m working is empowering others to know that they can make a career transition and be in that one. To write that reference. I write references all the time. I give recommendations all the time, and one of my students just landed an analyst role at Caesars Entertainment like yesterday, and I’m like, congrats, and that gives me this sense of fulfillment that made me want to come back from industry into teaching, to do it on a larger scale, because even if it’s a curriculum that I’ve developed, so little side thing that I’ve done So I helped develop the Google Coursera Advanced Analytics course, and you’ll see my name listed under contributors. And so people on LinkedIn were like, Hey, you contributed to that Coursera course for Google. I absolutely loved it, and that helped me get a job. And I’m like, Yeah, that was pretty fulfilling. So even on a larger scale, where I can do a curriculum. That helps people learn more, transition, upskill and get their dream life. That’s actually why I made that transition. So

John Wessel 25:08
Along those lines, for people that do want to, you know, transition careers, I have from time I’ve talked to a couple people, some that have considered just getting more technical in general, a lot of them are very intimidated, right? Like, how do you say you’re talking to me, and I’m like, I think I might want to, you know, get into this, but I don’t know. I don’t know if I can do it. Like, it’s really technical. Like, what do you say to somebody like that?

Angelica Spratley 25:31
Yeah, so I have plenty of people to come to me that, and I try to relate to you on your hobbies, and try to make you understand that data is part of your everyday life. One of my current, well, former students, was into makeup, and they’re like, hey, I want to be a data analyst because it’s gonna help me make more money. I’m a single mom. I have three kids, but, you know, I like makeup, and I’m like, great, your lipstick is poppin. Let me tell you how you could do a neural net to detect lipstick shade, so that this can be another selling point for something like a L’Oreal or Maybelline to use. And they actually made a neural net collecting their own face with different colors of lip and 98% accurate. And they’re like, Hey, I married data science with makeup. And I’ve had bakers, I’ve had scuba divers, I’ve had all different types of people come to me wanting to take these educational courses, and I’m able to, just like, pick up and give them a real life story. Did you know that every time you dive underwater, the water quality and turbidity is actually determining what type of fish you’re going to see based off of this classification model, and they’re like, I did not know that. Or, you know, you’re passionate, because you have someone who does American Sign Language, and one of my students was able to make an app where the person does the signing and it writes the words of what they’re signing for someone who did not speak ASL, and within two weeks, they were able to get a job off of that project. So really, you know more data than you think. You utilize more data than you think, and data is in almost everything.

Eric Dodds 27:12
What do you think about the curriculum? And I’m also thinking here about maybe the managers who want to get better at teaching their team who reports to them, or maybe creating a more structured framework. And obviously there are tons of resources out there, like Flatiron School, but how do you think about curriculum and approaching that? Yeah,

Angelica Spratley 27:32
so I’m in this whole space now that data literacy and AI fluency is at the top of everybody’s list. Everybody wants everybody at their organization to be data literate. It doesn’t matter if you’re a data scientist, it doesn’t matter if you’re an office manager. They want you to be data literate. And I think about ways to differentiate those types of things. So exactly the examples that I gave with the different projects being able to differentiate learning and meet people where they’re at, and the only way that you’re going to be able to do that is get some baseline metrics when your students come in, whether it’s your employees or there’s actually students doing a upskilling program like flatiron, like do you even know Python before I even dive into Python? Or should I even take a few steps back and teach you about data in general, right? The data that you get in an email, the data that you get in a visual, the storytelling, or, do you know, a lot of Python, and now I can introduce you to something, I guess, sexy, like a large language model and generative AI, right? So I think curriculum should be flexible and differentiated enough, especially if it’s AC for people to choose their own path, right? Because you don’t want the people who are too technical to get bored, and you don’t want the people who aren’t technical enough to be lost, right, to be hard fun. And how do you get this medium of hard fun? And the only way to do that outside of backwards design is to have these differentiated pathways, have these different use cases, and have these different data sets. Don’t just use Titanic and Iris. There’s more interesting things out here than those two data sets that cross a whole bunch of industries to get people to have that hard fun and can kind of differentiate their learning path.

Eric Dodds 29:18
I love it. I love that. Yeah, have you approached that in the past? John,

