2020-05-29-brenda-hali.mp3

Brenda Hali: [00:00:00] First of all, don't listen to those voices, because have a lot of people that they don't really understand that women can develop tech. Sometimes way better than men. Be careful with who you listen to, and be careful when those voices are even closer to you. And just like, say no, I won't listen to this. Just like push, honour strict communities, be a mentor of borders.

Harpreet Sahota: [00:00:42] What's up, everyone? Welcome to another episode of the Artists of Data Science. Be sure to follow the show on Instagram at @theartistsofdatascience and on Twitter at @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. Where I'll keep you updated on bi-weekly 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. And don't forget to subscribe rate and review the show. 

Harpreet Sahota: [00:01:31] Our guest today is marketing guru turned data scientist whose passionate about using data to extract decision making insights, understand causation, help companies grow, and make machines learn. She's got nearly a decades worth of expertise as a marketing leader and has experience leading high performing, culturally diverse teams and has a proven track record for adapting quickly. Having a solution-oriented mindset and serving as a reliable mentor to others. As a data scientist, she specializes in helping businesses make strategic data-driven decisions and marketing strategies. So please, help me welcoming our guest today. A woman who is committed to leveraging data to help shape a better world, one unbiased algorithm at a time, Brenda Hali. Brenda, thank you so much for taking time out of your schedule to be here today. I really, really appreciate you stopping by the show.

Brenda Hali: [00:02:16] Thank you so much. Thank you for the invitation.

Harpreet Sahota: [00:02:19] Let's talk a little bit about how you first heard of data science and what drew you to the field.

Brenda Hali: [00:02:25] I think that I have heard data science a couple of years ago, maybe like six years ago. And I mean, even though my background is not exactly in tech, I didn't study anything related to software or taking undergrad. I've been learning by myself. I like--through YouTube, through tutorials. Like I learned how to do--how to program in front and development. I learned how to build apps. And I learned--I even created like a bot for social media because I wanted to follow some certain hashtags. But I never did that as a formal education. And in that curiosity, I was in my tech conference because I love conferences and I love the media people. And I was amazed about all the possibilities when I heard that the term big data, but that was back in 2013 and I didn't know exactly how I could transition into data science. After that, I think that the moment when I decided to make for real that transition to data science it was because I was working in this program for helping entrepreneurs from Latin America, and it was a White House initiative. It was Obama's White House initiative, and we have all of these data from 500 entrepreneurs that we needed to find. We have several datasheets like from survey, CV formation, application. Over twenty thousand people applied, and we needed to look for some trends. We had a really small team and they were mostly inclining to political science, public policy, but not really into tech. So I came to the team and I literally show them how to use people tables. So the problem that we had at that moment was that when we were transitioning from one White House to another White House, a way how programs are a way that are in a different sense sometimes before you go. You could find like a couple of stories more and just share those stories like this is changing the world, and this is changing the U.S. as well. But in this case, they want that number. So they wanted to measure that impact. And we launch a survey and then we have all these responses from entrepreneurs. We don't know how to analyze all of the data that we have. It was a lot of data, a lot of rows. We didn't know even how to properly manage, like to read it completely or to find trends. And I remember at that moment, for my team, my team, they were mostly from the US. I'm from from Mexico, so for me, I'm from Mexico and I live in a couple of places in South America, like across Latin America. For me, that was like really close to my heart because I was seeing the impact of this program. I didn't want the program to finish. So in that moment, I basically told myself, I need to do something about these. I need to know how to analyze data because there must be a better way than just reading or printing a bunch of things and analyzing it in that way. So in that moment is when I decided to really look for that transition into data science. And it took me a couple of other years to actually act on it.

Harpreet Sahota: [00:05:50] Are you an aspiring data scientist struggling to break into the field? Well, then check out dsdj.co/artists to reserve your spot for a free, informational webinar on how you can break into the field. It'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: [00:06:16] That's really inspiring story, and I think that's really powerful because it goes to show to people that, you know, you don't need to have this background in some certain type of field in order to transition to data science. All you really need is, first of all, the curiosity, the motivation, just desire to actually learn something new. And you put all that together, and in your case, you put all of that together in service of some larger goal and transformed yourself into a data scientist in the process. And I think that's really powerful and impactful story. You've got an excellent career in marketing. So I'm curious, you know, as someone who is a marketer turned data scientist, what would you say that the data scientist and the marketer can learn from each other?

