brandeis-marshall-2020-04-15.mp3

Brandeis Marshall: [00:00:00] I'm so passionate about it because I know that when I was going through computer science, undergrad, graduate work, I didn't see many people that look like me in the days were hard and the days were long. And I'm still questioned with my credentials even to this day. And I want it to be a place where it doesn't matter what you look like if you have something valuable to contribute to the tech space that you're able to do that. It does not matter your skin color. It doesn't matter your gender. It doesn't matter your sexual orientation. It doesn't matter what class you come from. There is true equity. And only the only way to have true equity is if the culture itself shifts and it changes and it actually embraces diversity. Don't be scared of it. Embrace it.

Harpreet Sahota: [00:01:05] What's up, everyone? Welcome to another episode of the Artists of Data Science. Be sure to follow the show on Instagram @theartistsofdatascience and on Twitter @ArtistsofData. I'll be sharing awesome tips and wisdom on data science as well as clips from the show. Join the Free Open Mastermind Slack Channel by going to bitly.com/artistsofdatascience. I'll keep you updated on biweekly open office hours. I'll be hosting for the community. I'm your host, Harpreet Sahota, let's ride this beat out into another awesome episode.

Harpreet Sahota: [00:01:50] Our guest today is a skilled explainer who has a knack for making difficult concepts easier to understand regardless of a person's educational background. She's a computer scientist who remains curious about how data flows through digital environments and its impact on the racial, gendered and socio-economic impact of technology. She's passionate about educating anyone on all things data, especially those who like learning, as well as promoting diversity within computing and data science.

Harpreet Sahota: [00:02:14] She has nearly fifteen years experience in higher education, including 10 years teaching databases and data related courses. Having secured over one point two million from federal and industry funded sources, she's also served as a principal or co-principal investigator in data science education, cyber-security, education and diversifying computing initiatives as a scholar and educator. She has been a leader and contributor to activities that support data literacy and understanding, shaping best practices for data and broadening participation in tech, particularly in computer science and data science. Her research areas include information retrieval and knowledge management for effective assessment and summarization of data. In order to create valuable knowledge through labeled unlabelled and fixed link data analysis with specializations in topics ranging from data mining and social media for applications within information assurance and aviation, she's been involved in a number of projects, workshops and organizations that support data literacy and understanding. Sharing Best data practices and broadening participation in data science, including the design of data science, pedagogy for marginalized communities and the assessment of socio-technical impact of black Twitter. She's earned a Bachelor of Computer Science from University of Rochester, a master of computer science from Rensselaer Polytechnic Institute, and has gone on turn a PhD In computer science. She served as assistant professor of Computer and Information Technology at Pretty University, Chair of Computer and Information Sciences at Spelman College, and is currently a faculty associate at the Berkman Klein Center for Internet and Society at Harvard University. She's also the CEO of DataEdX Tech consultancy firm dedicated to cultivating data competency, providing workforce development training within the data space. Since 2009, she's been actively engaged in mentoring the next generation of STEM professionals, particularly those from under represented groups. This engagement has included, but definitely not limited to serving on the program. Committees for ACM. Richard Tapia Diversity in Computing Conference. Grace Hopper Celebration of Women in Computing and National Academies of Sciences, Engineering and Medicine Roundtable on Data Science, Post-Secondary Education.

Harpreet Sahota: [00:04:22] So please help me in welcoming our very special guest today, a woman who is a beacon of light for the growing number of black and brown women pressing higher education and PGD in computer science and data science. A scholar, an educator and a strategist, Dr. Brandeis Marshall. Dr. Marshall, thank you so much for taking time out of your schedule to be here today. I really appreciate you.

Brandeis Marshall: [00:04:44] Oh, thank you so much, Harp. That introduction was long and I appreciate you talking through all of that.

Harpreet Sahota: [00:04:51] It's my pleasure. My pleasure. I think it's important that our listeners get an understanding of, you know, that the impact that you've had on so many people throughout the course of your career. I just wondered if you could talk to us about how you first kind of heard of data science. What drew you to the field and to some of the struggles and challenges you faced as you were breaking into the field?

Brandeis Marshall: [00:05:10] Oh, wow. Where do I start? Okay, I'll start easily with entering graduate school. And when I entered graduate school, I actually was very interested in UX.

