anderson-prewitt-2020-08-23.mp3

Anderson Prewitt : [00:00:00] We talk about Data a lot of times people only really kind of focus on the quantitative side of the Data, but there's a qualitative side to right. And so I think a lot of times we look at just on Data his numbers and obviously the numbers. Yeah, but what does that mean and just what the numbers mean. What does that mean to your employees, to your people, to these people, to whoever? How do you feel about that? Like how does that fit together? And I don't think you can have a full picture unless you put all those different types of Data, all those different types of information, unless you put them together. You're not telling the full story. And I think at its best, I think that that is really all about just telling a story. Right. And understanding what the story is, because you can look at numbers all day and if they're just numbers on a page, but if you look at the numbers in context, they can actually tell you a story about what happened, what's currently happening, what's about to happen.

Harpreet Sahota: [00:01:05] 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 bitterly 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: [00:02:05] Our guest today is the thought leader in innovation, education and entrepreneurship.

Harpreet Sahota: [00:02:10] He's earned a bachelor's in electrical engineering, a master's in electrical engineering, and has also earned a master's and Ph.D. in materials science and engineering. He's currently the CEO of Prewett Solutions, a technology sales and consulting firm that leverages data science for economic development system, engineering for small business success and innovation management. In addition to working as an engineer and researcher with several Fortune 500 companies and universities before starting his business. He's also had an active career as a researcher, author and speaker in the areas of innovation, education and entrepreneurship. He's given talks across the country on topics ranging from student success and stem to how to leverage technology for business success. He's also the co-author of a book for students interested in pursuing careers in technology titled STEM Navigators. So please help me in welcoming our guest today, a champion for leveraging technology to promote transformative systems change. Dr. Anderson D. Prewitt.

Harpreet Sahota: [00:03:18] Dr. Prewitt thank you so much for taking time out of your schedule to come on to the show today. I really appreciate having you here.

Anderson Prewitt : [00:03:24] Thank you for having me. I appreciate it.

Harpreet Sahota: [00:03:25] So talk to us about how you first heard of Data science and what drew you to the field.

Anderson Prewitt : [00:03:31] Yeah, so it's funny. I can't say I really, like, heard of it, if that makes sense. Right. What actually happened with me was more so I was in school, you know, as I was taking classes and trying to really figure out how I was going to graduate. I took one or two classes just getting some electives for electrical engineering. So it seemed interesting to me because I was doing electrical engineering. But I still like computers more than necessarily the just straight hardware side of it. And so I started taking a couple of classes. They had just offerings on like things like pattern recognition and neural networks. What is this? And so that's kind of how I first really kind of got introduced to the concepts. And then later when I went on working, I would see again where I would think like her, you know, that stuff I learned may actually be applicable because I was doing things like process improvement or Six Sigma, where you actually collect a lot of data and then apply it to improved processes and things like that. And I realized, like, there's probably some applications for that type of stuff when you're doing classification and things like that. So it kind of went from there. And what's funny is I think I never really considered myself a data scientist. So I just thought I was this engineer who did some  stuff with Data. And I think probably where the real shift took place, where I really kind of started doing more data science than anything was probably right before I was going to graduate with my PhD, I was in the lab. I was doing hard, hardcore like research and using the piece of equipment for some of the experiments we're doing in material science.

Anderson Prewitt : [00:04:54] We had to use what they call neutron diffraction and not get into too much detail. Essentially, you collect information about what's going on inside of material and you're using these big nuclear reactors that are basically just bombarding this piece of material with a bunch of neutrons and you're collecting information that comes off those detectors. And so this thing is sitting in there for like days and weeks at a time. And you have to take all the information and interpret, OK, so what was happening with this material while you were doing these tests in real time? And I would say like the physics and stuff and the science behind how the materials react, what happens with that? I didn't really know how to actually deal with all that data. And especially my advisor was like, well, you should probably figure that out if you want to graduate. So I went back to the books, so I had to figure it out. And I have to realize, like, how can I what's going on here? How can I do some patent classification on these graphs and things that we got out? How can I basically, instead of me having to go one by one, how can I use Matlab or some kind of code and teach the computer to actually find these patterns, these things I'm looking at for me so that I can then understand? OK, well, if this is the pattern that I see based on these detectors, then this is what's happening. And now that I have that physical understanding, I can apply it to understand what's going on with this material and to know how to do other experiments or to do other stuff.

Anderson Prewitt : [00:06:09] You're producing it. And so from that, after graduation, I went on and really still didn't really know doing that science. So that makes sense, as crazy as it sounds. But after graduation, what I was looking for jobs and figuring out why I went to the next someone approached me who knew that I had done a lot of work with Data while I was in school. A while I was training and they said, hey, you have like ten years worth of data from this program that some students our grants ran out. We need to do something with it. Can you help us kind of interpret this data and tell us what's going out here to some analytics on it? Can you help us? We'll pay that, have it and have a job like this. I was like, yeah, sure, I can do that. Right. Figure out the rest later. So, yeah. So basically I was able to kind of help them use different techniques and software and stuff to kind of really interpret and go through what they were doing. And after that was successful, somebody else kind of hit me up and then somebody else eventually by like the third best customer I have. One of my friends was like,  Hey dummy, why don't you want  start a company or do something like actually be a business person, so I was like, oh, OK. And so I started the company doing that. I will say this. The interesting part about that whole story is that even then, I don't think I really put Data scientists or anything like on my resume.

Anderson Prewitt : [00:07:19] I really thought of myself as that I was just an engineer and now a business owner who we do stuff with that if you have it. And we just kept doing that because only fairly recently, since Data science and big data has come so involved. Right. That Data science, the new hot term topic career, I think had changed on my LinkedIn profile by maybe less than a year ago.

Anderson Prewitt : [00:07:40] Like I actually do this and I've gotten so many people like me up, like, oh, wait, we never understood it. I was like, what? I've been doing the same thing for years. I don't understand why it's such a big deal now, but that's kind of how long story short, I kind of got here and doing it now. So it's pretty interesting, I guess.

Harpreet Sahota: [00:07:56] Yeah, definitely. I love hearing about how people get into Data science and sometimes they even just kind of accidentally stumble into it through the course of their normal work. Like we had this Data is just what I have to do as part of my job. And now it's, you know, it's evolved into something so much bigger. So what were some of the challenges you faced, like coming up through school? Because PhD highly educated. Was it something that was just easier or was there a lot of hard work? Were there challenges?

Anderson Prewitt : [00:08:24] Yeah, definitely wasn't easy. Well, I put it like this. It was hard, but it wasn't I think as hard as some people think, like, I do a lot of stuff with students and talk about, like, you know, STEM and science, technology and math and just working with students, kind of helping them not only get PhDs, we help them get scholarships and just giving them encouragement, doing it and home them. I look, man, if I can get a Ph.D. anybody, it's not necessarily easy, but there is a process to it. I think that the most challenging part, quite honestly, isn't even, I would say the science. Right. It's not even like the  because that's different books. Right. You can learn that. You can you figure it out. But when you're talking about going to like the level like the PhD and beyond mastering and just really getting really knowledgeable in some area, you're literally creating new knowledge and finding something new. And that's more challenging just because the process isn't as scripted right or as it probably should be. I mean, I think the biggest thing that I think is missing in definitely in academia in terms of like how we train PhDs and scientists and things like that. But I would say in education in general is just better mentorship. And I think that goes from education to career to whatever in terms of if you look at like those last years, like when you can determinately like a PhD all undergrad, as long as you go to your class, you do the homework, you'll get your degree, you get all A's even in a master's, a certain extent you might have to have a higher bar in terms of what grades you have to make.