Brenda Hali: [00:06:56] This is a really good question. Really, really good question. And I am actually preparing a blog post about that because I want like marketers to understand where the trendings are going. So probably when this is published, you can add that there because there's going to be answered there. I remember when I started in marketing, I just started doing TV and radio. Mailing and some where offline initiatives. I didn't start in online and it was really hard to measure all of that. It was really hard to measure if we were paying one million pesos for a commercial. How do you measure if that was impactful or not? I think the beauty of the marketing that we are we are having right now is that we can actually track all of that because all the data is there. I think that the biggest problem is that sometimes marketers, they don't really have the tools or they don't understand how to predict a couple of things. And we can predict way more things than just the churn or if we are going to trends. There are a bunch of things that data science will definitely speed off how marketing is done. So one example of this is like chat books, obviously, that's part of the customer. You could meet there. A couple of articles of having written by just an algorithm, which is fascinating. You can understand better the trends in music in the case and you want to put like a catchy music. You can measure exactly what's working and when a customer is going to stop buying and you can push another initiative out of that. So there is limitless. Even TV like they're catching up on that and they're generating more data as well. But it's not, as intuitive and as well form as marketing data. 

Harpreet Sahota: [00:08:46] In the next two to five years, you know, in the near future, how do you see data science impacting marketing and what could the data scientists and the marketer do to best serve each other in this vision of the future that you have?

Brenda Hali: [00:09:01] Well, I see how possibly every marketing team now is going to have a data, a marketing data scientists in their team more than a data analyst. Like someone that really understand how to track the data. So I see in the team part to hire more data scientists. That's one thing that I've seen because before these data, these marketing team, it was more like creative people. But now I'm seeing that they're hiring more people that are like with a major in math, with a major economic side. You can say like why someone that is majoring in math is in marketing if marketing is a creative field. No, like everything should be based on numbers. So I'm seeing that field into that. I'm definitely more automation, but way more automation. And right now, 93% of the data that a small business have go to dark data. That means that that data is not used. So I can see also how even the small businesses in their marketing teams will start acquiring or will start hiring people in data science to use their data possibly. And I see like in the five years, probably most of the business will be generating big data, so that's where I'm seeing. Besides that, everything is going to be faster because of the use of GPUs, GPUs are getting cheaper. And that means that, for example, if before you run an algorithm to predict something and it took eight hours, now it is going to take 50 minutes. So that process is getting faster. So probably we're going to reach the point in which everything that's going to be like close to real time with big data and that with marketing data and with all the softwares is just gonna get crazy. Well, I love it.

Harpreet Sahota: [00:10:49] Sometimes when we're starting off with the data science project, it gets a bit ambiguous. So what are some of the first things that you do when taking on a new project? And what are some of the steps you take to kind of keep you on track while going through and navigating the ambiguity of a data science project?

Brenda Hali: [00:11:07] The first thing is to understand in which type of project you're working. You need to understand the overall where the company is going and what your contribution is gonna be in that project. And what is the contribution from that project to the big scene of the company? Like how you're generating that impact and that revenue or growth for the company. So, for example, it is different when a company is in a growth phase in which all the metrics that you need to generate and all the predictions are going to regenerate. They will generate it for growth and is different. For example, if you are optimizing for efficiency and if you are optimizing for efficiency, even in the same dataset, you can have different insight. Most of the time you need to have even like a different dataset. You can just use a dataset and then look for the findings. In the data science field communications, communication is everything. So you need to communicate clearly and to understand and receive constant feedback of is this working? We are trying this now. We are trying this now. We are testing this, is this working? Like first understand, second communicate. Understand for what you are--your algorithm, your data set is optimizing. The other thing, in the case that they don't know because it happened so long that you go and say like well I know above Data using your gold boards like give me go. And they don't know exactly what or how gold look like looks like in that moment. You need to have a lot of people skills to understand. That's the first thing, because if not, you will start building something that is not needed. So it is just like going back and forth between putting the goal, your goals, the goals of your team, they also the company and then optimizing for that.

Harpreet Sahota: [00:12:51] And actually, I read this awesome blog post. You wrote on a "Starting Guide to Excel at Teamwork." So I was wondering if you could talk to us a bit about the importance of teamwork for data scientists. Do you mind sharing the key points from that post with our audience?

