ashley-scott-2020-08-01.mp3

Ashley Scott: [00:00:00] How can we change the narrative of what a data analyst, data scientist looks like? This is a time when we can help bridge the gender gap and really be part of the conversation, have an interesting perspective to Data make it more meaningful and use it in a very powerful and just way.

Harpreet Sahota: [00:00:34] What's up, everybody? Welcome to the artist's 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 Italy dot com forward. Slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show and leave a five star review.

Harpreet Sahota: [00:01:35] Our guest today is a woman in Data science ambassador. She's earned a bachelor's in public health from the university at Albany and an MBA from Mercy College, which she completed in one year and graduated in the top percentile of her class.

Harpreet Sahota: [00:01:51] She's currently working as a data analyst, collaborating with administrators and medical professionals to develop impactful analysis, utilizing data mining, visualization and modelling to drive business solutions. She's a passionate advocate for educating women regarding Data career opportunities and spreading awareness about the advancement of women in the tech industry. So please help me. In welcoming our guest today, Forbes under 30 scholar Ashley M. Scott. Ashley, thank you so much for taking time out of your schedule to be on the show today. I really, really appreciate you being here.

Ashley Scott: [00:02:27] Thank you. Thank you. I'm so happy to be here and just had the conversation in terms of data analytics, just sharing a little bit more about my story. And hopefully, I can inspire someone else to impact on this journey in their life, whether it's learning more about Data or how to use it in their own business and their job and just get the conversation going. So thank you so much again.

Harpreet Sahota: [00:02:49] Oh, absolutely. It is my pleasure. Speaking about your story. Talk to us about how you first heard of the data analytics world and what drew you to this field.

Ashley Scott: [00:02:58] So a little bit more in terms of how I heard about data science wasn't until business school. So I actually had the opportunity to work on two consulting projects. One of the projects in our business school was where we were able to work as student consultants for a project in terms of providing advice to the tertiary officials in Tanzania, East Africa. So the goal of that project was to create a health care facility that was sort of like a resort, like setting. So if you can imagine just being in another country and really enjoying the luxuries that it has to offer, we would then have the health care facility at a resort where you would have your pulse treatment type of stay. So we had worked actually in terms of the research and marketing department to figure out what are other institutions doing in terms of health care settings and how we can really ensure that patients get an overall successful procedure from start to finish, whether that's being in a country that's not their native language, or maybe to help offset some of the cost, as you can imagine, to do medical procedures here in the United States can be very, very costly. And doing it sometimes out of the United States can actually save you a lot of money.

Ashley Scott: [00:04:17] And we were trying to work in terms of building that first class of very luxurious but efficient top of the top of the line facility there. And another concentration that I had worked on with the consulting company was actually creating a graduate level in the business school. So this was really talking about masters in business administration. But in terms of health care administration and initially when I started a business, that's something that I wanted to get into. So finding out that I could actually use Data to create another graduate-level concentration within the business school would really just put everything together. When you're starting to think about you want to execute as a project and you might not come in in the first part of you might come in the middle. And that was something that happened to me for both of these projects. They started coming towards the end of it. So now that I'm being in this position where I'm learning how Data is really helping us to find out what our competitors did, what we don't want to do, using historical data to figure out how we can improve and meet the demands of the climate and culture that we're living in right now is something that really sparked my interest.

Ashley Scott: [00:05:32] And I kept hearing this word being thrown around Data Data Data. And I'm thinking to myself, what is Data? Me coming from a public health background that was not advocated as much? We would use different terms that dealt with Data more like research. But I didn't know in terms of how data analytics really help with business situations. So it isn't until that I worked in these group settings. I worked in certain projects as well with my classmates and decided, let me just take an intro class to data analytics. And I'm telling you, after that class, I started really turning it in terms of like what type of questions that ask if after I finish this program, what a data analyst asks. And it's so funny because some of the things that we had talked about in our small group sessions, those are questions that I ask all the time in terms of how do you how do you know this is true? Where did you get what's your source in terms of what worked? What did it work? Testing different hypotheses. It's just so funny to see that there was a career waiting, so for me and I didn't even know, so I think it kind of just came into my lap. And those are one of the things that drew me to it.