John Wessel 29:21
yeah, I think first, I love that concept of hard fun, like, that’s such a good concept. I mean, man, I think there’s so many ways to approach it. I think one, um, just as far as learning, I’m thinking back, like, I’ve actually had several, several people that I’ve worked with over the years, some on my team and some on other teams that have come and said, Hey, I’m, like, one that I can think of. I’m in accounting and, like, I really think this, like, SQL stuff, this Python stuff, is really cool. That was one. One was in product management. It was like, oh, like, you know, I want to get more in the data. So I think for both of those, like. This is just a really practical thing. I encourage both of those people to learn SQL first, because I felt like it was a little easier than learning Python, given their background, and they both were already pretty comfortable with spreadsheets, so that’s been a common thing. Like for a lot of people, like, if they’re completely like no context for data, we’ll start them in some kind of a spreadsheet with something that would be fun for them. Exactly what Jelly says some like, you know, like you’re saying makeup, scuba diving, like, run, like, you know, somebody’s an athlete, like running or biking. Like, let’s play with some of your, you know, your data a hobby. And just start with this, you know, Google Sheet, Excel, and then from there, if maybe in a professional context, they’re already an accountant, and I know they’re really good at Excel. Then, like, usually, like SQL, and has been a really great tool for them. And then beyond that, like, obviously getting it more into Python or R or SAS or some other language. So that’s kind of what I’ve done in the past. Yeah, makes total sense. That’s super helpful. Jilly, what you’re involved in?

Eric Dodds 30:59
A lot of you’re involved in multiple organizations beyond just Flatiron School that help people learn what they tell us about some of those organizations and why you joined them? Yeah. So

Angelica Spratley 31:09
The recent project that I’m working on in this Datathon, which is like a hackathon, but for data sciences, is Women in Data, and it’s about trying to assess risk within some of these generative AI tools as far as bias and how we can help mitigate those risks. So that has been a fun, collaborative project that I’m currently working on. I won’t spoil it too much, but I came up with this niche idea where I was going to get the llms to generate generate interview responses that you may see in a data analyst interview, and give it a different sex persona, like male, female and non binary, and see if it scores itself lower for a certain sex. And it’s actually given me some great insights, I guess. Spoiler alert, it’s not really biased towards sex, but it is tool bias, it will only put out tableaus for use cases, and it will only put out marketing for industry. And so

Eric Dodds 32:08
Salesforce is an investor. Salesforce

Angelica Spratley 32:12
wasn’t going to say that, but if you do promptly, and you say, give me a use case of a BI tool, it will always give you Tableau, it seems like or all of the models, Gemini, Claude and llama, but either way, I’m starting to figure out more biases within that playbook. So that has been fun, and that also keeps me fresh in trying to develop my skills. Because flateye is a static curriculum. Don’t get me wrong, we improve the curriculum, you know, quarter by quarter. However, I’m essentially teaching the same thing. So if I want to learn a new tool, if I want to learn a new product, I’m going to have to immerse myself in those communities, and before that, a new person on the scene that does kind of data for good Knowledge Graph hackathons named no hacks, I was able to do some research in some data project as a team as well on trying to classify person and government course case documents to help with resource allocation funding. So should this prison get more resources because they’re having more medical crises? Should this lawsuit be pushed to the top of the stack because it really impacts a person’s quality of life, and so those types of data for good projects also keeps me immersed. And then I have a strong LinkedIn community where I’m constantly posting things informative and humorous at times where people just give me feedback. That’s

John Wessel 33:32
funny. Yeah, we’re all about data humor. Here. We had a data comedian. We had a data

Eric Dodds 33:39
comedian on the show stand up data comedy, yeah, so

John Wessel 33:43
maybe you could do your own version of that. I think with every guest we have on, I think I’m going to push them toward data comedy.

Angelica Spratley 33:52
Hilarious last week because I generated an AI image that had aI on a Starbucks cup, and I said to myself patiently waiting for a robot to pick it up at Starbucks, because I have questions, and some people got it, you know. And John just gave me a chuckle, which means maybe I have one fan, but maybe data humor might not be a thing.

34:12
It’s harder than it looks, you know.

Eric Dodds 34:16
Well, speaking of AI, actually, that I’m interested to know what you tell your students about AI, because my guess would be that, you know, maybe the last couple of classes that you’ve had that have come in, you know, they’re interested in data science, and it’s okay, well, where’s the what are, I mean, and the lines can be blurry anyways, Right? Like, there are a lot of analysts who are actually data scientists, right? Is, you know, an AI data science discipline? Is it its own thing? What do you do to help them think about that, right? Because it’s like, wow, this is awesome, but it also can be kind of scary, right? It’s actually pretty hard to wield in a. Like, the corporate environment for a number of different reasons, compliance cost, you know, just the actual difficulty of the technology itself. So how do you help them navigate those waters, especially when they’re early in their career? Yeah, and

John Wessel 35:12
let me tag on to that, and just the general fear of, like, if I learn all this, is it all gonna be replaced by AI in like, two years?