Anderson Prewitt : [00:09:42] But unless you're doing a thesis or was not, this is just take your classes, do your stuff in your thesis, carefully scripted. You'll get your degree when you get to a PhD is literally like three to five people telling you, yes, you can graduate or no, you can't graduate. And it's not just satisfying all those other requirements of satisfying individuals and people and anybody who's going to pay a you can tell you all kind of horror stories about personalities getting involved and to advisors so you can graduate or just all these other things. And I think what happens sometimes is the nonacademic stuff. They can actually get in the way of people and individuals who actually want to succeed. And sometimes that's based on things like the imposter syndrome, like, you know, because where you come from, you think you can't do it or even, you know, there's see the things happening today. There are systemic inequalities in certain times that can inhibit people's ability for success. It's not unachievable or unattainable. It's not that it can't be overcome is just sometimes the important part is recognizing that it can be done and figuring out a path to do it.

Harpreet Sahota: [00:10:43] Thank you very much for sharing that. That was really insightful. So I want to get into some of your perspectives on where do you think a machine learning Data science and all that is headed in the next two to five years? So what is the field headed in the next couple of years?

Anderson Prewitt : [00:10:58] I think it's interesting and I think it is such an amazing time right now. Right. Well, the tragedy, what all the crazy stuff going on, if you almost regardless of what fields are arena, what you're doing, you know, what's going on today is vastly different from what it was like this time last year. And what it's going to look like this time next year is going to be vastly different. And I'd say that's probably true across the board. Almost every field, every you know, everything I think particularly we talk about things like Data, big Data, AI, machine learning, deep learning. I think that it's kind of come to that point where it's kind of in the vernacular of just kind of people these days. Right. So even if you've never even been to school or don't know anything, you probably heard it on TV or know something about it, even more so than it used to be, say, back in the day. And it's more accessible than ever before. Right. There's all kinds of free software that YouTube programs is these courses and stuff where you even if you're not at school, you can learn it. And honestly, a couple of months learn enough to for the basics to be an analyst or at least understand it. And so I think that's incredibly empowering for a lot of people if you get the chance to, because what that means is now you can learn how to not only have control your own Data, but.

Anderson Prewitt : [00:12:03] How to do all these amazing things really bring your ideas to life. I think it's going to be a big kind of game changer, especially look at the economic devastation that we're facing worldwide, these all the different factors going on. But I think the other side is like anything else, there's a gift and the curse. Right. The fact that is so useful, there's more Data than ever before in the history of mankind. Right. And there's so much information that's readily available to be used for some really good, great, cool things. I think the flip side of that is and we see it sometimes in the media and in our movies and just in real time, that Data can also be used to harm if it's not done the correct way. Right. And so I think a very good example is something like we look at things like systemic inequalities, a systemic racism, systemic sexism, all of it. We spend a lot of time talking about the sexism, the racism, the inequality. We don't spend as much time talking about the system and really about race. And racism isn't the person. Right. We talk about the one bad apple or the racist person who did this. To a certain extent, they don't matter. It's not about them. It's about the system that allows them to do that. So we're at a point in time now where technically you don't have to be an individual racist to have unequal justice for people in law enforcement.

Anderson Prewitt : [00:13:16] If they're using algorithms that will hurt certain minorities and certain groups, they have different outcomes. It becomes like their bail that's set by, say, a computer or something based on their record or something like that, that we're talking about systemic inequalities when it comes to things like income or wealth creation. Well, if there's algorithms that banks or credit companies used to create the conditions that allow redlining in these neighborhoods where black people can't live here, they can't live here, or that allow certain credit scores or something like that, think the apple cart that came out and they come to fan out of apple cars, kind of sexist, you know, because it was actually giving for this to people who may anyway, they would give one credit score to this guy and those credits go to this lady. These are things that are now suspended and AI have become part of that system. And so what happens is if we're not careful about how we treat these machines and how we're applying this great tool that we have, we're going to see some very negative consequences. And I think that's kind of a roundabout answer. But I think this kind of really where we're at, where, again, the gift and the curse, where it's like we have a huge opportunity to do some really, really good things with these tools and this data and information that we have. But we also must be very, very careful about who can get hurt and who gains and who are the winners and the losers if we're not careful about how we apply.

Anderson Prewitt : [00:14:36] And so I think you're going to see a lot more of the things like the ethics behind, you know, like people that are talking now about all these cool things with deep learning. And I think deep learning is cool, is great. I don't necessarily think the pinnacle, I think, is going to have to be what comes after. People are right. When you teach AI machine to kind of train itself, that's great. But at the same time, just because they can train itself doesn't mean it's "intelligent". I think the artificial intelligence part, I think they can kind of throw people because it's like, you know, the computer really inspired the baby, right? The same way you have to train a baby. You have to train a computer. When you first start, you get really bad stuff. But as it gets trained to get smarter and smarter, babies work the same way. However, that first couple of years there is still poop in diapers and they're still making a mess. And whatever else, I'll do the same thing if you give it bad information. And so I think a lot of times the Data we start with the assumptions we make. And if that's the starting place, you're going to see a lot of bad outcomes unless we have some kind of ethical way to reinforce that what's developed there.

Harpreet Sahota: [00:15:38] So what can we do as practitioners of Data science machine learning to make sure that the work that we're doing isn't perpetuating these types of negative biases and these type of consequences downstream for society?

Anderson Prewitt : [00:15:51] I think part of this, I think, is that you go for it, right? I think we have to start and really look at the the whole idea concept of artificial intelligence versus just artificial computing and thought whatever. Like, yes, there is a quote unquote intelligence we are thinking of. These computers are trying to teach them things and like a human brain and as a machine. And that's what happens there. But I think that we also have to understand there's a responsibility that comes with that. And I think that the best way I always try to look at it when I talk to whether there's my students who just when I have this conversation about it, I always try to frame and that is like the brainiac right now is like the supercomputer of Skynet. Whatever. I try to talk about A.I. in terms of raising a child or maybe whatever else. And how would you want to raise them if you train that child up or that baby up and you teach them a wide range of things, they get experiences and you yourself have enough knowledge and actually take the time to learn the right things, teach them, then that child has a better chance of growing up and doing the right things, being happy, being healthy and actually helping other people, if that's how you train them, if you really don't care and just let the child go and kind of run amuck, there's no telling what she'll get back. And also to there's ways. And I noticed this, that you can almost guarantee that there's a high likelihood that that child will do really, really bad stuff later on, you know, and it seems really, really silly to say it almost look at ourselves almost like we're like parents.