Brenda Hali: [00:13:06] Of course I can. I can talk about that. Well, in this program I was working on, it was a [inaudible] for FEMA. A FEMA is the agency, the U.S. agency that helps to respond to natural disasters in the US. And in this specific project, we were focusing because in California, there are a lot of wildfires. And FEMA wanted to know what type of businesses--they have different businesses in their seven lifelines, meaning like lifelines, are the businesses that need to be alive for a community to be alive, so to speak. They have seven, seven lifelines and they need to know how and what type of businesses they need to target first to receive help and what type of businesses will be impacted when wildfire hits. So with a lot of Data in that case, we were a team of four. It has been one of the best experiences I ever had.

Brenda Hali: [00:14:03] Even though it was really short, but just because we knew exactly what we were looking for. So that's generally how that the of work we needed to go look at a bunch of Data from Google places. And so they changed the API. So we needed like to redo a couple of things from all their online resources that you can find.

Brenda Hali: [00:14:24] We need to collect data from Lou and from Joe and then from that the writing, because we need to understand the problem.

Brenda Hali: [00:14:33] My team was diverse we didn't have anyone that will be, I don't know that had some experiencing that in response to these answers. I think the closest one was one of one of our team members that he was in the Navy a very long time ago. So probably the Coast Guard, not even the Navy. He was the Coast Guard. I think that was the closest. Well, besides that, like we all went into a new field with tips that they share is you need to have communication with your team and that communication need to be in one place. I think that the mistake that a lot of teams make is that they don't have everything in one place. And we are humans. We forget. We forget whether we say it. I'm probably we're just holding the one thought of something that you agree. Two weeks ago. And right now it's nothing. You think so? In all these agile processes, obviously, in this case, we were not even looking software to implement an IJO methodology. But you can take a couple of things from the agile methodology, like to, to have everything in one place. And they're like, really basic things that worked. So have everything in one place communicate efficiently? Know the problem. Know the problem. And experiment fast and let things go like sometimes you write, you write a line of code that doesn't work anymore. So just let it go. Schedule a fixed time to have meetings because sometimes you just need to be record saying that you are on the same page all the time. So you'll see a schedule at a fixed time. Be comfortable with someone reviewing your work and actually that's going to be better. So if someone reviews your code and actually I remember that I work on another project. In which my team partner was really secretive. He didn't want me to see his score with. And that was so weird. I'm like, really open to everything. And I think that the best thing come when you are comfortable with someone reading your work, give feedback, be give kind feedback and learn how to receive feedback as well.

Brenda Hali: [00:16:36] I think that's summarizing how you can build a team and how you can move everyone forward in one goal.

Harpreet Sahota: [00:16:47] 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. And I'll also keep you updated on the open biweekly office hours I'll hosting for our community. Check out the show on Instagram @theArtistsOfDataScience. Follow us on Twitter @ArtistsOfData. Look forward to seeing you out there.

Harpreet Sahota: [00:17:17] So how do you think teamwork will change or be affected in this post-Covid world? What can we do to start being better team members when we're actually not going to be for a while at least some people aren't going to be in the same room, in the same office as their colleagues.

Brenda Hali: [00:17:33] Document everything that you do. I think that's the first thing. Everything that you do document it. And you need to have and really care what you're working on every week. So put like how many hours you're working on something and send that to your boss send that. Send that to your team even though like, maybe they don't need even care it's going to help you to know that you're like a vaccine every week. But also it's going to help your team to know exactly what's on your plate. Besides that, obviously, I have managed people and the people that communicate more with me, even though sometimes they then receive feedback from me. Are the people that I went to keep in my team.

Brenda Hali: [00:18:07] If you are a great communicator or if you are not developed, that's put. Weekly goals. Reach your goals. Communicate what you are doing in the practical side. If you are having a meeting in the same room now, that probably will transition to a hybrid between people that are on site or people that are not on site. Everyone needs to be on their own computer. So you don't follow that conversation, have everything in one place is Slack is amazing for communication, but at the same time everything gets lost.

Brenda Hali: [00:18:38] You do not know exactly how to track. So use other tools like Knowshon, use project management tools. Ask people how they are when you start a meeting in this case and they key to do have to be a better teammate is to over communicate. And sometimes people that program, they don't like to communicate, they just like to maybe hide their computer writing the code. Listening to Queen. But you need to over communicate. Because if you over communicate, you can be constantly in the same role and in the other side.

