Sadie St Lawrence - Mixed 1.mp3

Sadie St Lawrence: [00:00:00] If you want to own your career, really what you need to do is look at and see what things can I control in my life, and when you start 100 percent focusing on those, the world around you is going to change.

Harpreet Sahota: [00:00:40] What's up, everybody? Welcome to the artists of Data Science podcast, the only self development podcast for Data scientists. You're going to learn from and be inspired by the people, ideas and conversations that'll encourage creativity and innovation in yourself so that you can do the same for others. I also host open office hours. You can register to attend by going to Bitly dot com forward, slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show and leave a five star review.

Harpreet Sahota: [00:01:33] Our guest today is an advocate for women in Data science and loves to share her thoughts on tech, personal development and lifestyle.

Harpreet Sahota: [00:01:41] She's earned a master's in analytics from Villanova University, a bachelor's in psychology from Sacramento State University, and is currently an AI strategy consultant for Accenture Applied Intelligence. You might recognize her as the instructor of School for Data Science with UC Davis and Coursera, where she's trained over seventy thousand people in Data science, including me. She's helping pave the way for women in Data science as being the first female instructor for Data science on the Coursera platform and as a founder and CEO of Women in Data Science, a nonprofit organization focused on increasing diversity in Data creators, she brings a unique combination of technical expertize, analytics management and an ability to lead organizational change through compassion and strategic problem-solving.

Harpreet Sahota: [00:02:37] So please help me in welcoming our guest today and advocate for democratizing artificial intelligence and helping people transition into the fourth industrial revolution. Sadie St. Lawrence Sadie, thank you so much for taking time out of your schedule to come on the show. I really appreciate you being here.

Sadie St Lawrence: [00:02:57] Thank you. It's really a pleasure to be here. And I am so happy to be talking with one of my students. One of the great things about teaching, Coursera, there is the reach, but then sometimes you miss that actual connection. So any time people are like, I'm in Sacramento or I get to connect with them, it always just brightens my day.

Harpreet Sahota: [00:03:19] Yeah, yeah. It was a couple of years ago and I was like trying to brush up  skills and whatnot. I come across a course on Coursera and it was really helpful to make the connections again and because I hadn't, use sequel, in some time. But it was such a good course. Thank you for creating that. And it's benefited a lot of our listeners here as well. But talk to us about how you first heard of Data science and what drew you to this field?

Sadie St Lawrence: [00:03:44] Yeah, so I can't really pinpoint the actual time. I think it was a Google search or maybe even a LinkedIn ad, but I do remember what led me to it. So at that time, I was working in a neuroscience lab studying emotional learning and memory, and I thought that I was going to go get my Ph.D. in neuroscience. I really just loved the scientific method. I, I really love like having a question and answering it.

Sadie St Lawrence: [00:04:17] But at that time I was a little depressed with working in a lab that was in a basement. And then I was working with rats and dissecting them. And I was just really frustrated with the process of collecting the data. I just wanted to like, skip through that process as fast as possible and get to the analysis and the findings of it.

Sadie St Lawrence: [00:04:41] And so a lot of times what I tell people is the best way to figure out what you want to do is find out what you don't want to do. And that's what I did. I looked at what I was doing at that time and I realized, like, I don't enjoy the data collection part. It doesn't move fast enough for me, but I enjoy the analysis part. So I went to my trusted advisor, Google start doing some searching of like, how do I keep this? But get rid of that. And that's when I landed on data science. And at this time it was two thousand and fourteen. It was a fairly new field. And what I heard from it was a lot of people were leaving academia to come into it. And it was a little bit about these rebels. And I've always been a little bit of a rebel myself, and I just really fell in love with what it had to offer, but really just the impact it can have. You know, when I was doing an experiment, not a lot of people are reading research papers on their time off, but everyone gets affected by the algorithms we interact with today. And to know that your work can have that effect really drew me to the field.

Harpreet Sahota: [00:05:52] When you talk about data collection in the laboratory with the mice, like, can you describe that for us?

Sadie St Lawrence: [00:05:58] Yeah. So prior to mice, I was working with fish and there was I remember a year where I was at school three hundred and sixty five days a year, because I think people forget that you have to keep them alive. Right. To do your rats were a little bit better because you could leave a food over a few days and they would be OK. But yeah, a lot of people actually develop allergies with working with rodents.

Sadie St Lawrence: [00:06:24] So there's that problem. The other issue I had was, you know, I would take care of these animals and then course. I would have to euthanize them to take out their brain, and that was actually a really hard part for me because I kind of get a relationship with them. Right. And then in a way, I just felt like it was very wasteful. And so, like, part of my working with them was like, how can we do science in a more ethical way? And so, like, that really also led me in the direction I wanted to go, because it's like this Data already exist. It's kind of just laying out there waiting for people like what can we do to utilize that more versus causing harm.

Harpreet Sahota: [00:07:09] That sounds a lot messier than pulling Data from an API.