Anderson Prewitt : [00:17:15] Right? When you create this code, you're literally creating something with "intelligence",releasing to the world. How do you want to do that and understand the impact that you as an individual or you as a people will have? And I think if as practitioners we look at it that way, then we have to ask ourselves the hard question, like, OK, if I'm writing a program that will be specifically for, say, pregnant women. Right. Having never been a pregnant woman myself, can I write the program? Do I know the code? Yes. Does it make sense for me to get some insight or to talk to or to interact with some people who will be affected by that code to make sure that what I'm doing I have a full understanding, at least as much as I can get of how that affects them. And I think that that's kind of in a lot of times the missing piece, because it's real easy to look at. I look at this. They look look at this is great. But think about it now. Let's say you're writing code to make decisions about people who get medical procedures or something related, covid, whatever else. Now, we're not talking about just Data. We're talking about people. And so when the data points become people, then it gets real. And I think sometimes it's very easy as a scientist or practitioner, engineer, whatever, to to separate out the data points from the people point, so to speak. And so I think that we can figure out how to not do that or just kind of widen the conversation beyond just the ones and zeros. I think that's what's really going to make the biggest impact on fully comprehending the ethics behind what we're doing.

Harpreet Sahota: [00:18:43] So it's taking it one step further. Don't just worry about your evaluation metric or optimization metric, right? Because when we're building models, it's kind of what we care most about during that process. But is stepping back and saying, OK, hold on, let me see what is contributing to this and how are the features I'm using for my model, harmful or biased? I was just taking a step back and kind of looking at the picture holistically as that kind of. 

Anderson Prewitt : [00:19:09] I would say so because I mean, if you really think about it right.

Anderson Prewitt : [00:19:12] I think that helps in multiple ways. Right. Just from an ethical standpoint, I think it can be positive outcomes. You look at who can I help that hurt when I do and deploy this thing? Am I creating Skynet? Right. I have to ask that question. But I think even from a just being a better programmer, a better coder or better just thinking at a higher level in terms of wherever you're at in your position or your company or your business or whatever, it just makes sense to try to look at things holistically, like look at the traffic to a top level, the systems we use, think talk to other people to kind of get more perspective, because all that really does is it can be challenges and it's not a little bit right. This is how you feel. But it actually will make your code more robust to make your software more robust, to actually make it better, both for you as someone who's coding it and really think through all the parameters, but also for your end user or customer, whoever it is. And so I think that's just taking the time to kind of understand that piece. And honestly, depending on your level or your function or kind of what you're doing. And I think as a community of people who kind of practitioners, I think it's important to really have conversations about it often. Right. Because I think what that does is to to say those challenge the status quo. But I think that the more we as a community have these conversations and understand and try to bring these parts in, the easier it gets for us as a community to develop solutions to some of these really, really big problems that are happening as a result of those stuff we're doing.

Harpreet Sahota: [00:20:36] Speaking of ethics, I know that for a lot of people, that might sound like a very vague term. And it's not anything that really gets taught in Data science curriculums, which I definitely think it should be. How can we educate ourselves on ethics? Where do we turn to for guidance on that to make sure that the work that we are doing is actually ethical?

Anderson Prewitt : [00:20:59] Yeah, I think, honestly, you know, there's always the quote, right? Those who don't learn history are doomed to repeat it. And so I think that probably the best place is really just looking at history. And I think that there's so many historical examples of and I think history being just quote unquote Data science, but just the history of science in general. Right. So, you know, when I was in school for engineering to have minoring like, say, black history, so and also describe where everybody can study a lot of that and knew it. And I grew up in rural Mississippi. So the topics interest me and stuff. So I read books and things like that. But if you look at just the history of science when applied to bad thing right, and you can look across the world and what you'll see is, you know, you can basically tortured that they get to tell you whatever you wanted to to a certain extent. And a lot of times when you look at, quote unquote, bad science or pseudo science, it wasn't necessarily pseudo science at the time or even  "pseudo science", depending on who was using it, even in the US. Right. When you look back all the way, that is like slavery. There was actually a term for slaves who ran away or who to. Escape, they were considered a crazy science, say they were crazy, there was a term called Data Media, that person, oh, that slave is clearly insane because he's trying to run away from this nice slavery they're in. And so he must be a strict maniac and they chronicle the science behind it.

Anderson Prewitt : [00:22:17] When you look at things like World War Two and the Nazis and stuff, they were scientists that work there that had all this scientific data, why what they were doing was right and why use fear and stuff, whatever. And they backed it up with that and "science" in some of these are very depressing things to talk about. In your nice little book, the school program, I wrote a science class, but I think it's important to kind of really filter and or factor in an understanding of how a very, very good thing can be used to a very, very bad thing. And because a lot of times, again, we think in ones and zeroes and we're just talking about algorithms sometimes forget that depending on how you use those algorithms can't be people. And so I think that even some basic understanding of information can be misused for how one wrong line of code or not having a full dataset can affect people in real terms in real time, both historically and even some data right now where it's happening. I think that gives students or even older practitioners more perspective because you realize like, oh, wow, I didn't even think that was something to think about. But I think that kind of looking at the history kind of up to the president, those examples of how both science and even to Data science can be used effectively, but for negative reasons, I think that forces you to kind of think differently about what you're doing in real time.

Harpreet Sahota: [00:23:35] So it is taking ownership upon yourself to go and get educated, not just reading the Data science books, not just.

Anderson Prewitt : [00:23:42] Yeah, I think so. And I think honestly, obviously it's twofold. I think, one, if you're going to jump into history on the practice, that I think it makes sense to kind of take ownership or take stock of like that full spectrum. Right. To understand how it's being used, like has been used. Good has been. You bet. What am I doing or writing or understand that can adversely affect someone because it's just one of those things being an expert in being good at your craft, because depending on what you're doing, if you're right and they decide to go for a bank and you press the wrong button and lose half the money for all the people who are in there, that's a bad thing. And we know that. We sexualize that. Right. But if you only "do that, don't even realize". But you also lose half the money or don't or there's some subset population, then you may get to get their own money. You don't get to make money because of that. We don't necessarily see that. It's clearly so. I definitely say there's a personal responsibility to it. But I also think that as business owners, as practitioners, as a community, the use, the scientist, whatever it is, educators, I think there's a responsibility on the institutions that are either teaching or utilize Data scientists to make sure that's part of what they do.

Anderson Prewitt : [00:24:46] If you have a company where you know your employee data science or even just programmers, I think if you really have most couples have a mission statement of values and what they believe in, if you have that and stick by that and make sure that part of your mission and part of your values is to make sure that your employees and people who are practicing this are living up to those values as they're doing their daily work. If you're a university or some school for profit or not, whatever it is and you're trained people, how to do these things, part of the curriculum should be to understand the ethics and what are the negative consequences that can come if you do a bad job or if you use this skill set incorrectly. So I think that it needs to be kind of both where it needs to be a certain amount of individual responsibility, but also institutional responsibility as well.

Harpreet Sahota: [00:25:29] What's up artists, I would love to hear from you. Feel free to send me an email to the artists of Data Science at 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 by going to bitterly 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: [00:26:17] Absolutely. With you, and this is one of the reasons I push back against people who say they aren't data driven or they want to torture the data until it confesses or they're digging through data for insights because that's kind of the wrong way to go. You should work from the outside world to the Data. Right Data informed insights I think should come from the real world, your domain, and that using the data to kind of color inside a little.