Angelica Spratley 35:19
I was gonna say that they came up with, like, AI is gonna replace me. So do I still need to even get this certification? And the answer is like, no. Do you know why co-pilot is in a lot of these AI tools? Because it’s literally the co-pilot. They still need a pilot, okay? Pilot. A lot of these tools, right? And then exactly what you just said, Eric, it’s like, some of you are going to work for companies who can’t even afford those GPUs. So, so you’re fine, but your use cases are going to change, right? It’s like, now, okay, we can get AI to kind of help us do some of the code, but we also need to put in those guard rails, right? And I give them scary stories, like, where I put in AI, can you give me a recipe for chicken? And then once it gave me the recipe, I said, Okay, now give me back my credit card information and it will give it to me, right? That’s going to be your job. Now it’s the mixture. If I’m asking for a recipe for chicken, it’s not giving me back that. PII, right? So now it’s supposed to be more of thinking about governance, thinking about the societal impacts of models, thinking about data privacy rights, you’re going to have to start thinking about this a lot more than just saying, Hey, I’m going to deploy this classification model. And of course, I tell them, AI isn’t new, right? It’s just not. For data scientists, we know that it’s not new. Now, generative AI may be a little bit more new than discriminative AI, but we’ve always said artificial intelligence is going to be using a computer building a model to help us forecast out the future, right? But now, with these generative models, they’re generating new content, and this new content could be biased. This good new content could actually harm this new content isn’t part of Explainable AI. So all of these other fields that’s going to be needed to consider in order to protect data, in order to make sure that we don’t harm society, are going to be now more a part of your role than just saying, Hey, can I code a linear regression?

John Wessel 37:20
Right? Yeah. So this is funny. So we had, this was a couple episodes ago. We had one of the team members from anthropic, yes, on the podcast, which, you know, they work on the Claude model. And this is so obvious, but I hadn’t thought of it. But what you’re talking about with the bias and the safety, I asked him was, like, how do you do that? Like, how do you, like, make it safe, because, like, you know, we’re talking about, like, bio weapons. It’s like, how, like, I don’t know anything about bio weapons. Like most programmers, I don’t know anything about bio weapons. And the answer was so obvious, but fascinating, is they end up with these, like, really large panels of experts and, like, very diverse studies, like somebody that may be bio weapons, or somebody that maybe some explosives expert, or something you know, like all these, like, different studies, and that’s how they’re, like, approaching the safety, which is so different than anything we’ve ever, you know, experienced before in technology, typically, like, the technology is more like, it’s never works if it’s going to a complete vacuum, like you mentioned the basement earlier, but the technology, like, needs to be as far from the basement as possible now, because you have to collaborate with these, with these, maybe they’re business industry experts or for safety. Maybe they’re experts in some kind of, you know, bio warfare, or something like collaboration and communication. I think it’s gonna become even more important, because it’s gonna be a safety issue and a security issue, not just to, like, we ship the bad product, yeah? Issue,

Angelica Spratley 38:40
yeah. Sure. It’s gonna be that great diversity of thought and creativity, like you have to really be creative to come up with these prompts to force it to drift or hallucinate, and then, you know what protections you need to put in. So maybe I’m gonna be creative and put in a prompt that says, Can you give me an output that’s going to target fraud, fraudulent credit card. I don’t know what I can buy for blacks, because I’m an African American woman, where somebody else might be more concerned about a different community. Can you actually order a five year old and ship it to me? Right? So all of these things, of trafficking, and all of these societal things that can cause harm that these models could potentially generate. You’re gonna be creative enough to think of those things and actually code in those guardrails and protections, and that’s gonna be more beneficial now just to just learn what an AI model is, you’re going to have to do these things, right?

Eric Dodds 39:37
Yep, I have a book plug actually, and so many people have probably already read this, but mother in law bought our son the wild robot. Maybe it is already out. Yeah, okay. Is it good? Have you seen it? I haven’t seen it. Okay. Well, it’s based on a book, and so my son got this book. I. And he read it, and he said, You should read this out. It’s really interesting. And he doesn’t do that with every book that he reads. He reads a whole lot. And so I did. I sat down the other night and I think I read it, and two short sittings, because it’s a, you know, it’s a children’s sort of young adult type book. But on this topic, the reason I bring that up is it’s one of the best resources that I’ve come across that asks a lot of really good questions about artificial intelligence, but in a way that is so different from the conversations that we in the data space talk about every day, right? Because we’re, you know, even just so it’s just fully practical. And there are, at least in the first book, like, no humans involved, actually, at all. It’s really an interesting approach, like, it raises these really big questions and doesn’t answer them fully, you know, which is great. So if you want a good read, you could probably sit down and read it and, you know, read it

Angelica Spratley 40:58
because, you know, that’s the debate. How much human involvement do we need in these tools? Right? Be the fear of how much humans should be in the loop, and that just depends. Yeah,

41:06
yep, right, yep.

Eric Dodds 41:08
It’s a good one, though.