Brandeis Marshall: [00:05:22] When I got there, the person who was a UX professor. Well, the well-known individual was actually retiring. And I was like, well, I need to find something else to do. And then happenstance I fell into -

Brandeis Marshall: [00:05:39] I'm looking at some data that was part of a course. And I was like, this is interesting. This is really interesting. I like the structure of the organization. I and then I start thinking, well, everything needs the structure and organization. So I've actually been a data head since 2000. I've been thinking data is cool from way back then. Took a data science, databases course that dovetail into information retrieval and that's wind up what I concentrated my PhD dissertation on. And so for me, data has been part of my entire career. And in fact, applying data in how data is applied in different spaces has been something I've been doing since I can remember it as part of my my graduate work. So what I see as far as getting into the field, it's a matter of where do you know the origins of the data? Are you interested in that part? And of course, moving forward and trying to figure out what the dataset is, figuring out, how do you know, clean up the data? How do you figure out how to analyze the data? So all of those parts are interesting to me. And that's kind of how I got into it. It was happenstance by luck, by interest, by passion. But I already was a computer scientist, so I always say I'm computer science first, data scientist second.

Harpreet Sahota: [00:07:01] Yeah, definitely both very closely related fields. And it's interesting because you kind of developed that passion just through working with the data. So it wasn't like you were just born data.

Harpreet Sahota: [00:07:13] head. You really cultivated that passion well while working with data and realized that I'm kinda good at this. This is interesting. And then just develop a skill on the way. Right?

Brandeis Marshall: [00:07:21] Right. There's always a way in order to do your best to verify the quality of the data. So for me, I'm always concentrating on, you know, where's the data coming from? And is was where the data is coming from vetted? Is it validated?

Brandeis Marshall: [00:07:38] So I'm really in the beginning part of the pipeline when it comes to data science. And I consider data science to be ubiquitous. It is part of every industry. Everyone is concerned with their personal data. They're also concerned with how their data is being used inside of an organization. So for me, I'm trying to do my best to be, I guess, in a person to be, I guess, that beacon to talk about data in sizable, understandable nuggets, because it's not just a science thing. It is our everyday life. Now we're creating data, using data, and we're consuming it at such fast rates that we need to be a little bit better at understanding it and harvesting it.

Harpreet Sahota: [00:08:21] So where do you see the field headed in the next two to five years? Kind of taking into consideration everything you've just said.

Brandeis Marshall: [00:08:29] Yeah. So next sort of five years is going to be one where I actually tweeted about this early. And in the top of 2020 is to say we're going to be looking a lot at the gender, race, class disparities that happened inside of data, how data is used. We're going to be concerned about who is participating, who has access, how inclusion strategies are working or not working, as well as who's represented in the data.

Brandeis Marshall: [00:09:00] We're seeing it over and over again where marginalized communities are disproportionately not included or over saturated inside of certain datasets.

Brandeis Marshall: [00:09:12] And how do we shift the conversation so that all people are included in the data conversation? So the next two to five years is going to now being bringing aboard the understanding of the importance and the power of data and how that impacts communities differently. And, of course, developing policies, enforcing those policies, whatever regulations at the local, federal, national level in order to make sure that data becomes part of our known fabric inside of every facet from curriculum at K through 12, through those that are currently in the workforce, in all workforces, not just STEM

Brandeis Marshall: [00:09:59] Don't get me wrong, I'm definitely love my STEM people, but it's an all workforces across the board. Everyone needs to know more about data.

Harpreet Sahota: [00:10:13] Are you an aspiring data scientist struggling to break into the field, then check out dsdj.co/artists to reserve your spot for a free informational webinar on how you can break into the field? That's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out dsdj.co/artists.

Harpreet Sahota: [00:10:39] So what could data scientists start doing today so that, you know, two to five years in the future? They've kind of got this what it sounds like, kind of a diversity of data, they're cognizant of it. They've got that philosophy, for lack of a better word instilled within them. What are some things that they could start doing today?

Brandeis Marshall: [00:10:59] It's really connecting with people that you haven't connected with before.

Brandeis Marshall: [00:11:03] Right. I think opening up conversation about how the data that you're currently using now impacts communities that you're not necessarily a part of. Get yourself out of a comfort zone. I think that is going to be key to anyone to do right now. And how you do this is you follow someone you've never followed before. I started following people that were in anthropology and other social scientists, economists, people that were in anthropology. Right. Completely outside of my domain expertise. But I can now see a perspective of how they see data from their lens. That's very important. If you don't do that, if you stay within your own lane in your own expertise, only talking to people who have your particular background, you're losing the whole story. And with data, there's always a story. There's always going to be gaps. And you need to as a data scientist, as anyone that's even dealing with data, you don't call yourself a data scientist, but you still deal with data need to get outside of your comfort zone. The second thing that I would add to that is documentation. Documentation is absolutely necessary. You have to document what's a process, scientific and non-scientific. You have to document these things because there is so much misinformation and disinformation that's happening in these systems, these tech systems that are being built and being [inaudible] and massaged, don't have proper documentation. And that's where there is, in the future, going to be some some challenges for organizations and for companies. So I think it's just very important that if you are working with any data that you're documenting or process, you really push your team and your teammates to document their processes because you're going to forget. And once you forget, it's gone. And that data is now going to be used in a way that maybe you did not intend. And it's important that you document your process and have a conversation about that process.

