Sadie St Lawrence is the Founder and CEO of Women in Data, the #1 Community for Women in AI and Tech. She has trained over 350,000 people in data science and has developed multiple programs in machine learning and career development. Sadie was named 10 Most Admired Business Women to Watch in 2021 and has been listed as a Top 21 Influencer in Data. Her work has been featured in USA Today, Dataversity, and she is the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on multiple start-up boards and is the host of the Data Bytes podcast. In her free time, you will find Sadie painting abstract art or sitting in the sun daydreaming about the future.
Transcription:
[00:00:00] Sadie: And I think the big thing you're touching on too is really just the adaptability aspect, right? Like, I had to adapt from less structure to structure. You had to adapt from structure to learning on your own and creating that path. Right. And that, and so I think what we all can work on, whether we are homeschooled, whether we have traditional school, whether it's classical education, private, whatever it is is that adaptability and ability to learn.
[00:00:38] Ken: Today, I had the pleasure of interviewing Sadie St. Lawrence. Sadie is the founder and CEO of Women in Data - the number one community for women in AI and tech. She's trained over 350,000 people in data science and has developed multiple programs in machine learning and career development. Sadie was named one of the top 10 most admired business women to watch in 2021 and has been listed as a top 21 influencer in data.
Her work has been featured in USA Today, Dataversity, and she's the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on multiple startup boards and is the host of the data bites. In her free time, you'll find Sadie painting, abstract art or sitting in the sun daydreaming about the future.
In this episode, we learn about Sadie's experience being homeschooled, how she ended up in data from piano performance and the biggest lesson she's learned moving from being a data scientist to a founder. So Sadie, thank you so much for coming on the Ken's Nearest Neighbors Podcast. We got introduced through John from the Super Data Science Podcast, and I couldn't be more grateful. You have such an incredible story, and I'm really excited to be able to share it with the community here today.
[00:01:50] Sadie: Ken, it's a pleasure and it's such a small world because when I met you, I hadn't realized that a couple of weeks before I had listened to your YouTube video about why to start a podcast and then small world here, we are chatting with each other on a podcast. So I just love how things come together and it's going to be great chatting today.
[00:02:09] Ken: Some of that like beautiful. I wouldn't, I don't know if I call it quite manufactured serendipity, but it's one of those things where it's like, Hey, we can be here. And, you know, the, the work we've done, the content we've produced has been able to make all of that possible, which I think is again, a really cool sort of unintended network effects.
I would love to be able to get everyone listening, more familiar with your story. And I usually do that by having you talk about how you first got interested in the data domain. Was there one pivotal moment that happened where you're like, Oh, I love this. Or was it sort of a slow progression over time?
[00:02:47] Sadie: Yeah, I think that the biggest wake up call for me was when I was working in a neuroscience lab and when you're doing science experiments, you have to collect data and analyze that data. So you can write a paper on it. And I really fell in love with the analysis side of things and not so much the data collection, the data collection portion was very tedious.
I was working with rodents and I would have to take care of them every day. So it's like a school pet project, right. I had to come in and feed them and care for them. And then like to collect one piece of data would take like hours all day just for like literally one cell and row of information. And it was quite tedious.
And so through that experience, I quickly learned, and then not enjoy data collection, but I did enjoy the results of what I got the data and what I was able to analyze and look at and the statistics I was able to run on it. And so that's what really woke me up to finding more of what I love and led me to data science.
I, you know, one day had to euthanize a rodent cause I was done with my experiment with it. And it was kind of traumatizing thing because I'd taken care of it for six months, you know, spent every day with these animals. And then I felt just so bad because it was just like, I'm done with you. And so now I'm disposing of you and I felt like it was such a waste.
And so, took a little step back from there on a Friday afternoon, it was like, Okay, what parts of my job do I love? And what parts do I not love? And it was like, I love it. When I get the data, I hate collecting the data and I hate doing harm. Like I do not want to do any science experience where I feel like I have to do harm.
And so that weekend through a Google search, I was able to find the term data science again, don't know if it was an AI algorithm that showed me an ad. If it was algorithms or serendipity, not sure which one it was. It's a little fuzzy sometimes at that point, but yeah, that allowed me to start researching what the field was.
And I was like, this is it. I love that. It includes data that I don't have to collect. It's already collected through digital devices. I love. At that time, I felt like it wasn't doing harm. Right. It was had the opportunity to take insights and do a lot of good with it. And so I quit the lab that Monday and decided to start my path into data science.
[00:05:10] Ken: Well, I love the level of introspection news hook. And you really, I think a lot of people don't break down their work or the things they enjoy and they don't enjoy, you know, they look at like, Oh, I don't like being a researcher. I like being a researcher. A lot of times you get so much benefit by. Not compartmentalizing those, but breaking those down and understanding the individual parts that, that we, and the individual takeaways that we have.
It's funny. I had a couple of friends in psychology who had similar experiences with euthanizing, their outside of friend who literally had like six routes at home because he felt so bad. He would just like, take them all and keep them. And, you know, he had to like fake something for the lab, redo stuff like that.
But no, that is really not a fun part of the job. And a lot of my friends who have come from the, you know, some of the more like traditional sciences, it's funny. Yeah. A lot of your job really isn't doing science, right? If you're in like a chemistry lab, a lot of the time you're spent just like pipetting stuff, right.
Or you're spent like putting things on on, in Petri dishes. It's like, it's not I feel like really grateful for our work because a lot of the work we do yet, like, yeah, sometimes we're like scraping data. We're collecting it, we're organizing it, but that's still like a broader part of our work.
It's still like the scientific portion. It's still important for the end goal. You know, like only we could do it or data engineers could do it rather than like, I feel like anyone could put pipette stuff, right. Or anyone could, could put things in a Petri dish if they have the proper training. And I don't know, it makes me feel like utility and useful in the work.