Anderson Prewitt : [00:26:43] Yeah. And I think that's kind of one of those things where I think to your point, that's where you have to kind of put those things together. Because when we talk about data, a lot of times people only really kind of focus on the quantitative side of the data. But there's a qualitative side to. Right. And so I think a lot of times we look at just on data driven. So here's numbers and the numbers. Yeah. What? Does that mean that just what the numbers mean, but what does that mean to your employees, to your people, to these people, to whoever? How do you feel about that? Like how does that fit together? And I don't think you can have a full picture unless you put all those different types of Data, all those different types of information, unless you put them together. You're not telling the full story. And I think at its best, I think that that is really all about just telling a story. Right. And understanding what the story is, because you can look at the numbers all day and they're just numbers on a page. But if you look at the numbers in context, they can actually tell you a story about what happened, what's currently happening, what's about to happen, depending on how you're looking at them. But I think you have to look at it. You can't just have the content right or the Data and just those pieces. You have to have the context and the rest of them, too. Otherwise you're not getting the full picture.

Harpreet Sahota: [00:27:47] Thank you very much for that. Really appreciate that. Switching gears here a little bit, I wanted to talk to you about your book,"STEM Navigators". So what is it about and who's it for sure?

Anderson Prewitt : [00:27:57] So it's again, I feel like I've kind of backed into a lot of these different situations intentionally. So originally, the book was kind of written because of that kind of get out of school and started talking to black students and people who are getting into their degrees and some level of like science or engineering or and like that. People tell me that I got some good advice and they asked me again and again I was like, oh, actually, I got kind of dismiss emails like, oh, I should write this down. Right. And then what actually happened was I'm actually it's it's my book is coauthored by actually the five other authors.

Anderson Prewitt : [00:28:27] And we all kind of got together because we realized we'd all gotten our degrees and some STEM fields. Many of us, a couple of entrepreneurs, many of us have PhDs, some of us that had done a little towards working in education, something like that. And we all realize we had a different kind of story to tell. And so what we tried to do was kind of create a book that's not necessarily like a we wanted to have some here, some, you know, do this, do this, do this more, anything you want, like tell a story about, hey, in the reasons college, you know, stem navigational pathways to achievement in science, technology, engineering and math because we really wanted to kind of expand talk about tensorflow. There's no one pair. Right. So out of all the authors in the book, we all tell our individual story about, hey, I got to my degree, to my career, to whatever I'm doing, I got to my career and stem going this way. This other person got their career going this week and I just went that way. And here are the things that worked for me. Here are the organizations that joined here, the things I did to get letters of recommendation. Here is how I applied to colleges. I found scholarships to give that kind of real information, but also tell those real stories about, hey, for me it was a little different and I got here, but I'm not necessarily the typical right.

Anderson Prewitt : [00:29:34] And I think that for me, I think we try to be like anybody who's that kind of point in their career, whether or not even a career. But like if you're, say, in high school and thinking about maybe STEM before you definitely talked about how you can look through your pathways if you're a college student or even a grad student trying to figure out how to apply what you're learning, what's your path to success? Maybe this is a book for you kind of thing, even if you're not in school.

Anderson Prewitt : [00:29:57] But thinking about going back, I mean, I get your degree might be worth checking out because essentially I think what every one of the stories we all had different perspectives, all had different paths to how we got there and what we try to do as much as possible, make sure it was just short, readable and really spoke to, hey, this is my path and I might be your path, but whatever your path is, here's some suggestions and things that work for me or for us or whoever, and you'll probably find something you can use to kind of get you to whatever your next level is.

Harpreet Sahota: [00:30:27] I think that's awesome, because if you don't know that there are multiple paths to get to something, you're only going to go down the road that you know. Right. So it's interesting to be able to hear other people's stories, to see how they've gotten to where they got to, because then you can probably find OK, well, here's some roads that I might be able to take the get ready to go. You highlight a lot of amazing people in your book. Whose work do you think is most relevant to our current global situation?

Anderson Prewitt : [00:30:53] So it's funny. Like all different authors, they all do some pretty cool stuff. But one of the authors was interesting. And I think it kind of really speaks especially to kind of what's going on in the US, like how education looks different in is frame different for everybody. Right. Because it was like, oh, this is like a great student and did well. But like, her biggest fear before graduating wasn't like, oh, we're going to school or what have you got, literarian? That it was like, why do you stay in the country? Right. And I think that that kind of speaks to a laugh because she's kind of prototypical. Would you consider somebody who has all the checks, all the back in terms of being a great student, doing well, loves the country, loves the community, wants to make a big difference? Because I was interested in teach people in education doing these great things, but had challenges due to just institutional things and policies that weren't necessarily well thought out or good.

Anderson Prewitt : [00:31:39] Kind of fast forward, she's a serial entrepreneur. How successful did great in school educate businesses? That's all the stuff. But it wasn't a direct path, necessarily easy. And she had to kind of overcome a lot of those barriers. And I think when you look at today, that's kind of where a lot of people are, because there's so much strife internally, externally and worldwide related to everything from like you look. It like the U.K. and things like Brexit took place across the globe. Look at all the stuff in the US in terms of immigration and how things are happening. I think that how we interact with people from various different backgrounds, various different communities and how we bring them together versus separating them is important. I think it's also important to make sure that we allow space for everyone to learn from everyone and to be able to do whatever will make them and their community and our community better.

Anderson Prewitt : [00:32:29] Because at the end of the day, I think that one of the things that is often lost on us just as individuals, as people, as a society, is that we live in an ecosystem and it's an ecosystem the same way the water cycle in the environment, the ecosystem, we live in a socio economic ecosystem. And so if there's parts of our society that are bad or aren't getting what they need, even if we don't pay attention to them right now, that can mess other stuff up way later in other parts. Right. And I think that a lot of times, because we don't look at, again, society holistically, I think this where a lot of the issues that we have kind of come into play.

Harpreet Sahota: [00:33:02] So speaking of systems, with your background as a system engineer, can you talk to us about how systems work? And then maybe from there we can get into how systems can help us or hurt us. Speaking of systems of success versus systems of

Anderson Prewitt : [00:33:20] Ok, So I think the easiest way to kind of look at it is every system to a certain extent has essentially three main things. Right. It has some purpose or some function like this is what it does right in that system. It has certain elements or pieces to it that could be subsystems. They could be certain features or something. And then the third thing is just interconnections. Right? Those systems, those little smaller subsystems are smaller pieces or elements are connected together to help the major purpose Harp. And so in a nutshell, that's how literally every system works from the ecosystem to you look at your car to a bicycle, to even a school system or your business. They all work the same way. Right. There's some function that they're supposed to do. They're different subsystems and pieces to help them do it. And in those systems and pieces are interconnected or interact in some way to make the purpose. That, in a nutshell, is a system. I think that what's interesting, though, is a lot of times and this is the rub, right? It literally goes in order like that where you start with the purpose, that's the foundation. Then you have the connections that connect to whatever elements you have. The issue is that a lot of times we don't really fully understand the systems. And then the problem with systems is and I kind of lose that before we talk about systemic racism and things like that is if you don't really attack and change that purpose.