Harpreet Sahota: [00:13:12] Yes, very, very interesting. So you might have answered this already with what you just said, but to kind of drive the point home here, then what do you think it's going to separate the great data scientists from the merely good ones in this, you know, the next two to five years in this vision of the future that you have?

Brandeis Marshall: [00:13:29] What's really going to set those apart is going to be those that have open minds with very good documentation.

Brandeis Marshall: [00:13:36] Those that are consistently learning from sources of quality. And that means you're going to hit some bumpy road, you're going to hit some you know, you might get some disinformation, you might get some misinformation, but then you going to learn from it.

Brandeis Marshall: [00:13:51] And then you're going to now be able to discern what is quality and what isn't quality. Your then going to be able to talk about, oh, I know this individual is working in this space. That's not my expertise. So don't ask me. Ask this expert. And I think that's going to be extremely important for data scientists to not try to take on all the responsibility for the whole process. This is one where it is a it's teamwork. So you have to be able in order to share out where other people are better talent and a better fit to answer those questions.

Harpreet Sahota: [00:14:27] That's really interesting. Yeah. Thank you for that. I if we could talk to us a bit about DataEdX. Where you're guiding folks through the data space and elevating their data competency, breaking up into bite sized portions. Can you define data competency for us? And what's the process like for breaking data into these bite sized portions?

Brandeis Marshall: [00:14:48] So I consider data to be almost like an onion. So it should be the the the outside is the knowledge.

Brandeis Marshall: [00:14:58] That is what you you receive as part of a book, which is a part of media outlets. It's it's all things that are packaged for you and presented to you. But if you pull back that knowledge layer, you have information. And that knowledge layer contains the social context, a particular lens or view. So after you take out the social context, if you can, sometimes you can't, you get information. But information also has been packaged for you, too, because information assumes that you have a certain amount of reputation and credibility. So if you peel back information, you get data. And that's the raw form. So when I talk with individuals and with organizations about DataEdX, I'm trying to usher in with them a data company that means what is the raw first order unit? If that could be text, it could be video. It could be images. But what is the raw component and how is that raw component being used? And what is adding on to that raw component - like credibility and reputation, to give you information. And then what is that unit of information that has social context that gives you that knowledge? Right. So trying to usher people through what are some of these components of data that include collecting it, storing it, analyze it, visualizing it? And then what is the narrative that you're talking about that data?

Brandeis Marshall: [00:16:33] So data competency has essentially five different levels.

Brandeis Marshall: [00:16:38] So getting people to see data in that spectrum is something that's kind of not shared a whole lot. It's what's talked about typically is analyzing data. But to me, there's things upstream and downstream that we have to have more understanding about. And that's what I hope issue people. And for me, DataEdX. It's it's like it's a gym for the brain. As you go to work out physically for me, DataEdX is a space where you can really work out your brain. Work out

Brandeis Marshall: [00:17:14] What do you want. What do you want to work on. What you want to improve in your data understanding. And it could be in one of those lanes that I spoke about or could be about some hybrid of them that you might want to upskill as part of your career. Right.

Brandeis Marshall: [00:17:32] So it's really designed for those individuals that are currently working, right. For those that already have some experience with working with data in some way. And they now have a new responsibility and they want to be better at it, but they don't have time for a course to go back to school and go to school for six months or go to school for two years to get another degree. But they need us a little bit of help in order to just open up to understand a little bit more so that they can then move forward better in their current career or for something else they're looking for.

Harpreet Sahota: [00:18:06] That's a really, really beautiful initiative. That is amazing. I really like that holistic, holistic view and holistic teaching style that that you have for that, because I think a lot of data scientist, machine learning engineers that are so focused on their algorithms and optimizing hyper parameters, optimizing their code and all the technical stuff that they fail to look at the upstream and downstream implications of what it is that they're working on. Right. And that's really, really important. Wow, that's really awesome.

Brandeis Marshall: [00:18:36] I mean, it's really hopefully two to fill a gap, right? Because we have the traditional learning pass and then we have non-traditional learning paths. And I see DataEdX as being somewhere in the middle where you don't have to do an intensive program for a fixed amount of time. Maybe what you need is an engagement that is several months long or maybe more, depending on how you want to engage. But you have an idea of what you're looking for or maybe you don't, but you want to be in in a place and a network that's all striving to be better and understanding. And that's something that I haven't seen in this space, and that's what I'm working to build. Is is that that aspect for folks to go.