[00:06:59] Sadie: Yeah, I really love, I'm sure seen those means. And they do it for every job description to like what my mom thinks I do, what my friends think I do, what my boss says to do what I actually do. I think it's a great way to actually describe any job, regardless of what other people think we do.
There's many things facets to a job. And just as you mentioned things aren't black and white. It's not like, Oh, I'm a researcher or a data scientist or a doctor. This, if you go down a layer below it and look at skills, you realize like a lot of the skills, regardless of the job title are transferable. And so I really encourage people, whatever they're coming from and whatever they want to transition to stop looking at your title and look at your base core skills.
And you're going to find some that match in that transition. And I call it like finding the bridge, right? Like what's the thing that transfers over pretty much in any job. It's like communications, like, no matter what job you're doing, like you need to talk to people.
You need to communicate what you're doing. If you're a scientist, right. You need to write reports. If you're a data scientist, you need to tell data stories. Like there's going to be some transferable skill. And when you find that transferable skill, that's your bridge to the new career. And for me, I have built many bridges and it's been many, it's been a lot of fun because it's allowed me to transition multiple times.
[00:08:20] Ken: Well, I'm absolutely gonna steal the bridge terminologies. There that's something I 100% agree with. I hear a lot of people saying they want to jump into data science, so they want to switch into data science. And I hate the terminology around that. I'd much rather hear people say transition or do something like that.
It's so hard to like, Hey, I'm going to forget everything I learned and learned this new domain and trying to have success in it. It's like you spent a lot of time working on something else. It's not a sunk cost, right. There's value. You can find it. Not that I am certain would be useful in the data domain I had on the podcast, someone who was a, a warehouse worker.
Right. And a lot of people would say that's very different from data science, but he was able to find some really unique like interpersonal elements or, or organizational elements that you've been able to bring into this role and make him unbelievably successful in it, which I think is just so cool.
So th you know, echoing what you said, 100%. I love that bridging method terminology as well. So I also want to ask you know, you have a fairly unique educational background for someone in data science. I mean, you've told me before that you were homeschooled until college and I'm interested in, you know, how that transition was for you know, going into college and eventually into data science.
A lot of people, you know, I would imagine that there's like with homeschooling there's different elements, there's different benefits and drawbacks. And I just want to kind of break down that, that story for the audience, because I know only a few people who were homeschooled through college up to college and. I haven't asked them enough questions about what that experience is like.
[00:10:09] Sadie: Yeah, no, and it's a great question right now, too, because I've seen some stats on homeschooling that they spend the most year over year girls, and they think it's attributed to the pandemic grade. I think a lot of parents out there became teachers, whether they want it to or not, and then either fell in love with it.
Either found that it worked out better for their kids. Right. And so we're actually seeing a big uptake in just homeschooling overall, at least in the U S so when you asked me that question, I will preface that. My answer probably would have changed on my experience 10 years ago and what I thought it was.
And now, you know, how I interpret my experience of it. So, you know, growing up, I really wanted to go to traditional school. I just. I don't know what it was in me as a kid, but I really wanted to go to traditional school. And unfortunately my parents were pretty set on like, Nope, we're homeschooling, and this is our way.
And we kind of don't care about your opinion. Well, I need to go to Jewish school. So probably going up, I didn't have the best attitude towards homeschooling. And, you know, I knew that I wanted to go to college, but I had never taken a test in my life. So the first test I ever took was the act test. And I will tell you, I did not do very well on that test.
I mean, going into the test, I had asked somebody, you know, what are these, what is the sheet you gave me with little bubbles on it that I have to fill it. Right. That kid sitting next to me was probably like, Ooh, you're going to have a rough time with this. Right. And I did. But thankfully, like I, what homeschooling did allow me to do was it allowed me to explore my interests really deeply and allowed me to.
Develop and find teachers that specialize in those six. So one of the things I was able to do was really dive deep into music, music theory, and play the piano, et cetera. And so that allowed me to get into school. I pretty much failed my act test. I think I would've got a better score if I just randomly guessed answers.
But what it did allow me to do is make me spend more time on the piano and I was able to get into college. And then in college, you know, talking back to the bridge analogy, which I shared earlier one of the things that I was able to do was because I had such a rich, fundamental knowledge of music that allowed me to find the bridge to explore other subjects.
And so from that foundation music, I was able to adapt really quickly to other subjects and found out I really loved math. I found out I really love science, right? And then getting into school. I realized, well, I'm actually not that dumb. Like I'm actually kind of smart. Like, you know, even though I felt the act as they ended up getting straight A's, you know, all through college and was able to see like finding that bridge through piano really allowed me to be able to explore it.
So, today now as I look back, right, I said, I have a two-party answer what my experience was in the beginning and what it is now, today, I look at it as a gift, right? It allowed me to become a really independent self-learner. I'm able to read books and teach myself things really easily. I'm able to dive deep into subjects and I'm able to draw correlations between things because I didn't sit in a traditional method.
So now when I look back, I see a ton of benefits for me. And usually what I tell people is, you know, it really just depends on the individual, right? Like there's pros and cons to any style of education. It really just depends on what fits that individual. And you know, if you're teaching your kids, what kind of experience you want them to have?
[00:13:53] Ken: I think that that's really fascinating if we frame it in terms of. Learning a new domain, like, like data science. I mean, in the initial phases, like, you know, five, six years ago, when, when we were both kind of getting into the field and learning the skills, there weren't necessarily as many formal channels or as many informal channels as it were.
And so like even in a program or in like a master's degree, for example, there weren't as many options. Like you had to still trudge your own path. You still had to find your own way. You still had to like figure out projects and do them on your own. Hey, at least for me, like the classes I took taught me maybe 50% of the things that I needed to know why when I eventually got into the domain and I was sort of the opposite.