Ashley Scott: [00:06:44] It was something that was natural to me with something that allowed me to learn. I do believe that I'm a lifelong learning and I constantly like to keep in terms of like trending topics and things like that. So data analytics is something that you learn it at the beginning of the end. You have to keep learning a little bit more each day. So this is something exciting for me. And as soon as I started sharing a little bit more in terms of what I'm doing, I was able to connect with more people. So it was just great in terms of how everything mesh together. And we can really think about how we're improving business decisions based on learning our business industry, learning they're KPI's testing new hypotheses and things like that. So I'm just so excited when I get to hear about the analytics Data science because it's so vital and in a number of industries. But until you start shifting how you ask the question, then you can see this is Data. And I think we've been a lot of companies have been sitting on this information for a long time, but we just didn't know how to use it. So it's full capacity.

Harpreet Sahota: [00:07:50] Absolutely loved the cultivated like the Data science mindset for yourself, because it really is about questioning things like a scientist. It's about creating systems like an engineer or thinking about things in systems like an engineer and then communicating like a business person. So it sounds like you've really done a good job cultivating that that Data science mindset. I think that's going to be really instrumental in taking their career to the next level. And I like it sounds to me like you have a very human-centric approach, a person-centric approach with how you do your work as a data scientist evangelist. I think that's really awesome. You've been in the field for a few years now. How hyped, how much more hyped has it been since you first broke into it? Prior to actually getting into this field,

Ashley Scott: [00:08:34] As I mentioned, I didn't know much about Data science. And when I started doing a little bit more research at one point, the biggest headline that I kept seeing in terms of like Forbes and Business Insider was that it was the sexiest job of the twenty-first century and it seemed very lucrative. They really brought me in and I was actually interested to see, like, OK, well, I majored in managerial analytics at a few courses in my MBA program. So I started thinking to myself, OK, how can I get a part of this? How can I be a part of this? And I think the field of data analytics, data science is still relatively new. I hear it more so as a Business Buzz word, I should say, and it's usually categorized with different skill sets and different job titles. I noticed when I started reaching out to more people and I asked them, what is a data analyst in your job compared to somebody that works in a completely different industry? So I'll give you an example. I work in higher education, so I Data analyst in higher education does not do the same things as a data analyst would do at Facebook. And it's not to say that anyone is better than the other. It's just the needs of the business are different. And sometimes what happens and it can be very confusing is that in terms of getting into it, it becomes a little overhyped because you don't know how to get it.

Ashley Scott: [00:09:55] You don't know. Do you study more data mining, data engineering? How do you prep? How do you get a chance to be knowledgeable of artificial intelligence, machine learning and things like that? And I've noticed that in the midst of the hype. It's great because we're having the conversation. We're understanding Data is vital, but sometimes it's it's hard to differentiate the difference. Right. Because I remember I had a conversation with you in terms of being a data scientist. Right. But to some people, being a data scientist focuses more on machine learning, deep-diving into the to the Data artificial intelligence, where somebody else who works as a data scientist might be thinking about it in terms of marketing, analytics or knowing more about the business structure and how to generate more money. It is hype, and I like that it's hype because now it's something in your face and everyone's talking about it but not everyone has the same definition. That's when we come in and that's when we can really work on the different types of what this role is, what that role is, because as I mentioned before, it's an umbrella. But now we're putting everything together to actually form that that show, that covering.

Harpreet Sahota: [00:11:09] I think you've got a really unique combination of education and experience. Having studied public health and undergrad and then doing analytics for MBA and having worked in the health care industry even concurrently, I believe as long as you work on higher education, you're still kind of in the health care aspect of higher education work for the Albert Einstein College of Medicine. Really interesting and unique background. And I'm wondering. How do you see data analytics impacting the health care industry in the next two to five years?

Ashley Scott: [00:11:38] Right. So that's a really great question because newsflash, everyone, we are experiencing a global pandemic. We're learning how to shape how to adjust and really cope with it. Right.

Ashley Scott: [00:11:52] So in terms of seeing big data being used in health care, I see now in terms of if I want to make an appointment with my physician, the process was that you make the appointment sometimes for do, but you don't see the doctor till three o'clock and you're sitting right next to different people who probably are early or late for their phone or whatever the case is.