Harpreet Sahota: [00:19:30] I love it. That's really awesome. So I'm interested in hearing more about your research into the impact of live tweeting on social movement. I was wondering, what was the driving force behind that research and how did you get started?

Brandeis Marshall: [00:19:49] So one of my guilty pleasures is social media. So funny enough, this came about because I'm on social media and I'm I'm on Twitter.

Brandeis Marshall: [00:19:59] I'm on LinkedIn online a little bit on Instagram.

Brandeis Marshall: [00:20:02] And I realize that there is a whole subculture of people of color, particularly black.

Brandeis Marshall: [00:20:11] Twitter and I'd wind up seeing some entertainment beef, quote unquote, beef that was happening between two artists. And I was like, is this real? And so it it got my mind going about what happens if we just look at black Twitter. And for my day job, of course, I teach in higher education. And the students all look like me. So I said, you know, let me see if they're interested in this thing because they're on Twitter. They're in on social media. I mean, of course they're on, but they're mainly on TikTok and other mediums. But I decided that I wanted to better understand black Twitter. And there's some people in this space that already been doing work for years. You know, Archie Brock, I mean, there's so many people doing work in the space. So I was reading their content. And I figured out that I really want to have a better idea of understanding the Oscars because I was at the time where the Oscars so white became very popular. There was no people of color who were nominated for Oscars during the 2016 cycle. And so I decided to have my research students and I to now grab the tweets during the live broadcast. All three of us were grabbing tweets. We had done all of our preprocessing work in order to make sure we got the right hash tags and keywords. We started doing it and we did it again in 2017. Hidden figures had come out. So now there were people who were nominated and of course Black Panther was about ready to be released or just been released. So there was all that commutation. So then I did it in 2018 with the students again. So I just kept doing it. I actually just did it in 2020. So I've been doing it now for five years. And really the impetus came for trying to better understand the subculture because this is the first time in history really where black people can now report their own history in real time and see the impact of it.

Brandeis Marshall: [00:22:25] Previously it's been other individuals.

Harpreet Sahota: [00:22:28] Really interesting. How do you go about collecting that data? What type of data were you specifically collecting? Where there any challenges engineering the data - I'm using air quotes here for everybody listening - Engineering data.

Brandeis Marshall: [00:22:43] So we we did a manual process for this.

Brandeis Marshall: [00:22:49] So for about a month prior to the Oscars, we were scrubbing not literally as a tech term scrubbing, but we were manually going through what were the hash tags, keywords that were being used surrounding the Oscars when it came to any people of color. And we were cataloging those. We put them really into a spreadsheet because it was easier for all three of us to see a spreadsheet. And then we would review them on a regular basis in order to see which ones were appearing more often than others.

Brandeis Marshall: [00:23:21] So we looked at everyone who was nominated, anyone who was presenting, and anyone who had come at any type of comments that had risen to being inside of any type of op at. So we had those kind of three main criteria. It was very easy in 2016 because there was a lot of people that were in that space. So that's what we did in order to prepare in order to collect the data. And then the students put together a very short python piece of code in order to use tweepy in order to grab the tweets. And then, of course, just stored in two wonderful little text file. Right. Nothing fancy. No JSON, just I was doing - They were sophomores, sophomores in college. So they were pretty pretty new to the computing space. So we we did that. And of course, at the end of each of the live tweeting, we talked to each other about what we saw inside of 2016. What wound up happening was an individual. Everyone knows who it is if you did 2016. She came on stage, said a few words and then left.

Brandeis Marshall: [00:24:28] And so each said that the students actually didn't know who she was, didn't know who Stacy [Dash] was. So they had to they had those they misspelled her name. And we actually caught the misspelling over name inside of the results. So we collected the data in a way that was very manual because it was pretty fast because, you know, they were new in the space. I was just trying to get something going for them. And then at the back, it was all about processing. So it was a learning all the tools. It was learning about plotly. It was learning about matplotlib inside of python. It was, you know, showing them how Excel can't open up their file that's four hundred megs and why we need to separate the file. So it was definitely a learning tool that helped them better understand that, you know, working with data can be very challenging, but also can be very rewarding because at the end, the students were able to now, you know, map the tweets based upon regions in the United States. Another student wind up looking at the different languages that came out in the tweets. Right. You saw, you know, Portuguese, French, English in different parts of the world so she wound up being able to do word clouds on that. So there was just many different ways in which this series of datasets were used and the students were able to see certain trends. And that's what we actually did a research paper on where the trends that we saw, which was all the key words and hashtags that we were looking at.