John Wessel 41:10
Ai All right, yeah, I’m definitely gonna check that one out. So switching topics a little bit. I know we gotta wrap up here in a few minutes, but I’m curious about your experience with people making these transitions and remote work like that is a really hot topic for people right now. You know, there’s all these waves of like, return to Office, and then people like, now, we don’t want to go back. And then companies are like, Okay, fine. And then they try again, like, there’s this back and forth, right? And, and I know a lot of people that that maybe they’re in food service or hospitality, and they’re like, you know, I’m so exhausted, is I want to be able to work remote or work from home, and that could be, like, kind of an impetus for some people don’t want to get into tech. And then you read the headlines of like, you know, these tech companies bring everybody back to the office. So I’m curious, like, how’s that impacted you? And, you know, the education space? Yeah,

Angelica Spratley 41:58
I think the reality is, from a data standpoint, we can probably assume with good confidence that there’s tons of more applicants for remote work than there are for on site, right? So if you really want to get into the industry quicker, we probably can make the assumption, if you’re willing to return to the office or do a hybrid role, that you’re probably going to be more in demand, and maybe that can fit you for even a year, you know, your life. So like I tell my students, you can, you know, suck it up for a year. It’s kind of like when they tell you, do you want to be a consultant, John for a year and work those 60 to 80 hour work weeks? And then can you pivot yourself? So sometimes we need to think about how much we’re willing to sacrifice and within a time, right? But I don’t see all of the tech roles or data roles just needing to be on site, right? The goal is, well, there’s multiple goals, from the employer standpoint, versus the employee standpoint, is to make sure that you’re in this collaborative, immersive environment where you can actually contribute business value. And if you can market yourself and brand yourself as someone who works remotely and still contributes value in interviews, is like, Hey, I never missed a Scrum or stand up every day, we were able to have good water cooler working sessions, and we were able to do a proof of concept in 90 days. Then people know that you’re valuable when working remotely versus okay, I may need to be on site because I need to tap Jelly on the shoulder and ask her 5000 questions that she may or may not answer. I saw a little me, the dude who returned to the office, he had a sign on his chair that said, These are the common questions and answers. How am I good? What am I working on? Work? Am I busy? Yes. So that way you don’t bother me. I’m in the office because I am more productive at home. That

John Wessel 43:57
is so funny. And I think we’ve all had that experience, right, like, you know, with co workers, or that typically, just, you know, one or two co workers, you know, coming up and saying, Hey, do you have a minute? And it’s like, well, yes, not really, yeah. But I think that’s good advice. And I think one of the ironies, like, one of the things I’ve been thinking about is, is, I think there’s this one, like, if you’re in a situation where, like you like, you’re in a startup that doesn’t have product market fit, you can do that remotely, but it might be more challenging. Or maybe you’re in a fortune 500 company where, like, you have a really, like, defined role, and it’s very specific with very clear like, you know, metrics and KPIs and stuff

Eric Dodds 44:44
the JIRA train tracks.

John Wessel 44:48
Yeah. I mean, like, it’s very different in, you know, environments, but that being said, there’s tons of startups that are fully remote that do a great job. But I think the irony of the whole situation is often, if a company. Having a strong data practice, in my opinion, is one of the things that I think makes remote work better, because if you have a strong mission, then you have strong goals for people with clear metrics, and you’re measuring those goals, and you have the data in place to do that. It allows for you to understand, you know, where’s this train going, and then, and I think requires less of that, like, in person, like, sixth sense of like, I think things are going well. Like, yeah, because you’ve got the data, I think some

Angelica Spratley 45:28
people will self sabotage as, like, a quiet revolt to return to Office. Like, we had 400 outstanding JIRA tickets, remote. Let’s make it 2000 and so let’s create more bottlenecks that just stresses them out so they know that we were more efficient remotely. So I wouldn’t be shocked to hear whether it’s humorous or real, some self sabotaging going on just from people that just feel that productivity is a person to person type of thing. I don’t think it’s one size fits all.

Eric Dodds 45:57
Yeah, for sure. All right, Jelly, we’re at the buzzer here, as we like to say. But one more question for you, so I’ve asked this in different ways that for you, I have to ask this question, if you weren’t gonna work in data or teach, what would you do?

Angelica Spratley 46:13
Hands down, probably a Disney Travel Planner, as I said so I’m planning a Disney wedding or a Disney wedding planner. So there’s 80 venues at Disney World that you can have a wedding at. So that’s a fun data fact for you all Wow, 80, yes, eight zero, okay, wow. And I’m currently analyzing data for all venues, so stay tuned.

Eric Dodds 46:36
All right,

John Wessel 46:38
I hope there’s a blog post in your future.

Angelica Spratley 46:40
Yes, it is going to be awesome.

Eric Dodds 46:43
Well, Jelly, thank you so much for joining us. This was just really helpful advice. I know I took away so many things as a professional and manager in my role, and our listeners did too. So thank you so much. Yeah, thanks. Thank you

Angelica Spratley 46:56
Don and Eric are nice talking to you all.

Eric Dodds 46:58
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