Brenda Hali: [00:19:14] So, for example, you are not having a lot of work. Take classes like right now there are several like a bunch of different courses that you can take to advance your career and communicate that to your team.

Brenda Hali: [00:19:25] To, why?

Brenda Hali: [00:19:26] Because if everyone starts like, getting better. Also, the company is getting better. And also your your manager will know the person that is going to be right there for promotion is going to be you. So that's that's another thing that I that I see that could be good in the managerial part. Yeah. Just don't disappear for days and don't answer, like maybe you can just answer with an emoji or something, but answer every question and answer every text. Answer everything. Don't say don't leave people in limbo because that's that's the equivalent off of your money. You're coming to you and saying like, do you have these? And then you just done reply physically. That's the same.

Harpreet Sahota: [00:20:06] Yes. Really excellent point. I think now more than ever, the communication skills for data scientists is really what's going to separate the good ones from the great ones. Right. Because before it would be OK. Completely OK. Just, you know, show up to work, maybe only speak to people during your daily standups. And that's it. But now it's great. You might need to err on the side of over communication just to keep in contact and make sure progress is being made on projection. You don't have that face time in the office, so to speak, anymore. So you've got to make up for it a bit digitally.

Harpreet Sahota: [00:20:38] So I was wondering if we can transition a little bit here and speak to your experience being a woman in tech. Your involvement with Latinos in tech. And if you have any advice or words of encouragement for the women in our audience who are breaking into tech or who are currently in the tech space. 

Brenda Hali: [00:20:56] It's difficult to get into a field where you are not represented.

Brenda Hali: [00:21:00] Like if you see yourself represented. Definitely you will know that that's something that you can achieve. It is a strange because a lot of people say, like had the opportunity decided there by the woman are not applying, but they are not studying this. It's their fault. But at the same time, we need to represent the people that are our being there. If if these are representation is not coming with a diversity is difficult for everyone to see themselves out there.

Brenda Hali: [00:21:27] And that's basically why Latinas in tech and another thing a lot of things. But everywhere it looks, it works like to communicate to people helping each other and not just in their representation part, but also in giving opportunities to others and opportunities to people that look like you.

Brenda Hali: [00:21:45] Practical advice that I will give is look for a community. If you feel comfortable with people like that look like you. I'm sorry if it is like a Latina or if it is a woman. If it is, look for your tribe and there are people that are struggling the same way. Find mentors. That's really important and find people that probably they don't look like you, but they can guide you to all the way. And that will open a lot of opportunities.

Brenda Hali: [00:22:13] For you be comfortable with being the minority and take advantage of that. A lot of meetings that I have had in tech is 20 dudes and me. But they always remember me because I was the girl there. Take advantage of that. If you can be comfortable with being that minority and slowly you can help others to grow. So I can share a personal story on how it was hard for me and how since the beginning you have all these voices of people saying that this place is not for you.

Brenda Hali: [00:22:45] And sometimes it is as close as a family member. When I was trying to get in to select [inaudible], when I was like 16, 16 years old, I remember that I asked my uncle because my uncle was a civil engineer and he was like my representation. Right. I wanted to ask, like, hey, uncle, how do you study these? Like, where they you study what should I do?

Brenda Hali: [00:23:04] And I went to my uncle and I told him that I knew I want to study something related with a computer as, as you did, and he say, you know, you should study something for women. This is not for women. That marked me in a way that later I didn't want to study something that was not for women. And that came like into my brain saying if I study this, I am not a woman. I am not feminine because all the girl's, he he went on saying that all the girls are. They study these. There are more like lesbians and things like that. I was like, but I'm not lesbian so I don't. So one thing that I can say is like, first of all, don't listen to those voices, because a lot of people that they don't really understand that women can develop sometimes way better than men. I'm sorry, but I'm getting into a feminist but. Be careful with who you listen to and be careful when those voices are even closer to you. And just like, say no. I wont to listen to this. I think you can sort of select what word you're going to listen to, just like push others to communities and be mentor of others.