Brandeis Marshall: [00:26:04] That was concerning black Twitter that we constituted as black Twitter only received about five percent of the tweets. If anyone's in the black Twitter space knowing that black Twitter has a very large presence. So we already knew that there was some filtering of our ability to stream in all the data. Right.

Brandeis Marshall: [00:26:25] So it's just another conversation piece, another conversation for us to have as instructor to, you know, mentees, but also for them to move forward in their job interviews and their grad school applications. And so it opened up a space for them to talk about tech that was related to who they were. And I think that is the amazing part about the black Twitter project. I don't have students right now working on it, but it's definitely one of those passions that I think opens up doors to understanding data.

Brandeis Marshall: [00:27:02] And it could be used by anybody. 

Harpreet Sahota: [00:27:04] That's really cool project and really awesome way to get students engaged at such an early stage in their academic career. Because we've picked a project that's really going to resonate with them and their interest and they're going to be motivated to pursue that further. Maybe students who have previously not even considered careers or education paths in data now, because of this opportunity,  have gone on to do so. Do you have any interesting insights from your project that you and I could share with us?

Brandeis Marshall: [00:27:31] Oh, there's so many interesting ones.

Brandeis Marshall: [00:27:33] So we looked at not only Oscar so white each year, but we also looked at the Me Too movement and we also looked at Oscars less white. So what was very interesting in all the data that we're able to go through, this is 2016 to 2018, is not only did we see our black Twitter hashtags being used about five percent of the time we actually saw a spike in the Me too movement. More specifically, hashtag me too, as well as of course, the woman, Tarana Burke. Right. Because Tarana Burke wasn't really a well-known name at the time. There really was Alyssa Milano, unfortunately. So we actually saw a spike in in the mentioning of Me too as well as Burke. And then in 2018, the full press of the Black Panther was something that we couldn't get away from. It is very interesting to see each of the characters, as well as each of the actors who played those characters, received a lot of mentions, tweets and comments about their work. So those are just coming a little nuggets. The 2019 and 2020 haven't processed yet, but hopefully they'll be done in the next few months here and hopefully that work will be out soon.

Harpreet Sahota: [00:29:04] If there is any impact that you want your work in this space to have on society as a whole?

Brandeis Marshall: [00:29:10] As a whole. Wow. I think it's the importance of culturally responsible tech work. I think the reason why this project is something that I talk about so passionately and people ask me about is because I am a black person that's talking about black Twitter and there's not many people on the tech side that are doing that.

Brandeis Marshall: [00:29:34] And so I want the the ability for other people to know that they can talk about their particular ethnicities, content in a research space, in the tech space and still be successful, because that is one of the misnomers, is that if you talk about, you know, black things and you're black, you're not going to think people. I don't think you're credible. And I want to move that dial a bit and say, no, you can you can talk about. I can talk about black things, be black and still be known as a scholar.

Harpreet Sahota: [00:30:08] So kind of leading into the next question. Talking about that inclusiveness and  marginalization in the data workspace. I think it's really important that our audience kind of hear you out on this, because I think it's super powerful and super important. In the interviews I've heard you always speak so boldly and eloquently about it. So I just kind of wanted to let you kind of have have added to say what it is that you would like to say about about that.

Brandeis Marshall: [00:30:36] Oh. How much time do you have?

Brandeis Marshall: [00:30:41] I think this might be a whole separate talk, actually, because I feel as a a woman, a black person who who has obtained some level of success as perceived by the mainstream, that you can engage with anybody no matter what, if you're open. So to be inclusive doesn't mean that you are pushing away anybody. It actually means that you are seeking out those who have open mind in order to hear you and not to validate you, but to hear you as well as to make sure that you do not press or suppress anybody who has been marginalized. The recognition of people of color inside of all tech space is abysmal.

Brandeis Marshall: [00:31:38] I talked quite frequently about the textbooks that I've had in computer science. How many have had a woman as an author? I think I can only think of one. Now there's a database book that I had Jennifer Windham, the only one I can think of right now. That needs to change. The only way that's going to change is if the actual culture changes. Tech culture needs to shift.

Brandeis Marshall: [00:31:59] It's not a - we're just going to hire a diversity equity inclusion officer to then go ahead and hire people of color to put them into a toxic environment. We can't do that anymore. That does not work. And it's not going to. It's not going to work. And I'm not going to champion anything that does that. And I'm going to tell anyone who's marginalized in the tech space, don't fall for the okey doke. What you want is to actually feel safe. It's about safety. Do you feel safe in your workplace? Can you share with people? And can people share with you and not push you down?