Anderson Prewitt : [00:34:42] The other piece is just looking at changing one element here, one element there. It may change how the system works a little bit. It may do some things to make it a little different, but not fundamentally going to change how the system operates. So what happens is let's say you have a bicycle,right a bicycle system. It has wheels has chains, you pedal and all this kind of stuff. If I remove the left handlebar, it's going to not work as well, but it's still going to be going well. But it's still going unfortunately have really stopped it. Honestly, moving just the front wheel. It's a lot harder. I can pop. Will you still be going? Right. And so what happens a lot of times, depending on the system, both good and bad, we'll call ourselves fixing the system. But really, all we did was I changed out this bicycle seat, this black seat with a pink one. And I'm sitting there thinking, oh, that piggyback system, everything should be fine. But the bicycle is still going and going and going. And so we talk about things like systemic racism, right? Just saying, hey, we're going to study why it is black people angry. We're going to study that. That's putting a pink bicycle seat on a bike and then still ride and still going the same way.

Anderson Prewitt : [00:35:44] You're not fundamentally changing it versus saying, you know what, let's forget this bike. Let's just build us a car or build a skateboard to build something new to get us from A to B. That's actually a systemic change because now you're changing the purpose from or the fundamentals of it. So you have more impact. And I think a lot of times what happens is even individually, we set up in our own the way we interact or the way we do things we have like our processes that we put together to create systems for our own success. Right. You go to school, you read books, you graduate, there's a school system there, things that kind of operate. But I think what also happens is if we're not careful, if you do the same things over and over again, these things have a negative impact on your life. You can inadvertently set up systems that don't allow you to succeed. Right. So things like people have different addictions and things like that with that addiction now becomes the purpose of your life. That becomes your major function. So now when you can't go to work and concentrate because you've got to go and deal with this addiction, you can't do the stuff you need to watch your kids with your family because this addiction has become your overarching purpose.

Anderson Prewitt : [00:36:47] And so that's one of the ways where it can kind of lead. And I think another example often uses, if you think a typical church or kind of religious institution. Right. If you have a church. Right. In your purpose for that church is to save souls. That's what you have on the plate. That's what you read in the book. That's what you're supposed to do and then that's fine and that may be what you're trying to do. And everybody, they're all good people and always do good things. That is truly the purpose of what you plan to do. However, because of other interconnections of the way and accept you, you can still mess it up where your purpose is supposed to be accomplished and then get accomplished. So if you have a church and your purpose is to save souls, but you end up getting a loan for some mega building and now you're spending literally all the interaction, all elements of your church are going towards paying back bank loan and you're just going and more time trying to raise money and pay off this bank loan. You are saving souls. Well, now all of a sudden you wind up shifting the purpose. So now the theory espoused a theory and use are different. And so so that can happen as well.

Anderson Prewitt : [00:37:45] So it's really about understanding how the function and the major purpose of what you're doing is directly tied to the individual pieces and the elements of how you interact and how your business or whatever interacts and making sure that the connections between them are solid and sustainable so that you can be clear about the purpose that you have. You have sustainable interconnections to be able to do it. And you have kind of a very stable elements and all the subsystems are stable so that you can be successful in whatever you're doing.

Harpreet Sahota: [00:38:15] Thank you very much for that. Very insightful. I know the audiences really love that. Speaking of church and speaking of purpose, I heard you speak on another podcast. He spoke about how you're really passionate about helping your local community with Data infrastructure. You're talking about pairing up with a local software company to help your church, which I thought was really amazing. A lot of listeners on the show are also going to be up and coming Data scientists and students. How can a student with nothing but a laptop and Internet connection use A. I For good?

Anderson Prewitt : [00:38:46] Actually, laptop, Internet connection. You can do a lot of stuff these days. Right. And I think that's the cool thing about it. I think, you know, I think it really start where you are like, I didn't go and be like, I'm going to today use this data and A.I to save the world and do great stuff in my community. You just, you know, as you learn, you know. Right. So I just saw a need words like, well, wait a minute. You guys want to know about this type information? Well, can we find Data on that? OK, let's look at it. Can this data tell us something informative about what we should do or can do or do next? You know, whether that's helping us to understand the system and the landscape around us, whether it's using that data to understand whether the program you're doing is successful or not successful, whether it's just finding out information that would be helpful to the people around you. And I think that, again, because of the tools that are available and the resources available to people now, I think there is so many more opportunities for anyone to do that. So as a student or as a practitioner, as someone who works at a local nonprofit or just knows something about your community and the scientific method for the same, you know, it starts with a question like, I wonder why this happens. I wonder why that happens. Right, the bushfires. OK, let's I have a question. Let's maybe not figure out how to better frame this question unless you form a hypothesis. Well, I think it will do this right then. Let's see. Doesn't do that now. Again, you've got a laptop, you got Internet. You can test that hypothesis. Can I find some data to prove yes or no? Can I do some predictive analytics to see if we keep doing this? What might happen? Can I run these experiments and do these kind of things? And then from that, you can actually look at the data and visualize and see what these what's been happening and the graphs, whatever. And they are from that kind of understanding.

Anderson Prewitt : [00:40:28] Now, you can actually start to kind of draw some report on it, draw some conclusions and really dig deeper analysis on, well, hey, based on this data that I found or that we collected that we've seen, we think this is what's happening. And here is the literature. Here's this other article, his other model that says this is probably what's happening, what we think we might need to do. And so if you know that now you address conclusions and have some better insight on what to do next and what is give recommendations or build some software or whatever, I don't know. But I think the point is the opportunity is there, the data is there, the tools are there. And really the only thing missing might be you.

Harpreet Sahota: [00:41:09] Thank you very much for sharing that. I wanted to pick your brain a little bit now about stem education. You've got a tremendous background in STEM education. So what would your advice be to students who are interested in studying science, technology, engineering or math?

Anderson Prewitt : [00:41:23] Yeah, I think it's twofold. I think one, I would give probably to slightly better pieces of advice. But what really saying I think that one, if you have an interest, explore it. Right. I think a lot of times, you know, really it's all about taking a deeper look. Right. A lot of the things that for me, for example, I got into really stem because I was born to a lot of the cool stuff. I liked having to have some science behind it. And when I looked at my comic books or I looked at movies or even just stuff, I don't like video games, there was some kind of science behind the kind of drawing closer to it. Like I first learned to code in high school, not because I was just a super coder and this is what I was like, I. Didn't really want to do my homework, and so when I was supposed to be typing class, but he might have figured out that, hey, if you can figure out how to get in, is what I figured out, that you actually get an apparatus on the back of a computer. We actually download this program. We can actually play video games instead of typing and just press the button to switch. So that's how I actually my first time really getting the learning code or DOS or just operating systems, it was like, oh, let's for this video game here.

Anderson Prewitt : [00:42:25] So out. So we just happened to be typing. I can go and play this. And while I don't encourage students to do the best stuff like I did, but, you know, you've got to go with interest, right. So video games, look at me. It's like so you can make video games. I understand this. I can do more stuff. That's an important thing. Right. And then from there is stuff like these superheroes and all these things seem cool. And there's so much science in these movies. I wonder how is that really how it works? Let me explore that. And so I really just think it starts with imagination. Just exploring what's interesting to you. You know, these days, you know, you have Google, you have the Internet, you have ways to find and learn more information. And I think that there's an interesting book to it. I know there's a there's a movie or a documentary about something like Star Trek. Right. There is so much like science that we have today that was not even really fashionable. It was just like an IBM. I thought, would it be cool to have wouldn't it be cool to have like a device that you could just have and look at and communicate with that you could hold in your hand like this is a rotary phone.