I, well, first I was a terrible student up until college and I eventually kind of fell in love with school. Cause I realized I could, like, I could like a game, a fight and I did very well with structured education and it was very difficult for them. To pick up new things that were not in a formally structured way.
So I had to build that structure into my own life. I had to figure out something that worked for me, and I think it's really cool. Like, you know, in the early stages, yes. Maybe it, it, it caused some struggles with standardized tests, but in the longterm, I think, you know, from that experience, and I don't think people have to have that unique experience of homeschooling to be able to develop the skill.
But I think it's worthwhile for everyone to cultivate the skill of, Hey, I can pick this up. I can sort of hustle to, to learn this. I can create these new connections. And when you do that in life I mean, again, like, it was probably harder for me because since I did it later, but I just really relished that that type of skillset that you've cultivated there. And I would, again, encourage everyone just. Kind of pursue that if they can. But the hard thing is there's no roadmap for learning that skillset. Right. And that's a little bit of, yeah.
[00:16:01] Sadie: And I think the big thing you're touching on too is really just the adaptability aspect, right? Like I had to adapt from less structure to the structure. You had to adapt from structure to learning on your own and creating that path. Right. And that, and so I think what we all can work on, whether we are homeschooled, whether we have traditional school, whether it's classical education, private, whatever it is is that adaptability and ability to learn. And one of the things like I've fallen in love with.
In general, because I think there's a lot that we can learn from like the history of education and how we've evolved as a species and to really where we're teachers or not. Like, I think we all need to learn, like, what are the best learning methodologies and strategies for education? Because especially as data scientists, right?
We have to be lifelong learners to stay relevant in this field and to be able to continue to progress, especially with the new technology coming out.
[00:17:10] Ken: Absolutely. And, you know, that's, that's a pretty interesting in the scope of more traditional formal education. Like I've spent a lot of time in formal education, but I also have some major issues with it.
I mean, I've realized so much within our own domain. That the technologies that people are learning in school, they do not match or keep up with what you need in practice and in the real world, because a domain like data science, even a domain like software engineering, they move so much faster than academic curriculum can keep up with.
And, you know, I really do think the answer is more, self-learning more you know, online courses I think are great because they can keep up faster, maybe not as academically rigorous, but they're, there's this trade-off. And I think that, you know, in a future world, you know, what does that look like?
Where do we, where do we find ourselves? Are we going to be exclusively learning on these platforms? I would kind of hope that we do reach a place where expensive, formal education isn't as needed. But I'm interested to hear your take on that as well as you know, is. You know, is the prerequisite like it's trending, Oh, we need more degrees, more degrees.
I think in some companies like Google, that's going the opposite direction, but the general trend is over time. Been let's sack on degrees. Do you think in this domain that's going to be important or do you think people are going to be able to, to really tackle the domain from free or really cheap online resources?
[00:18:48] Sadie: Yeah, I think we're learning that it isn't just acts. Right at the, so what are the things that we've seen is with platforms like Coursera and et cetera. The whole idea was like, Hey, we could just give access to people to some of the best universities and some of the best teachers and some of the best content like that will somewhat fix education.
Right. But what we've seen is like, anyone right now can go. You all of the lectures from the Harvard CS degree. Right? So if you want a bachelor's degree in computer science from Harvard that is available and free to you right now. But when I look at that, like how we've really seen the impact and results from that?
No, we haven't, like, we originally thought it was an access issue. So we gave people access to information online. Right. And now some of them charge some of them don't, but overall you can pretty much get it for free if you're in financial need to be able to do it. So I think what we. And again, I don't think there's anything wrong in that we started there.
I think that was a good starting point. I think I look at everything in education as a hypothesis, right. We thought our hypothesis was like, Okay, people just need access to education. So we tried that it didn't work. And so what we're finding now is, Okay, what is the additional parts of like going to, let's say a Harvard and getting a computer science degree that isn't carried over to just having the lectures and the content and stuff online.
And I think what we're finding really is there is a network effect. There is a social learning effect, and that is what we're missing from traditional MOOCs education online is that social learning that ability to talk with your peers, not just talk to your teachers, but to be able to talk with people who are struggling with the same problems who are maybe just like an, of head of you and just figured out the problem, like those are the people who can really bring it down to your level.
But also those are the people who like you make connections with, who really help you accelerate in your career. So I think that from an online education standpoint, we're really, really missing that like social learning. And then also just the network effect of being able to learn with other people.
Additionally, though, the second problem I see that it's missing is the apprenticeship, right? The ability to actually practice and do the work. So much of online learning right now is very passive, right? I'll watch a lecture. Maybe I code a little assignment, but it doesn't model how the real world works in terms of like figuring out of a problem, being able to find those answers yourself.
So I think for me, the next step is really how do we add a social learning effect? How do we also add in a network effect? And then how do we also add in an apprenticeship model as well? So what I've developed is like a simple learning formula that I use. I use it for myself all the time. And it is learn, do share, and repeat.
And the reason I have this as the is one, the learning aspect is pretty self explanatory, right? I need to read some information, watch a video, et cetera, but the goal is to have it in bite sized chunks. So that the second I learned a small piece of information, I'm already putting it into practice. Right?
Usually I pick a project where I don't know anything about how I'm going to do it. And I only learned the piece of information I need to get step one done. So I'm quickly iterating from learning to doing then. The third aspect of share is very, very important. So this is that social learning aspect. This is where sharing can be telling your partner about it, telling your family about it can be writing a blog post.
It can be making a post on LinkedIn. It can be starting a podcast, right where you're sharing what you're learning, whatever it may be, but it's really important to share because it creates that social learning aspect. And then the last portion of it is the repeat side. And this is because I keep everything really small and chunks.