Brandeis Marshall: [00:32:31] So, as I said, I think this is a whole separate conversation.

Brandeis Marshall: [00:32:35] But really, it's I'm so passionate about it because I know that when I was going through computer science, undergrad, graduate work, I didn't see many people that looked like me in the days were hard and the days were long. And I'm still questioned with my credentials even to this day. And I want it to be a place where it doesn't matter what you look like. If you have something valuable to contribute to the tech space that you're able to do that, it does not matter your skin color. It doesn't matter your gender, it doesn't matter your sexual orientation. It doesn't matter what class you come from. There is true equity and always going to have true Equity is if the culture itself shifts and it changes and it actually embraces diversity, don't be scared of it.

Brandeis Marshall: [00:33:28] Embrace it.

Harpreet Sahota: [00:33:30] I really like that because I mean, me being an Indian male. Everywhere I go, I'm a minority, except when I'm in the data space because everybody else that I'm working with, looks just like me. Right? So how could I embrace this diversity? You mentioned you mentioned seeking out others points of view. Now for validation just to hear each other out.

Brandeis Marshall: [00:33:54] Yeah.

Harpreet Sahota: [00:33:55] What are some actionable tips that, you know, I could implement in my daily life?

Brandeis Marshall: [00:34:01] Well, I have a list of books. There is a list of books that I can give you.

Brandeis Marshall: [00:34:07] There is Ruha Benjamin's Race after Technology. There's pretty much anything that Andre Brock has written that...as a way in order to start. There is Data Feminism is a new book. I haven't read it yet, but it's on my reading list. There is there's Algorithms of Oppression by Safiya Noble. That one's really good. It talks about algorithms being filtered. Particularly by the organization Google. And how what that looks like and how it it suppresses oppresses black women, girls and Asian women and girls. Very, very interesting. Very good book. Definitely read that. Very well sourced. So well sourced, it got a lot of backlash. So this is a list of books to read. 

Brandeis Marshall: [00:35:05] But it's an ongoing process. There is another podcast. I would suggest looking into it, which is hash tag cause a scene by Kim Creighton. She talks directly about any blackness and white supremacy and what that looks like and how all of us, even black people, are leaning toward being anti black and being white supremacist. There is the work of Jesse Daniels. There is so - It's so rich with people. Once you get one person and start following them on social media, you start seeing other people. Right.

Brandeis Marshall: [00:35:41] There is a friend of mine, Mitalay.

Brandeis Marshall: [00:35:44] She is working in the space of race and tech and she's coming from an area of media. What does that links look like - Media and entertainment? So it's a matter of reading the works of individuals. You can start with the books. You can move forward and look at some of the OP Eds. But then it's also engaging with people. Its engaging with people you've never engaged with before, having conversations about tech with people who don't look like you, who maybe are not in your day to day space and seeing what they think about it. I think it's it's very important.

Brandeis Marshall: [00:36:19] Those are very actionable items. How much time do you have to read all those books, but you can definitely read some of those Op Eds.

Brandeis Marshall: [00:36:29] And hopefully they're all audible. So you will be able to listen to.

Harpreet Sahota: [00:36:34] I go through audible books at 3x speed. So I'll blast through those and I'll definitely include those books in the show notes so that our listeners can get into that too. And I think one thing that's really resonated with me from that is that because I know a lot of aspiring data scientists, up-and-coming data scientists, they're just their heads are just in the books. They're just study, you know, technical materials without consideration for the world outside. I think it's really important that they start being more holistic in their approach to data science, because it's not just about machine learning algorithms. It's not just about math.

Harpreet Sahota: [00:37:12]  Python. NumPy. Pandas.

Brandeis Marshall: [00:37:14] Right.

Brandeis Marshall: [00:37:14] Right, exactly.

Harpreet Sahota: [00:37:15] It's so much more than that, especially if you want to be a data professional today. Right. The implications are widespread throughout technology.

Brandeis Marshall: [00:37:24] Yeah.

Brandeis Marshall: [00:37:24] I mean, the home to me...I think one of the misconceptions is that, you know, in tech, there's this mantra of, you know, move fast, you know, optimize more.

Brandeis Marshall: [00:37:40] And I and I don't understand as someone has been in a space for a number years. Well, why are we doing that? Why we're trying to move so fast. What are we moving fast to? Why are we not considering people as part of the tech process in a careful way? Right. So I think that is what I'm trying to bring more to light is that this holistic view that I have is really because I've seen how I've been discriminated against because of tech. I've seen how students that I've taught have been discriminated against because of tech. All right. And there are individuals who have a lot to offer in this space. And those particular gifts aren't being absorbed within tech because it didn't fit into an algorithmic box. So we need to be in the Matrix. And as I just saw, The Matrix this weekend I didn't really get a break through The Matrix.