Harpreet Sahota: [00:24:11] That's very powerful, right, because of things that people can say to us. When we are younger can impact this for several years after that. So like you mentioned, it's good to make sure we surround ourselves with positive influence, people who are going to be encouraging us and encouraging our kind of interests. So what can the Data community do to foster the inclusion of women in Data science and A.I?

Brenda Hali: [00:24:33] You are doing it right now, which is listening the voices of people that are not represented normally. So, you are doing it definitely right now. But another thing is giving you the opportunity to someone that doesn't look like you give opportunities to new people as humans. And our professional career is full. We have people that invested in us, either their time, their money. And probably maybe our parents didn't invest their let's say their time, because they were they they were not Data scientist were not in a science majors. So your parents couldn't invest that in you. But there are other people that invest that in you and that say this person is going to be great. And even though this person don't have the resources, not just economically resources, but connections, which is huge. So you can meet your you can see yourself represented. They gave you an opportunity. So as you just remember, how many people gave you an opportunity and give opportunities to others seeing the same way. I am not a big fan of, for example. I hear a lot of my friends saying a woman shouldn't be higher because she's a woman. She'll be higher if she is great. And they go like all the way to the opposite side, saying, like, they shouldn't be higher just because they're woman. Probably that's true. I'm probably will get there in which everyone is going to get higher just because they're great.

Brenda Hali: [00:25:57] But right now, the system is so uneven. Somehow we need to develop systems that can help to even this this system.

Brenda Hali: [00:26:06] So if you are a guy that is listening to this just understand that the system has been so uneven for a long time. Give it a couple of years give it that couple of years and allow hiring these diversity hiring. That is how they call it.

Brenda Hali: [00:26:23] But that's what you are doing there, is helping a woman to grow, to be in managerial positions, for other woman to see themselves represented and to transition in the field. You will be surprised on how great tech women can develop. Yeah, it is not a matter of if you are a woman or if you are a guy. But we need this representation. And more in Data and  A.I because you need to be represented, like you need to represent the whole spectrum of people. And if you are excluding woman from your team, youre excluding the 50 percent of the world's population and probably the people that will sell.

Brenda Hali: [00:27:02] Very powerful message. Thank you so much for that. And we, we need diversity of thought, diversity of opinion, diversity of people, composition on our teams with those diverse perspectives. Right. To help us. Develop better products, too. Also, just going off on another awesome writeup that you had on Medium about the four things that women should consider to make the tech world their next big success. Would you mind covering those points for an audience?

Brenda Hali: [00:27:29] The point number one in that article is don't be afraid to explore. Which means even though your uncle is selling you that that's not for women. Just if you are feeling that in your heart.

Brenda Hali: [00:27:41] If that is where you want to go. Just do it. Even though you and select others and undergrads as ideas. But if you are curious, just transition like. Come on. We have probably for me I will have forty five more years of professional work.

Brenda Hali: [00:27:56] Like that's crazy. Forty five years where I can be doing whatever I love. So just do it. Explore. Go. Even though people don't look like you, just go and be OK. Another point that I that I say there is trust. Your plan is if you make a plan. Don't think that because some other people sometimes you say like oh but this person I don't know this Indian dude. Sorry. This indian person he has a PhD in Data science. He has a PhD in machine learning what I'm going to do. If I didn't do that? If he is amazing, just trust, then things will come out on the start doing it yourself. Start moving. Maybe you have a great plan. Act on it. Delegate. Let's be honest. A lot of like I'm married. And even for me, even though my marriage is sort of equal, I like to cook. I like the house to be sparkling clean. Just delegate the things in the house. Delegate the things in your team as well. If you're leading a team, delegate the things that you cannot do, delegate the things that are not worth your time. And learn how to do it. Sometimes we feel that we are the best to do it. But if you delegate, you will be surprised on how people can excel as well. Be comfortable with being in the minority. Be comfortable with being in that meeting where no one looks like you. Be comfortable with being the only person that comes in high heels.

Brenda Hali: [00:29:27] Be comfortable with that and not just be comfortable. Lead the conversation because there is a huge advantage of being a woman in tech because there are not a lot of women.

Harpreet Sahota: [00:29:37] Last question before you jump into a lightning round here. What's the one thing you want people to learn from your story?

Brenda Hali: [00:29:43] See, how I see my life is I will be working until like I am probably 70, right. Because like, I'm healthy, I exercise 70 and then I'm going to retire. I am 30. I have a lot of years still to work if you need to transition into something that you love, do it.