Anderson Prewitt : [00:43:23] Would it be cool to have that? Yeah, but who's going to have a flipping to talk to? That's silly right now. Right. We have the we have almost three quarters. We have the phones and there's so much other technology developing just based off those ideas. And I think that's kind of where it starts because it's not so much. A lot of times I always say Know Books is the start of asking why. And I think that's important. I think a lot of times when it comes to science, when it comes to these big ideas, you should ask, why not? Why can't we do that? What's stopping us? Why, you know, just be contrarian and just see. Maybe sometimes there's a reason. And I think that if you kind of start there, I think that's a great kind of intro to be like, well, is there a thing that can help me do what nobody else is not about or something that people have thought about? But I don't know. How can I learn now? And I think just use the tools that we have available to do that.

Harpreet Sahota: [00:44:13] Kind of going back to your book, STEM Navigator's talk about curiosity and asking why not? Do you feel that was a common thread or formula to success for the navigator's that you highlighted in the book?

Anderson Prewitt : [00:44:25] I think so. I think if I can say that, like the common theme, I would think it would be the like at some point figuring out that just because the path that you were on or what you were doing, what you were doing, didn't look like what everybody else had done or whatever might have been done before doesn't mean it's necessarily the wrong path. It might just be different. And I think for me and probably everybody else who was like author would probably agree with that, because really what it came down to is like, you know, you try you try again and you realize this. Right. That is wrong. But you don't stop trying know you don't give a lot of times you just got to find what works for you or what that missing links sometimes is. Sometimes it's the right mentor, sometimes it's the right funding, sometimes it's the right program or just a little bit of new information. And you're good to go. But overcoming the imposter syndrome, overcoming systemic issues, you know, overcoming your own personal things you got to deal with are all parts of it. And everybody has different kind of road to go down. But I think that as long as you kind of understand that just because it's hard, just because it's difficult, just because it doesn't seem like what the path that you're taking is the same as everybody else's doesn't necessarily mean your path is wrong. Maybe you just need some help. And I think that if you remember that, that's probably what I would say is the underlying theme in the whole thing.

Harpreet Sahota: [00:45:42] Can you talk to us about if you've ever encountered or had to battle with imposter syndrome? And if so, how did you overcome that?

Anderson Prewitt : [00:45:50] Yes, regularly. I think it's one of those things where it's I think you always kind of constantly I was like, man, I really was like I think even, you know, it's funny. Like even after doing getting up to your PhD year as you're working on it, you're like, man, am I really ready to defend this dissertation that I like time. Write a whole book, you walk in the room, you are all butterflies. It's like, man, am I ready for this? I don't know. I know it well enough. I don't know. And it's just one of those things where I think it's it's kind of a constant in the back of your head kind of thing. Right. And I think that everybody has it to a certain extent and for various different reasons. I think a lot of times you'll see incidences where a lot of times women, minorities, people of color, things like that, you know, sometimes experiences even more because they have those other systems that are operating to kind of reinforce it sometimes. And sometimes getting yourself to kind of here to get above the noise were saying, well, you know, you didn't you didn't pass that. Why I love to be here. Well, if you didn't understand this thing, you'll probably never be successful in this. I literally had a professor tell me. I remember when I was an undergrad like, oh, well, I've been selected and I needed a job.

Anderson Prewitt : [00:46:56] And so I applied to be, I think, a greater four, like one of these courses and matter of fact. At a department whose class had taken recommended, as a matter of fact, for the position, but the guy who made the final decision was like, yeah, well, I looked at your this grade and it was OK, but she passed. But how can you pass what kind of grade or whatever. And I just don't think you're cut out to grade for from electromagnetics class electromagnetics. It's hard and I don't think that's for you. Right. And I was kind of like very disheartening if I didn't tell you that, like, you know, whatever else. But I'm also kind of hard headed. So I didn't grade for their class, but I went on, I got a masters, the specialization electromagnetics. So I think overcoming it is more so about like understanding. You got to really kind of know who you are. And sometimes that understanding of who you are is kind of a combination of reinforcing it and getting that reinforcement from other places, like whether you just keep a journal of what things have you done, it kind of count your successes, whether you just make sure you have a big circle of people who will remind you, like he said, what a stupid. And you do this and this and this. And this is like, oh, yeah, I guess I did do it right.

Anderson Prewitt : [00:47:59] And just having those good things on your team. So whether it's from your religion, your background, your spiritual beliefs, your family, your friends, and just understanding yourself as a person, I think the more you do that, then I don't think it's necessarily easy to kind of overcome it because I always sort of reinforce and bring it back. But I think it makes it easier and they're more tools in your tool belt to kind of deal with it if it does come up.

Harpreet Sahota: [00:48:24] I'm not sure if you're familiar with David Gardner, but he has this this concept of the cookie jar, which is pretty much what you describe. Oh, yeah. Actually, I did do this before. OK, yeah. I've done all the amazing things that should be here.

Anderson Prewitt : [00:48:37] Yeah. I mean and even that I mean, I think I always say, like, you know, as once you get to like the you have like you have to like create like a sieve.

Anderson Prewitt : [00:48:44] Right. And I'm not looking at jobs so I still try to make sure I always do it, but I try to make sure that my resume. Yeah. Or put these little projects on that long things like for an hour or something or you just put your LinkedIn right. If you're not really trying to network or whatever else, just always update your stuff just to remind yourself that I did do that a while ago. I was doing that just because it's a good reminder for you, but also it's useful for other things like so for example, I'm currently the professionals chair for the national side of black engineers. Right. And so I'm basically helping them put together a conference. We just had one last week. We're actually putting together their next big conference for all the technical professionals who do stuff in that society. And I'm helping to organize the whole thing. And one of the things that we've been talk about recently, when I had to apply to be the chair, they were like, OK, we see a similar resume, actually. Send us your nasty resume and your resume. So that's the resume, just about the stuff you did with the engineer. So when you're in leadership that you're getting awards presentations. And I was like, yeah, I did a bunch of those I had when I got around back. And it was I was mad when they made me do it. But it was actually a really interesting exercise because when I got to see is and I've been a member of that society and like actually the other ones for like almost 20 years and in some cases a lot of the time. And you don't realize until you're like kind of forced put together like, wow, this interaction I had, the student actually helped me get information I need to get into grad school or doing these presentations actually helped me get ready for my dissertation defense or serving on this committee on a national executive member on one of the largest regulations in the world.

Anderson Prewitt : [00:50:15] At twenty somethin, I was on the board of directors for multimillion dollar organization. I didn't realize this kind of a big deal, you know, but you don't necessarily think of it in that context, right then. But I think doing that kind of forced me to kind of put it together in a certain way. And so I think that's just a good practice to kind of put your different stuff or whether it's organizations or and or just even like your community service, nonprofit stuff. A lot of times we're so in the rat race, we focus on my way for school and or for work or for what I've done here. We forget those hours and hours we spend every Sunday at church, those hours and hours we spend volunteering where we like to volunteer, those hours we've been coaching our kids little league, whatever it is. And we don't necessarily take into account the context of how much we actually learn from it and how beneficial it was to us or the community or other things. And I just try to make yourself seem Pliocene know whatever, but I think it's great reinforcement not only for yourself to know you're more than just your job are more than just what you do kind of thing. But also just to kind of show like skills and understanding and just personal development can come from anywhere. And so acknowledge that and look at it and pay attention to it, because sometimes we forget to do that.