Sadie St Lawrence: [00:07:14] Yeah (laughs)

Sadie St Lawrence: [00:07:16] You know, it's funny you say Messy because we talk about cleaning Data and everything. Yeah. If you want to talk about Messy, talk about trying to get your rat to run around in a cage and monitor their behavior like there is, it can always get way messier, that's all I will say.

Harpreet Sahota: [00:07:32] So how much more hype has Data science become since you made your transition?

Sadie St Lawrence: [00:07:37] I have been kind of surprised by and I will say twenty fourteen is now, what, six years ago. Now, that's crazy to even think about. I knew that it was going to have a big impact. Part of the reason why I started women in Data because they started it in twenty fifteen. So I knew that it was going to have an impact, but I really didn't think it would be this long standing. I think most people are familiar with the Gartner curve where it goes through the hype cycle. But, you know, with other technologies such as block change, I feel like they really follow the curve and kind of died.

Sadie St Lawrence: [00:08:12] But A.I. and Data science has really just continue to pick up excitement and applicability really even further than I imagine. So it's been a wild ride.

Harpreet Sahota: [00:08:24] Yeah, it's interesting because it's really the combination of two old ideas being put together in a new way. And with the new innovations in technology like the compute power is just being able to facilitate that growth. I don't ever see it slowing down because if you look at our world connected devices, like we got a ring that collects data watch, that collects the data is constantly being collected. It's never going to stop being collected. And we need professionals like us to to wade through it, make sense of it right.

Sadie St Lawrence: [00:08:55] And I always have to really give the gamers a lot of credit, actually, because, as you said, it's the old and the new coming together. These algorithms have been around for a long time, but we haven't been able to take advantage of them. And so to me, just the brilliance of the people who created these algorithms like you created something that wasn't even fully realized yet for another 20, 40 years. But really, it comes from the gaming community developing graphics, computers and pushing the boundaries on that then allowed us to start to use these algorithms because of the computing power. So I think it's really cool when you understand, like how many industries in a way have come together to make this possible.

Harpreet Sahota: [00:09:39] What do you think is going to be the biggest positive impact that Data science is going to have?

Sadie St Lawrence: [00:09:44] It's kind of a flip side. It could be the most positive impact and it could be also the most negative impact. So it really comes down to the automation of things. Right, to take essentially. I really like the way Andrew Ing describes it, where we're not automating huge task. We're automating things that take us one second to do right. And so in the interim, it seems like a small thing, maybe prioritizing your inbox sooner or providing recommendations to you so you don't have to search for things as far right. It doesn't seem like a big thing that we're accomplishing yet. But have you essentially take all those one second tasks, the things we're doing, and add them up? And as the industry progresses, we're going to be automating a big portion of our life. And so from that standpoint, the positive side of that is that can allow us to free our time in a way where we are now having more time to develop deeper connections with people, care about bigger social economic issues. We can also start to have more maybe creative time. Right. It allows us to free ourselves from some of those menial tasks that we have to do that Boggess down. But then, as I mentioned, there's two sides to this coin, which is the negative impact of it. And the negative impact, I think, is where people's job gets replaced. And we don't transition those people into new careers and it creates more economic divide within our society. So I think we really have to be conscientious at this time and how we're progressing and the next five. Years are going to make a really big difference.

Harpreet Sahota: [00:11:34] So in the next five years, what do you think is going to separate the great Data scientists from the just good data scientists?

Sadie St Lawrence: [00:11:44] I have seen so much automation in our own industry. I think it's kind of funny because in a way, we're automating ourselves, right? When I first started, it was most people used R and slowly switched over to Python a little bit more. But even within there, it was thought you would like create these algorithms. Now so much is out of the box.

Sadie St Lawrence: [00:12:07] I just recently used Salesforce Einstein platform and their discovery and just, you know, the amount of what is able to be done just in a gooey tool, right. Where you can put this at the hands of marketers or sales people. And they're actually like running algorithms now, which is just fantastic, I think. But it may scare some data scientist and like, well, what is really my job? Right. So I think what's going to really separate out the good from the great is those people who will be able to clearly identify the business problem that they're trying to solve and then understand how to scale that up in a business that will transform the business model so. Well, I think we'll have I kind of look at it as like little fireworks going off of everybody can run their own algorithms. But how do you integrate that algorithm into the whole business model that actually changes the way your business operates? And I think when we think a little bit larger. Right. So not just thinking of like, oh, I made this model, but how is it going to be used and how is it going to ether transform the business or start to, you know, in a way create revenue for the business? I'll give you an example. The tick tock, if you're been watching, is trying to go through an acquisition. And they said, yeah, you can get us, but you don't get our algorithm. And people are like, OK, we don't want to buy. That's value you created with the algorithm. Right. The fact that, like, you're a great data scientist, if you created something so much where people won't buy, you don't have your algorithm. And I think that's where the great data scientist need to be thinking is just a little bit higher level and bigger for business thinking.

Harpreet Sahota: [00:14:02] I absolutely agree with that 100 percent. Thank you very beautifully put. So question, I want to ask you to some of your SQL expertize here. I think this is something that the audience is dying to know when it comes to SQL. What do you think are the essential must knows for people who are breaking into the field?