Harpreet Sahota: [00:38:41] Not for the first time, I hope, right.

Brandeis Marshall: [00:38:43] No, it was like like the 50th time

Brandeis Marshall: [00:38:45] I don't even know how many times I but I actually sat and watched the whole, the whole trilogy like back to back. Oh yes. I think it's necessary to get to veg on some movies every now and again.

Harpreet Sahota: [00:38:58] Yeah, definitely. But like the work you do in that space is super important. And I'm glad you're putting light on it. And it's really important. I think our listeners should really take them into consideration as well. 

Harpreet Sahota: [00:39:17] What's up, artists? Be sure to join the free, open mastermind Slack community by going to bitly.com/artistsofdatascience. It's a great environment for us to talk all things data science, to learn together, to grow together. I'll also keep you updated on the open biweekly office hours. I'll be hosting for our community. Check out the show on Instagram @theartistsofdatascience. Follow us on Twitter @ArtistsOfData. Look forward to seeing you all there.

Harpreet Sahota: [00:39:46] Shifting gears a little bit here, I heard you talk about grit. Very briefly in another interview that resonated with me because I love that book by Angela Duckworth. So I was wondering if you could share any experience in your past where you had to embody grittiness and maybe some words for encouragement for for any of our listeners out there who might be facing something similar?

Brandeis Marshall: [00:40:09] Oh, grit. When haven't I had to initiate grit? I think I guess I'll go back to college. And there's been places all throughout my life, but I'll go back to college. And there is a class that you take in computer science data structures. Hopefully your listeners. Will not start twitching when I say that? Because it's always the make or break class. Right. Right, Harp? It's the make or break class. So in that particular class I was I was earning - I didn't know. But with the way the institution did, it was that they had to go into the professor's office and the professor would tell you which a midterm grade. So really, let's set the scene. You are 19 years old and you're told in order to find out your midterm grade. You have to go to the professor's office in order to hear it. And it's like a one on one conversation. It's not like a group thing. It's a one on one. So very intimidating. And I went and then I, I, I received my grade. It was a B minus. And then I was essentially told you might not make it depending on where you are. Right. You might not make it through the class. This may not be a good selection. That's what I heard and that's what I remember. So I walked out and I was like, I'm getting a B minus sweet. I'm going to get a degree and I'm going to get my PhD. Like that was it. I I'm. I, I'll know what he's talking about. So that was just one example of you. You see a situation with a different lens. And I think that's what gritty people do, not that you have blinders on, but that you look at some of the positive, you understand the negative, though you don't discount. So I understood ok I'm gonna need to work. It might get a little tough, but I can do this.

Brandeis Marshall: [00:42:06] Hopefully that helps somebody. Hopefully that helps somebody.

Brandeis Marshall: [00:42:11] But I think my my my best advice to anyone that's currently in a situation where they're faced with a bunch of adversity and they're feeling like they don't have enough grit, I actually think you need to go and watch a movie because your concentration on it might actually be blocking the talent that exists within you. So I mainly tell individuals, go have some ice cream, go watch bad boys, too. Well, no, that's not that one. Bad boys one. The original was the best battle. Go get some ice cream. Go get some hot chocolate if it's cold outside and after it's over with. Get back to it because you have to get your mind out from thinking negatively, badly. On the way to do that is to do something completely fun, completely enjoyable, so that me get back to it. You can see it in a whole new light and then you can go for it.

Harpreet Sahota: [00:43:04] Awesome advice - I love it.

Harpreet Sahota: [00:43:05] One last question before we jump into our lightning round here. What's the one thing you want people to learn from your story.

Brandeis Marshall: [00:43:12] I want people to learn from my story that I'm not done yet. If you feel like you're done, that's not the data science, that's not the data science. And the story is never complete.

Harpreet Sahota: [00:43:23] I love it. I love it. Jumping into our lightning round here. What's your data science superpower?

Brandeis Marshall: [00:43:32] This was actually a very hard question and I don't know how. I don't know what what is what is a superpower.

Brandeis Marshall: [00:43:37] So I think my superpower is the ability to explain things easily to folks that they can understand.

Brandeis Marshall: [00:43:46] I think that's my superpower.

Harpreet Sahota: [00:43:47] So what's an academic topic outside of data science that you think every data scientist should spend some time researching. 

Brandeis Marshall: [00:43:56] Sociology

Brandeis Marshall: [00:43:58] You have to understand social context. If you don't understand social context, you don't understand data and you're tanked. 

Harpreet Sahota: [00:44:04] I had more than one person say that.