Brenda Hali: [00:30:03] And it's better if you do it early and later, if you want to explore whatever thing you want to explore. We are not longer in the society in which you find a job right after college. You do your MBA or whatever, and then you work on that until you retire. We are not there anymore. So just be open to explore and to explore and to follow your passion as well. Another thing is never, ever, ever stop learning. Never. And this sucks. That's the thing that sucks a lot about the tech. The tech in general is that sometimes that you learn so well. Two years ago now, it's no longer relevant.

Brenda Hali: [00:30:44] And be comfortable with that and be comfortable with grading your skills to communicate and properly to.

Brenda Hali: [00:30:51] If you are in tech to be like a more integral person in a way that you could perfectly communicate perfectly, you present, and maybe you move to different roles, but just explore the things that are out there. It never is too late to transition. Never is too late.

Brenda Hali: [00:31:07] It took me eight years. 

Harpreet Sahota: [00:31:08] Yeah, excellent point. Especially like the part about lifelong learning. I think if you sign up for a career in Data science, and I'm sure my audiences heard me say this hundreds of times by now. If you sign up for career in data science, you're signing up for a career in lifelong learning. You have to be a perpetual student and it sucks if you don't like learning. If you think that once you learn the skills to get the job, then that's it. But it's great if you have that attitude. Yes, I love learning. I love keeping up with new tech. I love learning new things continuously, pushing myself to learn and grow.

Brenda Hali: [00:31:39] I've been thinking even about how Universities, probably will change their business model. I mean, the education field I'm seeing. How do you go to university? You go to M.I.T. probably instead of just taking off four years class, maybe you will take up two year's class and then you will have updates and classes for life.

Brenda Hali: [00:31:59] I've been thinking because that's so relevant. Like even in the policy making. Even even more like the people that used to the policy 20 years ago on a policy that is made right now. It's not the same. Commerce, marketing and their contents. It's like every field is being touched by tech.

Brenda Hali: [00:32:16] Somehow everything you see is going towards that if you can be that person that will update yourself, I think is going to get easier for you. Don't be resistant to technology because it's coming.

Harpreet Sahota: [00:32:27] Yeah. That's a really good point. I think it is. Ernest Hemingway. And he wrote this about writers that we are all apprentices in a craft in which no one will ever become a master. And I think that is now more true of every field than it was just one particular one, because everything is changing. So let's go ahead. Let's jump into the lightning round here. What is your Data science superpower?

Brenda Hali: [00:32:52] It is that I can grow things with Data. I love to start things and to grow them.

Harpreet Sahota: [00:32:57] Like a Data gardener.

Brenda Hali: [00:32:59] Exactly. I am a gardener myself, so.

Harpreet Sahota: [00:33:03] Yeah, there you go. So what's an academic topic outside of Data science that you think Data scientists should spend some time researching on?

Brenda Hali: [00:33:11]  Effective communication. 

Harpreet Sahota: [00:33:13] Yeah, communication is definitely key. So what is the number one book, fiction, non-fiction or both that you would recommend our audience read. And what was your most impactful takeaway from it?

Brenda Hali: [00:33:24] Of course. The book that I recommend the most Is this one that is called Nudge and is improving decisions about health, wealth and happiness and needs like how we create patterns to make decisions. And that's like to analyze you, right? Because if you don't feel yourself as an integral being, you're not just a Data scientist. You are also another person that that needs to understand what is going on with you. So this is like a science based book to understand yourself and to understand the people around you.

Harpreet Sahota: [00:33:56] Interesting. Is that kind of like neuroscience, brain science type type of thing, too?

Brenda Hali: [00:34:01] Yes. And also, like with choices, we Data like, how do how do we frame happiness? How we can reach happiness.

Harpreet Sahota: [00:34:09] Definitely. I'll definitely check that out and I'll add that to the show notes as well. What's the biggest blunder of biased you've seen or heard of with an algorithm?

Brenda Hali: [00:34:19] Three days ago.

Brenda Hali: [00:34:20] They found that the Instagram algorithm was reporting or blocking the photos of women over size in bikinis just because they were showing more skin. Meaning that if you are overweight, you'll have more skin and the photo has more esteem. And the algorithm, how they optimize it is like if you have more skin expose, it means you don't have clothes on.