Sadie St Lawrence: [00:14:22] I just start with, like, why I love it so much. SQL is like your grandmother's cooking. I feel like it's so tried and true, like there's been so much new technology and new languages coming in, but like we haven't gotten rid of SQL for like a year. It is not going away any time soon. So, like, I think one of the great things is like the more you invest in it, just like it's never going to fail, you know, it's kind of just going to be like that home cooked meal and just always, like hits the spot. And so where you think people can really invest is reading to me. It's like reading other people's code. Like I want to see how SQL is such a simple language.

Sadie St Lawrence: [00:15:03] Right. You have your select, you have where you're getting your from statement and then you have subclauses. Right.

Sadie St Lawrence: [00:15:10] And it's very simple structure, but there's so much creativity that can come around it and so much that can be done to me for what I like to do is just reading other people's code because it inspires me of like, oh, I would never thought to solve the problem that way. And I think that's the best thing you can do to continually just invest in your skills is by getting inspired and coming up with new creative solutions.

Harpreet Sahota: [00:15:41] What's up, artists? I would love to hear from you. Feel free to send me an email to the artists of Data Science @ Gmail dot com. Let me know what you love about the show. Let me know what you don't love about the show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register like going to Bitly dot com forward, slash a d s o h. I look forward to hearing from you all and look forward to seeing you in the office hours. Let's get back to the episode.

Harpreet Sahota: [00:16:28] What do you think is the most commonly Misunderstood concept and SQL or something that really starts to trip people up during the interview process.

Sadie St Lawrence: [00:16:38] I think a lot of it is getting in our own head and making it more complex than it needs to be. I failed SQL interviews before I started teaching where? Yeah, I really just got in my own head and tried to because I thought it was an interview. I thought it was supposed to be hard and I was making it harder than it needed to be. Like I said, SQL is a very simple language. And I think the more we can boil it down to the simplicity of how we write a statement, the better. You'll be right if you know you have two options in an interview and one is really complex that you did in there. Like, I think this other one may work. It's a little bit more simple and go to a simple one.

Harpreet Sahota: [00:17:24]  yeah, it's just a very simple, elegant language and very readable. So you could tell exactly what it's doing just by reading it, which is also the power of it. Right, to be able to communicate clearly across people using it. Thank you for that. So enough with the Data stuff let's get into some interesting things. So you've got this really excellent blog about the action plan for getting into Data site. So I guess we are gonna stick with the Data Science topic here. But I think it's a great action plan for getting anything that you want in life. Would you mind walking us through this action plan that you've got?

Sadie St Lawrence: [00:18:00] Yeah. So I love that you say it's a great way to get anything you want in life, because one of the things I think life is talking about, see, I think life is a lot more simple than we make it out to be. Right.

Sadie St Lawrence: [00:18:19] I think that what I've found is I've switched my career multiple times. I was a piano performance major. I was in psychology and neuroscience and then went to Data science. Right. But what I found is like the same principles that made me good in one of those careers, also made me good in Data science as well.

Sadie St Lawrence: [00:18:44] And so part of this action plan really came from like, how do I take the principles that I've learned in things I've found success in and apply it to this new industry as well?

Sadie St Lawrence: [00:18:59] And that's one of the first things I just encourage anyone to do, is really if you're looking to get into Data science and transitioning from something else, what made you successful in that area and what principles did you apply?

Sadie St Lawrence: [00:19:11] Because those same principles will probably apply here.

Sadie St Lawrence: [00:19:17] And so the action plan is really just something I use for my life, which is like first taking quiet time to write down my goals and getting myself organized. I think our world is very quick now with how we communicate with each other, and it's easy to always be on screen. I like to just like sit down and like actually get a real piece of paper and like write down like, what is it that I really want. So if you're thinking of using this for Data science, what I would say is like, what's your motivation for getting into Data science? Right. Like, how much do you want to get paid? Right. Like what does a day in your mind look like as a data scientist? Look and write down those goals and then the next thing to do is really prioritize. So once you've written down like what your goal is and kind of broken out what you need to get there, you need to prioritize what you're focused on to do that. So a lot of people in Data science for sure, overwhelmed by how much you need to know, you don't need to know at all at first and you won't ever know at all. Like, that's the beauty of it. If you think about it at the core, you need four areas. And I think I talk about this, too. And one of my posts, which is you need like a data visualization skill, you need data base skills, you need algorithms, and then you need communication.

Sadie St Lawrence: [00:20:41] Right. So maybe those are your four starting points and then you just prioritize one thing you're going to do to learn in each of those areas. And once you prioritize it, this is really important because if you try and do everything, you won't see progress. When you have four things you're focused on, you can easily see progress. And the next part is to stay the course. So this is where the grit comes in, the hard work, right. You got to put some work in, even get some input out to see is this the right thing for me? So, like, don't give up before you're three feet away from your prize. And then it's really just about the second step, which is reflecting off and like what's working, what isn't working. I'm a huge fan of like I keep very detailed track of my time and keep very detailed track of. What my results were, I use it as a way to steady myself, so, like, do you not like learning on an online format? Do you need to find a community to learn with? Right. Like analyze yourself in this process so that you can optimize yourself. And then finally, the last portion is to get support. And this is something that could really go all around it. And part of the main reasons I started with LinkedIn Data because I knew that if I was going to do Data science, I needed a community and a tribe of people to be a part of.