Iterating through this cycle continually, right. Learn, do share, repeat, learn, do share, repeat. And finally, through those iterations, it's almost like a spiral and that spiral is kind of an upward mobility that eventually gets me to my goal. So that's my own personal methodology. That's something I use and implement in women and in data a lot too, like how I've designed all our education is very much in a social learning aspect, very much a doing aspect and very much a small iteration of repeating. So that's my hypothesis and theory on education. I start to improve it. Maybe call me in a year or two and I'll let you know some of the results of it.
[00:23:34] Ken: Well, you know, it's interesting. We were talking about bridging earlier and that seems like a very. Common process in music, right? Where you're learning, you're literally hands-on applying and then you have to share with people and get feedback and chapters, whatever that might be is that where you started to originate that learning methodology. And I'm interested in, you know, more broadly, how you transitioned from music eventually into psychology and then eventually into data and learning those skills while we're on the top.
[00:24:09] Sadie: Yeah. So I think it's the, the formula has come from my own personal learning journeys from being homeschooled to doing traditional university, to then teaching people. I had a piano studio, I taught people music. I now have classes on Coursera. I found myself always somehow teaching people. And then through women and data, I have an amazing community where I get to, you know, hear some of those struggles that they're having in getting a job or learning.
And so from all those experiences, it's kind of just boiled down to like, Okay, here's the philosophy and problem. I think what music really taught me was just the power of imitation. And this is a tried and true method, whether it's music, whether it's art, whether it's literature, this is what I would say is like an education principle is that when you're getting started, you copy a great, right.
So when you're starting to learn music, you don't start by composing your first song, what you start by learning the fundamentals of the notes. And then once you have the notes, then you can start to read a little bit. And then when you can read, you play other people's music. Right. And why is it so important to play in other people's music is because it allows you to get familiar with the structure, right.
To see what's the like, optimal way to do it. And in music we have. This the sane, which is you have to learn all the rules so you can break them. So you actually don't get that freedom of being in a way, like a great composer until like you have a master self in so much literature, right. That, you know it in and out that now you have in a way like the creative freedom to go and create your own.
And it's a very similar process in science. Right? You read other research papers and you have to read hundreds of research papers before you can even come up with your own theory or thesis to develop. I look at this as like kind of a tried and true education principle, which is like, if you're getting started in a field, like take somebody notebook, work through the notebook, like start finding people, you really admire and copy of their work right now.
You're not, not copied in the sense of like, copy the code, put it in and say, this is what you did, but copying it in the sense of like, I'm going through this exercise as a learning journey. When you do that enough times, eventually it's going to become second nature to you. And then your creativity of other ways to explore that problem and idea are going to start to develop. So for me, music really taught me, like, you have to learn all the rules before you can break the rules.
[00:26:52] Ken: I don't know. I, without lack of other words, I think that's a beautiful sentiment. I think so many people, especially in learning, coding and learning the data domain, they feel like they're cheating themselves when they copy someone else's project or when they're working through it.
And that's absolutely not the case. Right. You're understanding the structures of why they're doing certain things. You're understanding you're like effectively looking into their, their brain to understand the logic that's going on. And I think that with music or with, with any domain, if you're copying something, you can always like tweak it a little bit and see what happens.
See if it sounds good. See if you get good results, whatever that might be. And what you're saying with repetition and copying mixed with experimentation, I think is what the secret sauce is. Is that again, if you have this understanding of the fundamentals, then you can hypothesize about what happens if you make an adjustment.
And you can eventually see if you were right or not. And you can iterate just like you're describing with your learning philosophy. I am interested, you know, in specifically your experience, learning programming and learning more technical skills. I mean, psychology, you have to do some like quantitative analysis, but you're using mainly like I use SPSS when I was like in a psychology program.
How did you make the technical transition into data? I mean, it you're going from like a lot of people would say music is like one of the furthest things. Like it would be either like music or theater, which we have described isn't necessarily the case. There are these really cool parallels, but you know, that your journey into actually learning technical skills might seem overwhelming to someone like, wow, that's as far as you can go, how did you actually go about doing that?
[00:28:49] Sadie: Yeah, it's a good question. So when I did my like self analysis, I do like little grids of like, what are transferable? What are, I knew that my weak point was like coding skills, right? So like SQL and Python. And again, it's the bridge technique. So when starting to transition analysis over from SPSS to Python, I start with the same statistic instead.
I know, right. Because I know how to do it in SPSS. So if I'm doing it in Python, I can easily see if the answers are right or wrong. Right. Instead of starting from scratch, right. Like find the bridge. So the bridge is like, I know the algorithm, I know how to run a T test. They know how to run a Nova. Right.
I know how to prove statistical significance. So start with something simple. That is that bridge. And so from there, like it, like, yeah, there's more to add on to it, but I had a really strong, fundamental base then when it's like, Okay, I can run these basic statistics in Python. Now it allowed me to learn more Python.
Now I can do more to explore additional libraries and additional algorithms that I may want to use. Right. Again, though, most of it isn't I think we make things more complex than they need to be because of. I actually started with SQL cause I thought SQL is an easier language to learn than Python. And it was, and at the end of the day to most of it, if you even all boil it down to a level below, that is just a critical thinking skill, right?
So I look at everything as like a tool, like the base of it is critical thinking, but like maybe the critical thinking my tool I'm using is playing the piano versus, you know, using a coding language at the end of the day, I'm still using some of the same brain connections to determine like, is this right or wrong?
Does this logically make sense? Right. And so, as long as they find that bridge, it it's able to accelerate from there. So what I tell people all the time is like, I think we count ourselves out before we even get started. Right. We're like, Oh, this is too difficult and I can never do this. And you've already.