Harpreet Sahota: [00:51:26] Thank you so much for sharing that. That was awesome. And congrats on obtaining that appointment. That's that's really cool. So what can the STEM community do to foster the inclusion of people of color, especially black Americans in particular in our field?

Anderson Prewitt : [00:51:40] There's a lot of things, I think that the some of it starts with just representation. Right. And I think a lot of times what happens is and I can definitely talk typically, especially like if you look at academia, a lot of times what will happen is, hey, let's hire a black guy, but let's hire a black lady and get, oh, we got a double minority quota. And so now we're good. And that's all they. Look at in terms of what they consider that inclusion or representation, when in actuality it's like, no, let's let's fully include these people and people like this into what we do. Right? If you just hire that one black person, are they part of the Decision-Making body, who can actually see what seems to be coming in, where the funding is going and things like that? Are they just there to be like, oh, yeah, put them on the poster and we're going to go ahead and do business as usual. If you know your institution where for decades you have not had a lot of minority representation or a lot of people or you have students who are complaining about their representation or something, and it was constantly and consistently kind of stepped aside or put away. And you're like, oh, this is not a racist place.

Anderson Prewitt : [00:52:41] I don't feel like there's racism here. Your students can tell you that maybe it's time for you or someone else to take another look. And I think that a lot of times what happens is that as a STEM community, we again, just like it times we focus on the ones and zeros, just the numbers just on the edge or whatever else. We don't talk about the context. We're not having a conversation with social scientists, not having conversations outside of just this small engineering circle of people who all look like this and have done the same things, some of the same schools we did. And so we basically keep reinforcing these same bad practices over and over again. If you're at a university or at a college where for historically everybody has come from the same college, you're all talking to the same people about it. And you for guys don't feel like there's any problem here. But yet you haven't asked any black women or any black man or any other minority of people of color and haven't really listened to them. When they tell you like, oh, they just that's just those those couple AirBnB else, then you have a problem. And I think that step one of the knowledge like, hey, there may be a problem, maybe there is some systemic stuff going on.

Anderson Prewitt : [00:53:45] And I think that one of the things that's happening now, we look at like between the death of George Floyd here in the US and all the international outrage about it, you've seen more institutions and more police acknowledge, you know, what, systemic racism, systemic injustice is a thing. And because it is the thing now, we start talking about think about what to do about it. And I think the starting point is, of course, obviously acknowledging it. But I think that if you really want to have true inclusion, you have to look at for everything that we do are people of color, are black people, are women included in that? We're talking about things of who's getting higher, who's getting fired. We're talking about where the funding is going and where it's coming from. We're talking about what's the curriculum, who's going educated, who gets admitted, who doesn't. Are black people included in those sections at the highest levels, or are they just they're over there and we're that really aren't really part of the conversation. We're just talking about them, but we're not actually talking to them or are talking with them. And I think that's where it really is going to start when that inclusion happens at every level.

Harpreet Sahota: [00:54:46] Thank you very much for your insight on that. Really appreciate it. So last formal question before you jump into a quick lightning round, and that is what's the one thing you want people to learn from your story?

Anderson Prewitt : [00:54:58] I think the one thing I would like people to learn from my story, especially when it comes to just understanding and learning about science and stuff, is that it's anybody can do it any way I can apply it. And so you don't have to get a PhD to be to do science or to do really cool stuff. Right. Learning is a lifelong process. And we live in a time now where there's so many things that are available to you to take advantage of really go and take advantage of technology, because we're at a place and we're at a point in time where either you are going to learn to use technology or you will learn you've been used by technology is really binary at this point. And so I think that is the more people kind of understand that, the better.

Harpreet Sahota: [00:55:38] Jumping in to a quick lightning round here with the first question. If you could meet any historical figure, who would it be and what would you ask them?

Anderson Prewitt : [00:55:48] I think I would probably really be interested in meeting Martin Luther King Junior mostly. And I would basically ask them, like, could I think one of the things that always intrigues me so much about Martin Luther King was like a lot of the stuff that people don't really talk about. We always hear about the I have a dream. We don't talk about the garbage worker strikes. And, you know, what he was doing after that. I have a dream speech and really the more rounded out version. So I was really asking, like when he realized the way he decided, when he fully understood like that the importance of the poor people's movement and how those things are integrated, because he was one of the first who really was really talking about that socio economic ecosystem has all related how you can't just talk about black people and getting the rights to vote without talking about these poor garbage workers, even if they are white, because that poverty and racism are all these things are all connected. So I really like to talk to him about that and how you how you put that together. That's fascinating to me.

Harpreet Sahota: [00:56:43] Yeah, definitely open a lot of doors for people like my parents to come to the States. So what do you believe that other people think is crazy?

Anderson Prewitt : [00:56:53] Well, probably a lot of stuff. It depends.Like you talk to my wife will say almost everything, but I think it's a couple of things like I know for me, I think one thing that I got a lot of. Back AIs, like the rest of artificial intelligence, and I refuse to say it's artificial, it's artificial intelligence, we're talking about babies, we're not talking about Skynet, we're not talking about brainiac. Let's start the conversation here, baby, and work our way up, because that's kind of where my starting place is. And I usually get a lot of pushback and they lose their science community for that. But I think the other thing just in general is like when a lot of times I'm always one way when we talk about like ideas or businesses, when I'm done consulting, I'm usually the guy in the room was like, no, let me hear the whole idea. Like, if you say I'll be like, hey, I want to sell hot dogs in outer space. I'm like, tell me more. Right. I'm not the one I got this stupid. I never know because I think that a lot of times we're very quick to dismiss ideas as things. But again, I always ask, why not? Right.

Anderson Prewitt : [00:57:47] So you want to sell hot dogs in outer space? That may sound like a crazy idea, but let me hear more. Maybe all you have to do right now, but maybe you have a great idea for some meatless, gluten free, very healthy thing. If you raise that plants yourself. It just so happens we have commercial companies that are going to space all the time. They're planning missions to Mars right now and they need some kind of sustenance and food for them, whatever else. So maybe you don't want to actually go to the moon, have had hot dog on the moon. Maybe you just want to be able to sell your hot dogs, your brand to people who happen to be in space, maybe connecting, you know, this hot dog out with the guy, SpaceX, Damascus, or somewhere else. Maybe he can in a couple of years actually be selling hot dogs in space. But if you're not open to hear him out or understand or put these disparate things together, we don't get our hot in space. So I'm kind of weird like that. I want to hear the rest of the story.

Harpreet Sahota: [00:58:38] I love it. So if he can have a billboard put up anywhere, what would you put on it and why have a billboard put up anywhere?

Anderson Prewitt : [00:58:48] I don't really know where in Times Square, I suppose we could make New York maybe anywhere, but I think it will probably be out when I believe especially like technology, like I think that I would say like if just to everybody, anybody who wants to listen, like learn to use technology. Right. Because you're going to learn to use technology or you will learn you are being used by technology. That's like a fundamental belief I have, especially right now. And so I think that's probably one of the biggest, I would say, takeaways from any time I talk, any time where I usually try to do with that are at least once or twice, because I think that's like the thing that everybody has to remember, but not enough people do, in my opinion.