Harpreet Sahota: [00:22:02] Talking about communication, what do you think are some of the soft skills that Data science need to really employ to elevate their careers?

Sadie St Lawrence: [00:22:12] There's so many soft skills are, I think, so often overlooked. One of the first ones I would say is communicating your own results. Right, because a lot of times I have had different models or things that I've created, but because I didn't know how to properly articulate and communicate and they never got used. And it's really a shame because it was a great model or it was a great analysis, but it didn't get used because I missed that last part of communicating it. So one like knowing how to communicate it to different people, usually business people, is really important in that same regard of communicating with people outside your industry.

Sadie St Lawrence: [00:22:54] Data science isn't just about building models and doing that type of work like you're working usually for a business. Right. So how do you communicate with other team members? How do you communicate with your boss? Right. Like having those emotional skills to know and understand team dynamics will really help and how you progress in your career so that you can continue to be promoted throughout your journey.

Harpreet Sahota: [00:23:19] Speaking about team dynamics and communication. Talk to us about how to use verbal judo so that we can be a bit more persuasive.

Sadie St Lawrence: [00:23:28] Yes, I think there is a book called Verbal Judo. I think I've actually read it. So I will paraphrase what they say. I'll just see how I use it. It's testing a learning process right back to, like, keeping kind of Data on yourself, keeping Data on what worked and what didn't. And as you grow, I learned is there's different styles of people.

Sadie St Lawrence: [00:23:53] You know, there are I would say one of the things that helped me the most is first listening to help others talk. You know, are they a quick kind of scattered pace? Right. You need to move fast when you talk to them. Are they a more slow, relaxed, methodical need to get into the details?

Sadie St Lawrence: [00:24:13] And so what technique I would use a lot is what's called mirroring. So before I actually verbal to tell them I want to listen to them to understand, OK, how do they speak? Because that's going to tell me a lot and how they're going to receive what I'm saying. And once you have listened to them, then mirror that back right. With executives, you'll notice they have a million things on their mind. They like things usually at a really high level and quick pace. So mirror that back with them when you're communicating.

Harpreet Sahota: [00:24:46] So speaking more about executive. So Data scientist. You know, obviously we have a bunch of technical knowledge and we always like to showcase that. And we assume that other people will share our viewpoint and appreciation for all this technical stuff. But like you mentioned, executives just want to get to the point, right? So talk to us about what we could do if we find ourselves in a room full of executives and we have to communicate our ideas.

Sadie St Lawrence: [00:25:13] Yeah. So there's a really good YouTube video. It's called I think it's called "How to Speak like a consultant from an ex McKinsey consultant". And I really like it because it talks about taking a top bottom approach, which is like bring it down to a sentence. What's the point you're trying to get across then maybe like a subtext and then maybe have some additional bullets to support what you're saying down below. So what I would say is summarize it like it should, but you're trying to communicate should be boiled down to a sentence. And if you can get so clearly to that point, that will help a ton in how your message is perceived.

Harpreet Sahota: [00:25:55] So you have this awesome blog about the Data science mindset. Can you describe that for us?

Sadie St Lawrence: [00:26:02] Yes, this is really where I think everything starts. I'm so such a fan of having diverse people come and Data science. I often talk about how the skills are. You can learn the skills, but like having the mindset is really the foundation. And so like the mindset, it really just starts with like curiosity of, you know. Like wanting to dove into the details to solve a problem.

Sadie St Lawrence: [00:26:35] Curiosity is something I feel like I teach SQL, but I can't teach people to be curious. It's either you have it or you don't. So I think that's really important. The second portion, I think is really important is just like your tenacity, right? Like there's so much new stuff to learn. We've talked a little bit about Messe Data and the problems that you're that are going to come at you like you need to have a lot of tenacity and to be able to get through the problem that you're trying to solve.

Sadie St Lawrence: [00:27:10] So when you think about the mindset of it all, like for me, it's really like, you know, the last thing is then having that growth mindset.

Sadie St Lawrence: [00:27:22] So am I always looking at learning new things? Because in this industry, like, you're never going to know it all, so you should just accept it and be OK with the fact that you'll be continually learning.

Harpreet Sahota: [00:27:35] I think once you're faced with just the vast amount of stuff that you don't know, you start to feel overwhelmed by all the stuff you don't know. That's kind of what kills the curiosity. And just like, oh, fuck it, I'm just not going to learn. It's not going to be curious. But by not being afraid of this or standing into action while you don't know, you kind of maintain that curiosity. Do you have any tips for Data scientists who are maybe at a networking event? What would be the proper etiquette to network with the person online or in person?