Cut yourself out of the game half way before you even started, right. Yet at the end of the day, like, do you have some critical thinking skills? Yes. Then you're probably going to be able to make it and be fine. Is it going to be a little painful because you're making neural new neural connections in your brain?
Of course it's like exercising, right? If you get, you're trying to build some new muscles the next day, you're a little sore because it's stretching and growing. Yeah. It's going to hurt your brain a little bit, but like you're able to do it because you have that muscle there underlying the critical thinking. It just has to grow and flex in a new way for a language.
[00:31:46] Ken: Well, you know, I had a very interesting thought when you were describing that about how we're making it harder on ourselves and, you know, fundamentally these programming languages let's think SQL it wasn't designed to be done. Like the people who made it, made it as easy as possible to do, what would you like to do?
Same with Python, same with these other languages. Like they're designed to be as user-friendly as they could be. They're not designed to trick you in any way or to make it confusing or anything along those lines. So the idea that they're like, you know, that, Oh, it's so hard, like, yes, for someone picking it up, it might be difficult to learn, but they're already structured in ways that are as user-friendly as possible for people out there.
And so, you know, when you think about how confusing you in theory could make a programming language, if it was like, if it was a friend where you're trying to pull the wool over on someone's eyes. Right. And so, you know, that idea I think is pretty interesting that these, these challenges, aren't there to trick you.
You know, I had a friend in grad school or we would do finance homework together and he always over-thought the problems because he thought the professor, or like the book was there to trick us or something like that. And it's like, no, like if you're looking at the details, if you're picking it apart, if you're if you're asking the right questions of the problems, if you are using those critical thinking muscles that you described, the answers are, are just there.
It's not like this convoluted thing. I mean, sometimes professors can be real direct and they do that. But in the real, in the real world, like the problems aren't, they're tricky, the problems are just there and you either can solve them with thinking or you can't, which I think is somewhat liberating way to think about a lot of these things.
[00:33:32] Sadie: Yeah. And I'm so glad you mentioned that because I have a great example of how I over-thought it in the beginning and the programming language, wasn't there to trick me. So when I first started coding, I was terrified by air messages. Like I get an error and be like, ah, go on to look at it and know I did something bad.
And then finally I realized like, no, the AirMap message is actually giving me feedback to tell me how to solve the problem. Like in making it super easy. Like sometimes they're like, you have a misaligned character online, 1 29. And I go to line 1 29 and was like, Oh yep. I didn't put the comma in the right place.
And I'm like, I was freaking myself out about the air messages, thinking like it was this big negative thing. And I realized like, no, the air messages are actually just there to help me. And if I actually just read it, it's instead of freak out about what the air message is telling me, it's actually even giving me the answer.
And so I think you're spot on and like, it's not designed to be tricky. It's actually designed there to help you. And no one's trying to trick you.
[00:34:35] Ken: Well, it's a funny, weird parallel from what you were talking about before with your you know, you're able to, to break down your career in psychology and figure out what you were liking and on what you didn't like.
It wasn't that the whole career was wrong. It was these specific elements. I think coding is very much like that. And like when you're early in that, in that learning phase, you're like, Oh, all my code must be wrong because I got this error. Right. It's not just the specific thing that I need to adjust or tweak.
You know, that I can dive into to, to make that, you know, to make this actually run and work. And the idea of, again, I mean, maybe it's a common theme in this conversation, but the idea of sort of breaking things down and looking at them in isolation can be incredibly empowering because then it's just like one small thing.
This one comma, that's an issue rather than like, Oh, I'm doing everything wrong. No, that's like very, very rarely the case, unless you're like trying to, you know, right. Are in a Python terminal, then you're in some trouble. So I kind of skipped over one thing that I'm actually personally very interested in is the transition from music to psychology.
I mean, we talked a little bit about psychology to data, but that is a little bit of a leap that I guess we didn't touch on. And I'm sure the listeners would also be interested in that transition. I'm always interested in why people make like career altering or, or potentially a life altering decision.
[00:36:04] Sadie: Yeah. So with music. Obviously, I really loved it and I still very much do, but when I entered school, I got to take all these great GE classes and you know, a lot of times, again, back then, I would've been like, Oh, why do I have to take all these random classes? I think I talked to a lot of people in the reps just decree.
Like I just want to get to like my major and what I really want to focus on. But I think GE classes are some of the best because it allows you to explore things that you may not have known you were ever interested in. And so through GE classes that like I realized, like I really loved math and I really love science.
And then I was really fortunate enough that halfway through my piano performance major, someone gave me a book called music ecology. Or sorry, Musicophilia by Oliver. And so musical Filia was my bridge. All of her is this phenomenal neuroscientists neurologists. And he would write about essentially weird things that happen in people's brains that relate to music.
And so he was my bridge of like, there's a correlation between music and your brain. And I was totally blown away and fascinated by it. And it was just reading that book. I was like, yep, I want to study this. And I want to change my major. And again, finding the bridges and the key, right? He was his book, musical philia tied, something that I knew music to something I didn't know the brain and psychology, but it gave me the hope that these two are connected in some way that I could make that transition.
I again, I get an insight and then I make a decision right away. I remember leaving class and going home and figure it out. Like, I got to figure out how to change my major and going online and just like researching, like, this is not my major anymore. I need to switch my major over. And so I did, and I'm just very grateful for that book. And also highly recommend any of his work to people who are interested in hallucinations, how the brain works with. Music's really fun things about your brain. He's fantastic.
[00:38:15] Ken: I actually will check that book out. That sounds really, really cool to me. You know, one of the reasons why I asked that question at the beginning, if, how you got into the data, was it just to like a spur of the moment thing or like a one situation or was it a slow progression?