Brenda Hali: [00:34:44] And if you don't have clothes on. That's a photo that shouldn't be on Instagram. It's definitely biased in a way that overweight people. They can wear bikinis as well, and they shouldn't be classified as a sport.

Harpreet Sahota: [00:34:55] That's a really interesting one. Yeah, I'll have to check that article out as well. I've never even considered that. And I can, like, just conceptualizing that I could see how the algorithm could make that mistake. That's very interesting. Thank you. If we can somehow get a magic telephone that allowed you to contact 20 year old Brenda, what would you tell her? First, give us some context. 20 years old. What were you up to? Where were you at? What would you tell yourself at that moment in time?

Brenda Hali: [00:35:16] When I was 20 years old, I was studying in Mexico. I was just finishing. I finished when I was 22. So I was in the middle of undergrad. I will tell her, go change her career. It took me eight years, nine years and a different career to change and get to this point. But sometimes I wish I could go back to the beginning and start studying tech in tech.

Brenda Hali: [00:35:37] Start to study something related with technology. So that's one thing that I will tell her. But never is too late.

Harpreet Sahota: [00:35:43] What's the best advice you have ever received?

Brenda Hali: [00:35:46] People care about themselves. They don't care about you. And that is the best advice because people are just focus on themselves. Sometimes there reason why you don't try new things is because you have this fear of being judged. You have this fear of being a failure on doing something that people dislike.

Brenda Hali: [00:36:05] The reality is that people just care about what they're saying, about how they are performing. So if you understand that actually people forget, you see, they just care somehow about their own failures and they don't care about yours and people are kind. You will try more. And you were accomplish more.

Harpreet Sahota: [00:36:23] That's really, really good advice. And I think something people don't really realize is that nobody is thinking about you nearly as much as you think that they're thinking about you. Yes. So that should alleviate your fears. And you're up there giving a presentation or your public speaking, because even though you might be at the center of attention, people are still in their head thinking about themselves, not about you. Great advice. So what motivates you?

Brenda Hali: [00:36:45] I am the oldest in my family, so I am the first woman born my family. In my mom's family and my dad's family. My parents didn't go to college. My. I think just like a couple of uncles went to college. My like no aunt went to college. Maybe just one. For me, it was very important to mark a difference in my family for my siblings. I have two brothers and for my cousins as well, because somehow I knew. I was opening the path for them, seeing them, seeing themselves represented. So something that motivates me a lot. Undoing everything and even sharing all the things that I do is sharing with them and allowing them to know that is possible, allowing them to know and not to listen. A couple of things that they shouldn't be listening from the communication and the dynamic in the family. That's motivates me a lot. And everything that I do, I share. For example, one of my cousins. I push him, I lot a lot a lot to study something related to tech. He right now is in the best university in Mexico, is studying an amazing career in electrical engineering. But he has a software component. There is like robotics is like these hybrid.

Brenda Hali: [00:37:54] When I started my company, I shared with him all the things that he could have done and push him to learn different languages. So that motivates me helping others, though, be them, their best self. Because if I was able to do it, you can do it.

Harpreet Sahota: [00:38:08] So what song is giving you life right now? What song do you have on repeat?

Brenda Hali: [00:38:13] Don't stop me now by Queen. That's my go to song right now in the morning. It's amazing. I love it. I love Queen. I, I. Yeah.

Harpreet Sahota: [00:38:21] I'm a huge fan as well. I like that song. It's awesome. Good choice. Great. So, Brenda, how can people connect with you? Where can they find you?

Brenda Hali: [00:38:29] You can just Google Brenda Hali and connect with me and all the places up here.

Brenda Hali: [00:38:35] I'm on social media. So I'm on Twitter. On Instagram. I have an Instagram that is called that Data pruner. And I just share things related to data science on my career path so you can follow. Follow me there. Just like with me right there.

Harpreet Sahota: [00:38:49] Brenda, thank you so, so much for taking time out of your schedule to be here today. I really, really appreciate you. Thank you.

Brenda Hali: [00:38:55] Thank you. I appreciate that. A lot of good things will come out from this. I am really happy that people like you are giving a voice to people that are not naturally represented. And it was a pleasure just to work with you in all of this process in know the research that you're doing. And I'm just so excited to see what you are going to do next.