Sadie St Lawrence: [00:28:07] Yeah, so I've never liked networking. I'll just be honest. I have actually changed my mind, set about it a little bit and what I have done, I have been really helpful and I've gotten better results. And so what I change it to is thinking in a way of not networking but relationship building. And I think everybody feels a little bit slimy when they think about networking. We all know it's beneficial. We should do it blah, blah, blah.

Sadie St Lawrence: [00:28:34] Right. But we all feel a little slimy. So what I would say is think of it like relationship building and like, how do you like people to build relationships with you? Right. When you normally find common interests to there's there's usually a value exchange. Right. And three, like you want to be around, people make you feel good.

Sadie St Lawrence: [00:28:56] I see a lot of younger people try and network with somebody because of their position or power, and they often forget themselves about what does it being around this person even make me feel good. Right. So I think if you change your focus to, hey, I'm just trying to build relationships with people, kind of go back to kindergarten, I want to make new friends and maybe something great will come out of it. I think you'll be really successful.

Harpreet Sahota: [00:29:24] And when it comes to working on a team environment, what should we do if we're in a situation where we don't know the answer to a solution or don't know the answer to a problem, rather, but we don't openly communicate that with our teammates?

Sadie St Lawrence: [00:29:36] Yeah, that's a good question. I would have to see what the culture of the team is. Right. If it's not a open environment where people feel like they can be vulnerable and be like, I don't know what like, can somebody help me, then what I would say is find somebody that you can trust and ask and do it on a one off kind of basis. And hopefully there is one person on your team where they you can they may not know the answer, but they can maybe point you in the right direction. I would say what's a better thing is to start to build a culture where people can be vulnerable and say, I don't know, I need help, people figure it out. But that's sometimes a little bit longer journey. But I would say definitely talk to one person who may have the answer, even if they don't have the answer. Sometimes for me, just explaining what my problem is.

Sadie St Lawrence: [00:30:29] And as I talk it through to someone, I'm like, oh, I figured it out.

Sadie St Lawrence: [00:30:33] I thanks for listening. So you can ask maybe even just your partner or your mom and you and those people help sometimes, too.

Harpreet Sahota: [00:30:43] Yes. Like the rubber ducky effect, I guess.

Sadie St Lawrence: [00:30:48] Mhm Exactly.

Sadie St Lawrence: [00:30:49] So I'm curious, how do you view data science? Do you think it's a or do you think it's just purely a hard science? What's your take on that?

Sadie St Lawrence: [00:30:56] I think it's 50/50.

Sadie St Lawrence: [00:30:58] I think if you don't have the art side of it, what you will end up with is something very technical but never useful to the business. And so if you're feeling frustrated from that of like, I just feel like I kind of missed the mark. Like, I know it's a good model or I know is a good analysis, then I'd encourage you to, like, explore some arts more in your hobby and see how you can implement that if you're too much on the art side of things where you may end up is. Everybody being like, oh, that's cool, and then not going anywhere from there, too, if that's the case, then whatever would say is, you know, take some technical classes. And I always try and just switch my learnings from technical to a different our hobby. I've just recently gotten into painting and still doing things, I think outside of your role and your job is so undervalued and raided because that's where I get my inspiration from. And so I think sometimes even thinking about 18 year lens will help you in Data science as well.

Harpreet Sahota: [00:32:09] Thank you very much. I agree with you on that. Just do something outside your own field. Let your mind wander like you probably have those called transient hypofrontality. When you're doing a task that's not associated with the task you're doing, just kind of like free form doing it and you're actually working on problems in the back of your mind. And you might have that Eureka moment while you're painting or going on a walk or playing guitar. I'm not sure if you've heard of that phenomenon.

Sadie St Lawrence: [00:32:32] Yeah, no, I haven't heard of it as that term, but I've definitely experienced that like a lot of shower moments where you're, like, totally inspired.

Sadie St Lawrence: [00:32:43] Yeah, it's just an effort where your conscious mind is busy. So it gets it out of the way. Right. But your subconscious mind can really do its work and then surface to make the magic.

Harpreet Sahota: [00:32:55] Yeah. So this being a self development podcast for Data scientists and you being a self development blogger, we would have to we'd have to discuss some of this stuff. So talk to us about how we can own our career.

Sadie St Lawrence: [00:33:11] This one's hard. It's it's hard to lose that victim mentality.

Sadie St Lawrence: [00:33:16] But I, I get to see a lot of people with in women and Data who want to get a promotion or want to change their role to a different thing. And then there's all these excuses. Right. Well, you know, it's coalbed and the economy's bad. That's the main one right now. You know, my company doesn't allow for people of this age to be promoted off or whatnot. And a lot of where that comes from, it's like a limiting mindset and belief. And I think first, recognizing that it's a first a mindset issue. Right. And that you're going to have to change your thoughts into overcoming that. And so when you're only in your career, you're taking responsibility for where you're at today and not being a victim of, oh, well, my company doesn't believe in this or won't allow this Friday cycle switch companies.

Sadie St Lawrence: [00:34:12] It's a free trade world, right? I think so often people forget that they're not will employee.