Is because for a lot of people, like decisions can happen in both of those ways. Right. It seemed like, Okay, for the most part. Switch from music to psychology was a little bit one of those like aha moments. Right. But your transition into data was a lot longer, like longer tail type of thing. And I think that there's power in both of those types of things.
Like we, sometimes we just know, it's like, wow, this happened, like I've gotten really into Brazilian jujitsu recently. Right. I went to the first class and I was like, this is something I really want to focus on. I'm having so much fun, whatever it is. Other things like, even data for me, it's like, Okay, I like some of these elements, how do I build a career that has more of them?
How do I sort of position myself slowly over time they get into a good role. And I think that there's fear in a lot of people that, Oh, I don't love this right away. This isn't this aha thing for me. And it's so cool that in your story, you have both of those types of. Of moments and you know, one of the things, what you're doing now, what are they full-time that you love?
And the data domain was the longer Margaret sort of tailed thing. The other one was something you did and you enjoyed, but you eventually transitioned away from, and so there isn't necessarily predictive power. And if you had this quick transition moment or you had this long tailed moment but they're both completely valid ways to, to make decisions and to feel them.
And, you know, I, one of the reasons I have this podcast is I think so many people are so scared about making decisions, right? They're so scared about, Oh, if I make the wrong decision here at all, pervade, whatever you know, you made a decision to study psychology, which, you know, might not have been optimal if you look at it from big picture, Oh, she wants to become a data scientist later, but you were able to transition out of it into the domain.
There were things you were able to take away there. There's very few things aside from. Probably like murdering someone and going to prison that can completely, you know, derail what you're trying to do. Like, so what you took a year to do something else. So what you did, whatever. I just think that's highlighted so well in your story is that like, you know, there are these other paths, but if you tell if you're a good storyteller, if you think about it the right way, they can all be constructive for your sort of broader, longer term career.
[00:40:58] Sadie: Yeah. And that's what makes us as individuals like so interesting. It's like the multi-dimensional experiences that we each have. And to me, like that is really why I love diversity and working with diverse teams is because you never know what I'd experienced. Someone else has it bring in that allows to look at a problem differently.
And I think we can even look at diversity in our own life of like, how am I breaking out of the traditional role to get new experiences, to take those risks so that I can think of new ways because inventions really happen when we're able to draw connections from existing things. And it's through those connections that we're able to innovate and create the new.
And I always tell people like we're on a cost benefit analysis, right? And I'm like, what if I make this decision? If I take this job. What's the worst that can happen. Like actually take some time and literally just write it out. And you realize like most of the time we're freaking out over something that has really low consequences and we can always change your mind.
And most of us are an at-will employees. Right. Just take the job you give me, don't like it, you can quit. Right. And you can go back and do something else. And so just take some time to actually like write out what your fears are and write out what you're losing by not emailing that person. You're in the same point right now, by not emailing them.
They're not saying yes to your non email, so they don't reply to you. Like you're not losing anything by it. I think there's a lot of opportunity for us to take greater risk and chances and put ourselves out there to have those new experiences.
[00:42:39] Ken: I think it's a really weird paradigm in our society. Where the people that are most popular, the people that are most interesting are the risk takers.
For the most part, they've tried things. They've lived an interesting life. They've gone down a lot of avenues and goalies and whatever it is. But even though those people are stand out the most and I would argue, have the most opportunities professionally, and a lot of other things, there's still a lack of appetite to take on risk in like a more traditional sense, like in the job market and in some of these things.
And I would argue that that carries over to the job market, right? If someone sees, like, for example, like on my resume that I tried to play professional golf, and then I did this and I did that. And I ended up here as long as those things sort of flow together. And it it's like, wow, this person, isn't just like, like a weird thrill seeker and data sciences, this thrill that he's trying to find and run away from.
That's like, you know, that is something that implants you in memory rather than. Just blending in with the crowd and, you know, at least for me, maybe this is how I'm wired. Maybe it's how we're both wired from like an entrepreneurial ... point. But like, I never want to just like, be part of the crowd.
That's not fun. That's not how I like actualize or how I see my life going or how I'd want my life to go. And yeah, I'm wondering why there is so much hesitance of risk-taking or like conservative risk-taking when those, the people that take the most risks to put themselves out there generally have the most success. We talk about them and we idolize them and worship them, whatever it might be. It's a very, very strange, very strange, you know, psychology.
[00:44:26] Sadie: And my theory is that it comes to our need for belonging and it comes to our roots of tribe mentality. Right. And to go out and to take a risk, sometimes we have to do leave the tribe. Right. And we build these identities of like, well, I'm a scientist or a neuroscientist and all my friends are that. And so like, if I am no longer that, and I joined this new tribe of the data science tribe, like, will they accept me? And will they like me? And will I have my sense of blogging?
So I think a lot of it stems from a fear of wanting to belong and the idea that there's like this consensus that we must be held to, but like true belonging only comes when we're truly ourselves. Right. And when people can be like, Okay, I accept you and appreciate you and love you just as you are. And that's really, I think the belonging we're all looking for. So that's my theory.
[00:45:34] Ken: I think that that really matches up with my experience. I was an only child. I was like always very like fiercely independent. And that's probably why I feel so strongly that like risk-taking is relevant is because, because of that, you know, you can go two ways. Like you're always seeking to be a part of other groups, or you're always trying to, to pick go a different direction.
And fortunately, or unfortunately I decided to go the Ken's own direction, a path a little bit. Are you okay on time? Do we have maybe 10 more minutes? Okay, perfect. Perfect. I just want to make sure I'll let me note that this is right around the hour for the ender, that part out. So there's two, two other things that I definitely want to touch on.
The first we can briefly touch on it, but you know, you did pursue a master's degree to sort of transition into the study data domain. I'd love to understand sort of your thought process around that. We talked a little bit about formal and informal education before, and you know, that is an advancement towards more formal education compared to going more of the self-taught route you know, with your background being homeschooled and being a very good self-learner, I'm interested in why you went down that road.