Harpreet Sahota: [00:44:05] So what's the number one book, whether it's fiction or nonfiction? You'd recommend our audience to read and your most impactful takeaway from it.

Brandeis Marshall: [00:44:19] So I would say, oh, see, it's hard because I actually have two. Can I say two that if it said, OK, OK, OK. So the first one is Algorithms of Oppression by Safiya Noble.

Brandeis Marshall: [00:44:31] I mentioned that earlier, that one because it talks about algorithms with social context and how it's been manipulated. The second one is. Who gets what and why that's by a Nobel laureate, Alvin E. Roth, I believe his is name. And he received the Nobel Prize in economics with two of his comrades based on market analysis. And I think everyone knows about the kidney transplant struggles. Right. There's not enough kidneys. A lot of people need kidneys. And so these three individuals actually founded the idea of kidney transplant chain. One person has a kidney that another person needs. And so forth and so on. Until the person, the original person who needed the kidney, he received a kidney. So it could be two, three, four, 10 people in the chain. But he goes through other scenarios in the book of how market, market design works, including, you know, charter school systems and things like that. So it's very impactful to learn how that particular problem was solved by economists. The supply demand model was very interesting to me.

Harpreet Sahota: [00:45:53] I'll add them both to the show notes. So if we can get somehow get a magical telephone that allows you to contact 20 year old Brandeis, what would you say to her?

Brandeis Marshall: [00:46:06] I would say it's going to be OK.

Brandeis Marshall: [00:46:09] Your time to shine isn't quite yet

Harpreet Sahota: [00:46:12] You eventually did shine through with dozens of publications. What's your favorite publication?

Brandeis Marshall: [00:46:20] My favorite one. I think the one I am most proud of happens to be the black Twitter, the black twitter one. Mainly because it was not only socially relevant and timely, but also because I did it with students.

Brandeis Marshall: [00:46:38] So it was nice to engage not only with peers, but it's nice to engage with with students in there and see their development and growth.

Harpreet Sahota: [00:46:48] And which of your publications do you think it's most relevant to our current times?

Brandeis Marshall: [00:46:55] I think there is a more recent publication I did with Frontiers in Education, and it talked about a faculty development program. Trying to develop faculty in data science. It's really part of a project that's funded through the NSF in order to see data science in different curricula at the at the college level.

Brandeis Marshall: [00:47:19] And so I think that one is probably the one that needs a little bit more review, because there's a lot of data science programs that I'm not quite sure how that standardization is going, because I believe it's going at all.

Brandeis Marshall: [00:47:35] So. So how can, you know, upskill faculty and do it in a way that it makes sense for the faculty life?

Harpreet Sahota: [00:47:43] What's the best advice you've ever received?

Brandeis Marshall: [00:47:46] It's so funny. I ask this question to my class. The other day I got some great responses and this is what I told. Am I the best advice I ever received? This comes from my great grandmother who has since passed. She told my mother and my mother told me. And it's don't take any wooden nickels.

Harpreet Sahota: [00:48:04] Don't take any wood Nickels.

Brandeis Marshall: [00:48:05] Right. Because wooden nickels don't exist.

Harpreet Sahota: [00:48:08] Huh. All right.

Brandeis Marshall: [00:48:11] So you don't want to take any with nickels like that.

Brandeis Marshall: [00:48:15] It's a...

Brandeis Marshall: [00:48:17] It's a very, it's a it's a Deep South black culture type of piece of advice, but it's a goodie.

Harpreet Sahota: [00:48:25] I like it. What song is giving you life right now?

Brandeis Marshall: [00:48:29] The song that gives me life is Won't He Do It. And it's by Koryn Hawthorne. She was a winner of one of those shows, but that one gives me life.

Harpreet Sahota: [00:48:42] Awesome. And how could people connect with you? When can they find you?

Brandeis Marshall: [00:48:47] So I am on a mainly Twitter @csdoctorsister spelled out. I'm also on LinkedIn. So feel free to connect me there. You can also get a hold of me by just going to BrandeisMarshall.com. You can see what I've been up to and contact me the email of course. So just check it out. And then of course in the next few months, DataEdX will launch officially. So you can go to DataEdX.com and sign up, subscribe. So you get to get on our email list and we'll let you know when we can get you in that community.

Harpreet Sahota: [00:49:25] Yeah, I'm looking forward to that. I'm definitely gonna be scoping out that Web site and joining that email list. Dr. Marshall, thank you so much for being so generous through time. Really appreciate you taking time out of your scheduled to be on the show today.

Brandeis Marshall: [00:49:36] Thank you so much for having me. This has been a blast. Good luck to you.