Sadie St Lawrence: [00:34:18] Right. Or if you're or maybe it's not even switching, maybe it's move to a new team. Right. But at the end of the day, like it's your choice, it's your mindset. And so when you if you want to own your career, really what you need to do is look at and see what things can I control in my life. And when you start hundred percent focusing on those the world around you is going to change.

Harpreet Sahota: [00:34:46] Once I stopped being the victim of my own life, like things just got so much better for the first part of my career, I kind of felt like I was done with my own life. But then I just chose to adopt a new belief system about things. I think that's one thing that people don't really appreciate is that you can choose to not believe your thoughts. Like just because you have them doesn't make them real. And you can choose to pick which ones you want to focus on and you can choose to update your own belief system like possible. You can do it now.

Sadie St Lawrence: [00:35:16] You can get new software and install. Right.

Sadie St Lawrence: [00:35:18] And I think that's like the most exciting thing. I mean, sometimes I get a little bug in my brain when I'm trying to sell some software, a belief system, but eventually will pick it up. Right. And I think you make such a great point of realizing that you are not your thoughts. Right. I just recently did a little video on my Instagram about imposter syndrome. That's a question I get a lot. And one of my tips for overcoming it is realizing you're not in your thoughts, even though that thought may be coming in and saying, like, you're afraid. You're like, no, that's a bad software thing. You need to.

Harpreet Sahota: [00:35:53] You talked about how to focus on things that you just are in control of. And speaking of software systems like that, making this analogy like I updated my software system and like my philosophy of life is now like stoicism, capital, S, Stoicism. And one of the main themes of that is just focusing primarily on the things that are within your own control, within your power. And that is almost entirely the things that go on in your head.

Sadie St Lawrence: [00:36:20] And when you realize that's about all you can control, I realize how much work I have cut out for me, right? At first I felt like it was limiting. Like all I get to control is my thoughts. And then I started getting in there and I was like, oh, there's a lot I can do and that changes a lot when I start focusing on it.

Harpreet Sahota: [00:36:38] What are your steps for combating imposter syndrome?

Sadie St Lawrence: [00:36:43] I think the first one I don't remember the order. My video, the first one was realizing you are not your thoughts. Right?

Sadie St Lawrence: [00:36:49] So just stop being that dead in its tracks. The second thing, though, is I've also moved up in my career. I had this idea of like people in executive positions and whatnot, and then you interact with them and you realize, oh, they're just humans. Like, it's kind of like the curtain gets revealed, right? It's like, oh, and one of the things the quote I love is that no one is less than me and no one is more than me. And when you realize that, like, wow, this is just a human being with thoughts and feelings, just like I am right. It brings us down to the same level. And when we're all at the same level, there is no imposter, right? I am human. You are human. Right. We're at that. And that's really comforts me a lot when I go into rooms a lot where I'm like the only woman and I'm a lot younger than people. And I'm like, no, but then they still a human being who wants to be loved just like anybody else. And so that has helped me. And then the last thing is really that action cures fear. I think too many times we use imposter syndrome as an excuse to not of why we're not taking action to like, you know, sit on that board seat or raise your hand that you want to do that project. And the only way to overcome that is this starting to take action against it.

Harpreet Sahota: [00:38:17] So you touch on a little bit as well. If we can dig deeper into it. If you could speak to your experience being a woman in Data and if you have any words of encouragement for the women in our audience who are breaking into the field or are currently in the field.

Sadie St Lawrence: [00:38:33] Yeah. So I think one of the things is this message is really for anybody who feels different. Right. I focus on women just because we know there is a disproportionate amount of women in this industry. And I feel passionate about how relevant it's going to be in the future.

Sadie St Lawrence: [00:38:52] But there was someone I was talking with the other day. I think we need to broaden our perspective diversity a little bit because sometimes diversity just is mind stepped right of like maybe you feel like you have different values than a lot of the people you work with.

Sadie St Lawrence: [00:39:05] Right. And you're afraid to voice those concerns. And, you know, when I think about, like diversity and inclusion, it's like really all of us, no matter how we look, we can show up as our full self and not feel like we need to hide anything.

Sadie St Lawrence: [00:39:23] And if I give anybody kind of like what my experience has been like a lot of times, I would say I've tried to hide parts of myself straight to fit in. And you know what I can say to Data scientists or anybody being in tech, that's the worst thing you can do, because those individual traits about what make you unique is going to get you that job.

Sadie St Lawrence: [00:39:48] That's a great fit for you. It's going to make you stand out against the crowd. I mean, there's so many people getting masters in Data science. And A.I, you know, to really stand out of a crowd, you know, share that you love collecting baseball cards or whatever it is, or share your side hobby, because that's going to help you find your tribe and your game and that's going to make sure you find a place where you fit in.

Sadie St Lawrence: [00:40:20] So, you know, really, my work with women and Data is just making a helping to create an environment, a future where everybody feels like they can fully express and be themselves and not feel backlash from it.

Harpreet Sahota: [00:40:36] So that's a little bit more about your involvement with women and Data like what was the moment that caused you to start this organization or some of the hurdles you had to jump over to start the organization? Can you kind of talk to us about the genesis of that?