[00:46:55] Sadie: Yeah. So for me, it was a couple of reasons. One, it was in 2014 when I was looking to make this transition and there was not the community and data. There was not the resources available. I started with Coursera and the only data science classes on Coursera where the John Hopkins certification, it was like a four class.
It was, I still remember it. It was like data science toolkit are like, there were four classes. It was like, this is what you need for data science. And that was pretty much all that was out there. And then there was. Only four universities, four or five universities who even offered master's programs in this space.
So I preface it just to share like the different time and it, I don't feel like I'm that old, but now I'm like 2014 and this was how it was like, feel really old, like back in the day things move really quickly in this space. So for me there wasn't that level of resources and what I found taking the John Hopkins classes, like it allowed me to realize I really like it.
I have an aptitude to learn to code and all of those things, but I was missing the social learning aspect. And I mean, that was part of the reason I started women in data was I felt like there was the need for community in that if I was going to survive in this space, like I needed community. It was a little bit different time.
I wanted more of that community learning aspect which led me to want a master's. And at the same time, I had already taken all my GRE tests because I was planning to get a PhD in neuroscience prior to that. And I didn't want those expire and have to take them again and like four years or something.
And I was like, Hey, this is like, I know I'm eventually going to want a master's just like personally. So like now is just the time. So part is just like a personal decision of like, I still have good qualified test scores. I don't want to go through that again. So, and I know I'm eventually going to want a master's so like now's the time to be able to do it.
[00:49:08] Ken: Everyone should be thinking, taking these advanced degree considerations or whatever it is with a grain of salt. Right. We should be thinking about our unique situation very much like you've done in the past. And if it's relevant for you. So there's a couple of factors. You obviously, there weren't that many resources that could help you learn something that I loved about graduate education is that I had teachers as resources.
Like I absolutely wore out office hours. And, you know, like for me, I was paying for their time. I was like, if I'm going to be doing this program, I'm going to ask them every question I have, whether it's related to their class or not related to this class, because, you know, that's a benefit that a university provides, you know, unfortunately online education.
That's, there's some programs that do that. And I think it's awesome. But for the most part though, you know, one of the best ways to have access to individual professors or like people that are more at the top of the field is by doing that is by going to a more formal route and know. You had all these factors that made it relevant for you at the time?
I know I personally, I don't know if I would have gone and pursued my master's in computer science. If I had access to all the resources I do now you know, another thing is like, you know, I had a steady job. I could afford an advanced degree. That's something that a lot of people, if you're in a position where it's like, wow, I'm going to be taking out a hundred thousand dollars worth of debt to pursue this.
And they pick that up. I don't know if that's necessarily a good trade off. There's no guarantees of a job after any of these things. But that just goes to say that, Hey, this is a decision. This is there's no, yes or no answer. It depends very much on your individual situation and. It frustrates me so much because there are so many people that are like blanket.
Statementing like, should I do this? Or should I not do this? And I'm like, well, you're going to have to write me like a freaking dissertation about your life for me to tell you what I think. And it won't be a yes or no, it'll be like, Oh, like 60, 40, probably. You shouldn't do it versus you should do it based on these other factors. Right. And you know that to me, that quantitative process or that more systematic process is something everyone could benefit from in a decision making perspective. So you talked about the.
[00:51:28] Sadie: That was just saying, you're saying before, like a lot of the decisions are black and white. There's a lot of gray area and it really comes down to a lot of factors and a lot of personal factors. So I should mention also at the time I was working full time as a data analyst and the company. Would give me like a $5,000 reimbursement for education.
So like that was a big motivating factor. And then I ran a cost benefit analysis on it too, of like, Okay, what is, what am I going to have to pay for this degree? And what am I going to get from it? I think a lot of times we don't look at degrees as actual financial investment and a return on our investment.
There's a lot of people I know who didn't even graduate college and have more money than me. Why? Because they've made better investment. So I think we also have to start looking at degrees. It's like investments because it's our money that we're paying for it. And that should be a factor that comes into it.
So there's so many factors. It's a very personal decision and there's a lot of opportunities to run a cost benefit analysis on it. Not only from a financial standpoint, but like a personal, like, do you want a master's do you not want a masters? Does it work with your schedule? Like, do you have family and kids and what, how much time do you have and priorities and what are you looking to get from it? So definitely agree there's no black and white answer, and I'm not able to answer that question. When anyone asks me, I'm like create a model for me.
[00:53:05] Ken: That's how I do. I just like keep making videos on like, how you should think about it rather than what you should actually do. There's a really good book.
Just, just describing what you were just starting going there called thinking in bets by Annie duke. So she's a. I think former poker player, she might still be a poker player, but she talks about, Okay, like everything that we do is like an investment or a Tibet it's about ourselves. It's about, on something else and everything can, in some sense, be broken down to a little bit to expected value.
And a lot of the time we don't create expected value scenarios about the possible things that could happen. We just look at one side of the equation. If I do this, this happens, we don't look at, Hey, these are the five things that could happen if I don't do this. And these are like their expected tale returns.
Whatever's maybe like overly quantifiable for some people. But when you do that, you take so much fear out of these things, because like we were talking about before you limit the downside, you understand what that is. And I don't know, that's been really great for my mental health, honestly, because you know, you have concrete outcomes that are bad.
You have concrete outcomes that are good. The scary thing about all decisions or many decisions is the uncertainty about what's going to happen. And if you can at least have some semblance of what the uncertainty is going to be, or you can say, Hey, these are different scenarios that are likely to play out.