Harpreet Sahota: [00:59:31] What do you love most about being a data scientist?

Anderson Prewitt : [00:59:34] I think it's just honestly, the toys like I think lose because things got weird. Like I think I really enjoy most kind of doing Data science right now, even more so than when I was when we first learned it. It was just neural networks or whatever, because now it's like there's so many more tools available and it's such a and I always brought a conversation because the more data available to do cool Data science stuff with. So I think that is for lack of a word, like playing with the toys or looking at new things in the new classroom. Do something about this. This is really cool. Oh, we can use oh you can use it to usually transforms. This is a pretty. Wow this is awesome. You know, like those new new alguien whatever else. Like I like the toys, the new stuff.

Harpreet Sahota: [01:00:17] We talked about selling hot dogs in space. But is there anything else that you're curious about at the moment.

Anderson Prewitt : [01:00:24] Yeah. And I really you kind of lose a little bit. I'm very curious in terms of like what comes next, in terms like how these different aspects of the technology and that kind of the quantitative side are going to start getting merged with that kind of qualitative understand the context. So what I've been really interested in looking at now is the whole kind of the kind of stuff like the whole socio economic ecosystem. Right. Because like what kind of systems work?

Anderson Prewitt : [01:00:48] How do they work together? And so we look at like even in our society right now, how are these things connected? How are they related? And is there data can we use it to show to show how those connections work? Can we use predict how we might work in the future? Can we use this to kind of help us? And how do we need to watch out for it to see how it hurts us? But I'm really fascinated by how this abundance of technology and tools to look at data and other stuff and this abundance of data that we have that's right there to be looked at how those things will emerge, but also how we as a society will be able to apply it. And my hope is that we apply it or apply our knowledge.

Anderson Prewitt : [01:01:28] Well, in terms of using it for data versus real.

Harpreet Sahota: [01:01:32] What is a academic topic or maybe just an area of research outside of Data science that you think every data scientist should spend some time studying or researching about?

Anderson Prewitt : [01:01:45] I think and it's not, I guess, all the way outside, but I think it is fairly a little different is really just looking at like the Internet of Things, because I think that we got a lot better now. And a lot of the new stuff is going to come from the not traditional stuff that makes. So I think the Internet of Things and having a watch while your dad and having these things in your house, these things and buildings and how they all fit together, I think a lot of the newer Data is going to kind of come from there.

Anderson Prewitt : [01:02:10] So in some ways it's going to to science. But the part that I think is really interesting is how people are using these things and how they can really be used kind of collectively. Right. So it's not just getting stuff off the Apple Watch, but has the Apple Watch Watch plus the Data again from the car, plus switchgear from the house, plus what you're getting about their health and whatever else, how those things all fit together to create, you know, better lifestyle. Like, how can they help with, like, health care, like the elderly or sick people, whatever else, you know, how can they help in fitness and understanding or just how could they help with education and learn? Right. And so I think just kind of really looking at the history of things and how those things are coming together and not just the Data part, so to speak, but the usage part and the user experience part. I think that's going to I think that will be really fascinating for from a data science perspective, because I think it will give you more insight about just where Data might come from, the future or where it's coming from now. But also it'll also help with I think we'll talk about early those kind of higher level ethical questions at some point, because you know that, hey, in other things is this Apple Watch can actually tell me stuff about people's location. And it's great to be able to get that Data do cool stuff with it. But it could be dangerous if there's a stalker or somebody else who hasn't had a science background to begin with and actually get to that. So I think that kind of understanding how will help a little bit when we got to ask what's the best method or what's the best thing to do that makes the most sense.

Harpreet Sahota: [01:03:39] What's the number one book, fiction or nonfiction? One of each. If you'd like that, you'd recommend our audience read. And what was your most impactful takeaway from it?

Anderson Prewitt : [01:03:49] Yeah, there's actually a book called Systems Thinking a Primer, and I think there's actually a really good book because I think you just gives a really good basic breakdown and like not only what systems are, but how they work. And I think as a as a data scientist, I think that systems part is key because I think, again, it's easy to think of everything and just ones and zeros. You know, it's easy to think everything is just the bad and stuff. But I understand, like, how those kind of those systems come together I think is really, really critical to understanding the importance of your role in bigger stuff, whether good or bad you have over the books,

Anderson Prewitt : [01:04:25] The book that has the slinky on the cover, right?

Anderson Prewitt : [01:04:28] Yeah, yeah.That's and I'm like, what was her name?

Harpreet Sahota: [01:04:30] yes there's Donella Meadows.

Anderson Prewitt : [01:04:34] And yes. So just like I literally I had my bag. I was just like I just I just thought a minute ago, like, OK,

Harpreet Sahota: [01:04:43] Yeah. Listen to look at audible. It's excellent, great recommendation.

Anderson Prewitt : [01:04:46] Yeah. And that's what I like about it. It's not like a ton of like equation's stuff like that, just like a real like straightforward like hey this is I do it. So it's a good quick way. Just gives you some understanding, insight into like how this office together. Oh so even don't do if I don't do anything and just let it this thing can go running amuck. If I'm not careful I need to think about that when I'm designing this program. I need to think about that when I'm doing whatever I'm doing. It's Yeah. Because very insightful in that way.

Harpreet Sahota: [01:05:10] If we could somehow get a magic telephone that allowed you to contact eighteen year old Anderson, what would you tell him?

Anderson Prewitt : [01:05:18] I probably give some stock tips, sure. But I think honestly, I would just say that, like, don't be so serious.

Anderson Prewitt : [01:05:24] Like, I think that there is I would definitely say make sure to pay attention to the, you know, the fun, you know, the good stuff and the good times, because there's in some ways the stuff you didn't think was as important and wound up being more important. You know, some of those relationships that you made when you were in college and whatever else were more important to some of those classes, you were breaking your neck, try to get through and just kind of understanding the importance of just, you know, relationships and just, you know, mentoring and just connecting with people. I think that's probably the biggest thing is like I'll take it for granted. Everybody's going to be here to be around. You're going to have opportunity to. So I think that's probably the biggest thing is just the importance of just those connections.

Harpreet Sahota: [01:06:01] What song do you currently have on repeat?

Anderson Prewitt : [01:06:04] Outkast, actually, Outkast bombs over Baghdad. I was actually listening to that the other day.  

Harpreet Sahota: [01:06:10]  A classic one.

Harpreet Sahota: [01:06:11] How could people connect with you? Where could they find you online?

Anderson Prewitt : [01:06:15] Yeah, they can reach out to our website, Data for that. Can you give me a bit of info at Dr. eight 'Ates dot com or I'm on LinkedIn D.R.A.B.W.F.F.T  Instagram, whatever.

Harpreet Sahota: [01:06:28] Dr. Prewitt, Thank you so much for taking time out of your schedule to be on the show. I really appreciate you coming by. Insurance. That's wonderful. Incestuousness. Thank you.

Harpreet Sahota: [01:06:36] Absolutely. Thank you. Thank you for having me.