Sadie St Lawrence: [00:40:49] Yeah, so this was in twenty fifteen. I was one year into my master's and there was only one other person, other female with me and my master's in the whole program. I only had one female teacher and I was just like feeling like, is this really my tribe? And like where are all the women? Because I think that where we're moving is not that technology is its own sector segment. Technology is woven into all of business and especially like Data. And so I really didn't know what I was doing. And I started a Meetup group, the first event. I literally just said, hey, let's meet at a restaurant, let's network. What a. And I sat there by myself. I was so nervous because I was just like, this is a dumb idea and, you know, it was about a couple of minutes past the hour. And I'm like, nobody's coming. Like, nobody's going to like nobody else is going to show up and like this great fear, like I'm going to be alone. And so I called my boyfriend and he was like, just stay 15 minutes. And I was like, OK, I can stay 15 minutes. Like, that seems like an achievable goal. And within those 15 minutes, three women showed up. And I always think back to this moment because I think, like, how close are we are to doing something great, but we may leave before the 15 minutes. Right. And I just think if I had an estate that 15 minutes like women and Data wouldn't have been born. And so from there, you know, a lot of it came from just listening to our community. What do they need knowing what I needed?

Sadie St Lawrence: [00:42:31] And we started to create really fantastic events, create really fantastic communities. And from there, like women in Data, has just spread. So today we are in thirty two cities. We're in 11 countries. We have really grown now beyond just having events, but we have memberships where we provide career coaching. We have residency programs for people to do internships. We have job boards and really just at the heart of it, a really fantastic community. So it's just been so exciting to see it flourish and to see just the impact that we've been able to make by staying 15 more minutes.

Harpreet Sahota: [00:43:14] I absolutely love that. How can people join the organization? Where can we find out more about it?

Sadie St Lawrence: [00:43:18] Yeah, so it's women and Data dot org and all the information to join is there. You can join as a member on the website. We also have our local chapters, which you can find on our community page, and you can join a community group. And then all of our social handles are women and Data or G as well. So you can connect with us on Social too.

Harpreet Sahota: [00:43:43]  Awesome I will include links for that.

Harpreet Sahota: [00:43:45] What can the Data community do to help foster the inclusion of women in the field?

Sadie St Lawrence: [00:43:52] Yeah, so one of the a great quote I heard the other day was until women's trades are respected as much as military, we will get to equality.

Sadie St Lawrence: [00:44:03] And I think that really resonated with me because what I think we're missing with women being in the field will really benefit both genders. And why I say that is things that we think are female traits. And I use air quotes right now are things like compassion, right? Are things like collaboration. You know, there are things like community. And to me, like, I don't know any guy who's like no other parallel worlds. I don't want that right. I don't want community. I don't want collaboration, like most teams want that.

Sadie St Lawrence: [00:44:43] But I feel like often that doesn't get respected and the value that it has. And so one of the things I would say is if you're a guy, value that trait like value somebody who's creating collaboration on your team, value somebody who's, you know, is really compassionate and thoughtful in how they operate versus what we see a lot today in our world is promoting somebody who's aggressive and trying to get more than the other person and trying to self promote. Right. That gets promoted, unfortunately, a lot in our businesses today.

Sadie St Lawrence: [00:45:19] So how do we more promote collaboration, connection, community? And I think when we promote those things, women will want to be a part of the field in the industry.

Harpreet Sahota: [00:45:31] Thank you for sharing that. Appreciate it. Last formal question before I jump to a quick lightning round, and that is what's the one thing you want people to learn from your story?

Sadie St Lawrence: [00:45:40] Yeah, I would say that if you can dream it, you can do it. If that thought came into your mind like you've already been blessed, the hardest part is to get the idea, to get the inspiration.

Sadie St Lawrence: [00:45:55] Once you get that, not go write it down right away and put that action plan into place. But yeah, I mean, I, I come from a background where it wasn't technical. I learned the technical skills. It wasn't one where I had a lot of influences, you know, in this area.

Sadie St Lawrence: [00:46:11] I am the first woman in my family to get her master's degree to work in tech. And so I think what it is, it's really just comes down to what we've talked about and being your mindset around it and know that if you thought it and you dreamed it the hardest. It's done, you're already halfway there now, it's just putting the action plan into place to do it.

Harpreet Sahota: [00:46:36] I love it. See it, believe it. We should achieve it.

Sadie St Lawrence: [00:46:40] Yes, you've got it.

Harpreet Sahota: [00:46:43] Jumping into a quick lightning round here, if you could meet any historical figure, who would it be and what would you ask them? 

Sadie St Lawrence: [00:46:50] Right now, Ada Lovelace. She, to me, embodies art or science. She talks about like mathematical poetry, which to me is just like beautiful. She created one of the first computer programs. Yeah.

Sadie St Lawrence: [00:47:05] So what I want to ask her is I would probably ask her, oh, there's so many questions I would have thought I would want to know, like where she gets her inspiration from, you know, like what inspired her.

Harpreet Sahota: [00:47:21] What do you believe that other people think is crazy?