The uncertainty goes from like this big thing to like, Hey, there's this like 5% sliver of like, who knows what the hell is going to happen. And that's usually a lot more manageable than unlike that much. So the last thing I really want to touch on, you talked about community building. I really want to, to talk about your experience, being a founder, your experience, growing up an organization where you are building community and what that experience has been like.
I think that that is so incredible. I obviously have a little bit of an entrepreneurial niche as well, and it's always just fascinating talking to people and hearing about your journey into something that is also completely different from music psychology or, or data science in some sense,
[00:55:18] Sadie: Yes, it is completely different. But again, I just feel like this whole episode be called bridges because I just keep talking about him because, you know, I was working as an AI strategy consulted before I transitioned into leading women in data full-time. And my job is very different than it was before in the sense that, you know, I'm a CEO and I am responsible for all functions of the business as an AI strategy consultant.
I was responsible for one specific thing of a business AI, right? So my job has changed rapidly, but again, the, the bridge that connected me over was the organization is women in data, right? I am a woman and also I worked at data. So I know those things, but yet my day to day job has changed a lot. And I will say it is really.
Community that allows me to be able to learn, to be able to fail. We all need people who we don't make the right bed, right. Not the right decision, but the right bet. There's people who will pick us up and support us, but also people in our corner who are encouraging us to move forward and push on. Even when maybe it's not your best podcast to record it, or it's not the best product you released.
Right. We all need those people to be able to push us and support us. So I'm just so grateful for like the women and data community that allows me to lead the organization. Cause I feel like very honored. There are so many individuals in the organization who. Truly inspire me every day with their stories of, you know, overcoming struggles and their tenacity.
And it's that type of storytelling and knowledge sharing that really allows us all to get stronger as a collective. So every day, again, just using my methodology of learn, do share, and repeat and I'm excited for where the future goes from here.
[00:57:29] Ken: Awesome. Well, I'd love to hear about what type of projects as an organization you guys are working on now, how people can get involved. That's that, to me, something really, really exciting.
[00:57:39] Sadie: Yeah. So we just released these, a new program called learning pathways. So again, it ties in learn, do share, repeat, and essentially you have the ability to learn on your own, but then you're paired in a cohort with people. So you have that social learning aspect and that networking aspect.
And then when did you finish one of the pathways you go through a portfolio, project builder. So again, tying in that doing aspect, and then from there you go. Getting some lessons on how to share it in terms of write a blog, posts, attitude, get hub, make sure that you're able to communicate those results.
And then from there, you can go into a residency program, which allows you to be able to take everything you've learned and kind of tie it all up in a nice bow so that you get executive presence and can start to do data projects for a client and deliver it in a value that you can talk to executives.
You can talk to stakeholders you know how to not only do the project from beginning to end, but you can actually present and communicate those results, which is so important. So that's one of the new things that just launched this month. In addition, we have a career services program now that pairs our partner organizations and sponsors with our members for easier hiring access.
Next week at, or in the next couple of weeks, we have an executive data forum that's being launched next month. We're doing some work with NFTs and education on web three and the blockchain. And then we've got some stuff happening with allies and ally networks that are being created. So it is nonstop development and creativity and opportunities. I can keep going, but I feel like that's enough for people that you offered out.
[00:59:34] Ken: That is incredible. I'll obviously provide all the links in the description and in the, in the show notes of the podcast on YouTube and then on Spotify and all the other platforms something to end on, I think it, which is really cool.
And I'd love your insight on is, you know, sometimes people can feel intimidated to, to join or engage in a community. It sounds like you're doing some really cool things with cohorts and with like encourage participation in specific ways, but how can people. Join a community or feel engaged in a community and sort of get, get out of that sort of scared state of like, Oh, I'm just going to be a lurker.
And you know, it's totally fine. That's what the communities are for like people who just observe and, but, but how do they make that sort of step into from like observing to being a participant and an active participant and getting the most out of these committee?
[01:00:30] Sadie: Great question. So in women and in data, we have a motto like you get what you get, right. And I think so often we just forget that we can raise our hand and say like, Hey, I'm happy to help if you did that in any community, that you're a part of, like, you're going to meet people. You're going to find ways to connect. You may even not feel like there's something you can help with, but just the sheer ability to be like, Hey, this is who I'm in.
I'm here to help. Like everyone has something to offer and some knowledge to share and give. And I know that coming into a community, you can be like, well, I'm just getting started. And I want to just like, learn from other. Okay. But again, going back to the beginning of the conversation, you have some skill that you've developed.
And however, even if you're 18 years old, that's transferable that somebody can use it. So I think the first thing it's like when you join a community, think about like what you're going to give to that community. And whenever you give, it's going to come back to you, tenfold.
[01:01:32] Ken: I love that. I also, I think that perspective is also something people can give, which everyone has their own unique one. I mean, there can be someone completely new to data science that comments on my video, and they give me insight into how someone new to data science is thinking about this problem. Like, I, it's very difficult for me to go back, you know, six, seven years when I was first starting and put myself in those shoes and run by what I was feeling.
And those things that I was feeling might be very different than what something. Who is now approaching. That problem is feeling because just as we described, like the educational resources change, the number of jobs have changed the content and information around this field has changed. And I couldn't agree more.
I love that you're building such a powerful community purpose driven community, and I'm happy we could share about it on the podcast here.
[01:02:26] Sadie: Yeah, it's my pleasure.
[01:02:28] Ken: Awesome. Well, Sadie, thank you so much for coming on the show. I really enjoyed this conversation. I, your story is fascinating and I really think people are genuinely got quite a bit of a perspective and they might build some bridges because of it as well.
[01:02:45] Sadie: Oh, I love it. And thanks so much for having this podcast. I think it's fantastic and such a great resource for people to hear stories and learn from others. And I mean that you're building your own community here, so thank you.
[01:02:59] Ken: Incredible. Thank you so much again.