Harpreet Sahota: [00:47:24] It's gaining more popularity, but probably just like the power manifestation. Right. I really believe in that. You know, the energy you put out in the world comes back to you and it's physics, you know. And so by having this little brain mechanism that puts out energy and my thoughts, they really do take form and manifest.

Harpreet Sahota: [00:47:47] If you can have a billboard placed anywhere, what would you put on it and why?

Sadie St Lawrence: [00:47:52] I think what I would put on it would just right now, I'm doing a lot of work in terms of caring for my inner child and self. And I think a lot of times we lose ourselves. Right. And so one of the thing I would put on it is like do one thing that brings you joy today. I think that we need more joy in our world. And I think a lot of times we all abandon ourselves for what brings us joy. And yeah, I'd just like to remind people like you can choose joy. And like if everybody did one thing today that brought them joy, like think we'd start to see a little bit different world.

Harpreet Sahota: [00:48:33] Speaking of the inner child, if we can somehow get a magical telephone that allowed you to contact 18 year old Sadie, what would you tell her?

Sadie St Lawrence: [00:48:41] I would tell her relax, like it's all going to be OK and never stop dreaming because, like, you don't even know the possibilities of what could happen. You know, it's it's going to be quite magical. So just enjoy the journey.

Harpreet Sahota: [00:49:02] What are you most curious about right now?

Sadie St Lawrence: [00:49:04] Curious. I'm I've been really into finding a new algorithm of the week or like a mathematical algorithm, a new equation and how the world operates on that equation.

Sadie St Lawrence: [00:49:19] And so that's kind of been my latest curiosity. It's like once a week I challenge myself to find a new equation and to find how the universe follows. This equation gets me really inspired because it makes me realize how connected everything is.

Harpreet Sahota: [00:49:34] I think you'd really enjoy the book by Scott E Page called "The Model Thinker", and it's a big book of mental models, of models and formulas. It's really good. And if you're interested, you can listen to the interview I did with him on Artists Of Data Science.

Sadie St Lawrence: [00:49:50] Yes,I would love that

Harpreet Sahota: [00:49:52] Yeah It's some really good book. It sounds like he'll be right up your alley.

Harpreet Sahota: [00:49:55] What is an academic topic outside of Data science that you think every Data scientist should spend some time studying about?

Sadie St Lawrence: [00:50:01] I would say physics.

Sadie St Lawrence: [00:50:03] Yeah, I secretly want to be a physicist, so I may be a little biased in that. But the core elements of how the world works, I think I just so I am reading this book right now.

Harpreet Sahota: [00:50:17] You might enjoy it as well. "Loonshots". It's written by physicist  Sophie Mookal. 

Sadie St Lawrence: [00:50:21] On nice.

Harpreet Sahota: [00:50:22] It's a mix of physics, history and business all in one. It's a really interesting book. Definitely. Check that out. See if you get a chance. Speaking of book recommendations, what would you recommend? Our audience read fiction, nonfiction, whatever book from either one of those topics or both. And what was your most impactful takeaway from it?

Sadie St Lawrence: [00:50:42] I wouldn't recommend polls, I think. Follow your own curiosity. I have a little bit more trouble with nonfiction, but yes, I can't speak much on that.

Harpreet Sahota: [00:50:57] Do you have a do you have a fiction book that you recommend everyone read?

Sadie St Lawrence: [00:51:00] I really love the book. "Think and Grow Rich". It was just like so foundational to me. I think in terms of like self-help, it's one I like the first, like really just great foundational ones.

Harpreet Sahota: [00:51:10] So life hack for everybody. It is available for free on Spotify. You can listen to it in its entirety.

Sadie St Lawrence: [00:51:16] Nice.

Harpreet Sahota: [00:51:16] What song do you currently have on repeat?

Sadie St Lawrence: [00:51:18] You know, I've been really into Mariah Carey lately, so just like anything by Mariah Carey.

Harpreet Sahota: [00:51:25] Which decade?

Sadie St Lawrence: [00:51:27] 90 is for sure.

Harpreet Sahota: [00:51:28] Alright so, so which track from the 90s? 

Harpreet Sahota: [00:51:30] Probably right now." Always be my baby",

Harpreet Sahota: [00:51:34] Ah that's a good one yeah.  That immediately comes into my head every time I hear the name Mariah Carey.

Sadie St Lawrence: [00:51:39] Right

Harpreet Sahota: [00:51:39] So how can people connect with you, where can they find you online? 

Sadie St Lawrence: [00:51:41] so pretty easy.

Sadie St Lawrence: [00:51:44] My website is Sadie St. Lawrence dot com. All of my social handles are Sadie St Lawrence, so I'm very active on Instagram. Twitter, I'm getting more active LinkedIn. So I'm definitely happy to connect anywhere.

Harpreet Sahota: [00:51:59] Sadie, Thank you so much for taking time out of your schedule to be here.

Harpreet Sahota: [00:52:02] I really, really appreciate you coming on the show.

Sadie St Lawrence: [00:52:05] Thanks. It's been my pleasure.