Ashley Scott: [00:12:15] Right. So you could imagine there's a lot of foot traffic. Sometimes you can bring your pets, you can bring your children, all of that. And now the Data suggesting that we're pushing now for new innovations, new technologies such as artificial intelligence, working on virtual reality, the VR, so that doctors can understand what's going on with the patient. If they were to do like a virtual type of scenario, if you will, to practice getting that experience to thinking about the training for medical doctors. Right. And now another popular type of technology that's in the works as well is telemedicine. So you can imagine before you'd have to be in the office, you'd have to go through different stages before you see the doctor. Sometimes you wouldn't even see the doctor. Sometimes you would see a nurse practitioner or you would see a physician assistant. Now, you know that if you're going to make the appointment with whoever it is you're at with that session and it really helps you when you think about more vulnerable populations in terms of commuting to actually get to the appointment travel time or taking off time for work and things like that, it might be more of an inconvenience to go to their actual appointment compared to having a certain time that's allocated for your physician. And you'd actually need these are new technologies that are coming up that probably weren't implemented as much. And if you look back in terms of the core curriculum, that's something that a lot of our students weren't really learning too much about. But now that they're doing clinical rotations and they're now thinking about what is the etiquette that they need for telemedicine because as you can imagine, you have this session in your with yourwith your physician and you're honed in on it.

Ashley Scott: [00:13:58] But it's not the same where you're where if I say, hey, my arm is hurting you. Right. You can then touch, you can feel it, there's a bump or something like that. You have to rely on an Internet connection. So to make sure that your cause isn't God forbid, you don't know what's going to happen and things like that. So I think it's a really great opportunity for us to get more data is constantly being generated. There's a quicker turnaround time in terms of getting a response back from your physician and even from getting information for your pharmacy before you would probably have to wait online with the paper script. Right. And send it over to your pharmacist, say, hey, I just need a refill of X, Y and Z. Now is something so simple as you can do it on the app and we might not think about it. Right. But our local pharmacy is taking that information. You can even have a mail-in pharmacy. So as much as it's limiting that interaction in some ways, in the sense that you are more independent and can do it for yourself, there is data being collected. I sometimes get phone calls in terms of, hey, do you need another refill of this medication or such or things like that. So I think there's a lot of opportunities for big data to be collected in health care. And sometimes when we think about it in one area, it actually trickles down to other departments as well, because if one massive change happens, it is all over. And another important thing I wanted to touch on, too, was the electronic medical records.

Ashley Scott: [00:15:24] I remember there was a time if I wanted to get a copy of my bloodwork, I'd have to go to the doctor's office. I would have to put an actual request to say I want my medication or medication. I would want my health record, I need it for school and things like that. It's now simplified to the point where you can actually write your doctor on an online portal. So I used to work at NYU on health and we use a system called my Harp. So with my chart, we were able to write our doctors, attach documents if we needed them to sign it. It was so easy, so much easier. When you think about getting that, getting those things done. And in the midst of anytime we submit things electronically or tell somebody our personal information to verify or authenticate our account, that was something that was used for other departments. And another thing in terms of technology and big data, there is also kiosks that you sometimes have in medical facilities. I don't know if you have it by you guys or we'd actually have a hand scanner and with the hand scanner, it scans just a part of your. To authenticate you so no one else could go to your part, to your appointment, and you can imagine if you had somebody whose insurance isn't high on this person, right. Note we authenticate you. We know this is the person that's data being collected. And some people can argue that this is scary, what is going on with the world. And some people are like, oh, this is so much easier here. This is me. I'm that person. And you just go with the next part of your appointment. So it's really interesting to see how data is being collected and we don't even know it.

Harpreet Sahota: [00:17:04] Very interesting, very insightful vision of the future. So I'm curious, like you mentioned, there's so many opportunities now to generate data, to get data collected as practitioners in the data analytics space, in the data science space. What do you think will be some of our biggest areas of concern as we move towards this vision of the future, where we're getting this really kind of biometrics like precious personal data generated about us as a practitioner? What are some things that we should be mindful of that we should be concerned about?

Ashley Scott: [00:17:33] Right. And now that I'm thinking about it in terms of being a Data mindset, I showed you my palm. Right. But that can be something that harms me in the end because you could take a picture like it is. You have to be very careful with what you show and what you don't. And I think as Data science practitioners, one of the things that we have to really be mindful of is data collection is both beneficial and scary. At the same time, we're really asking people to be open and vulnerable to us to get most of their private information. And even though this, as I had mentioned it is scary, it does open a lot of doors of opportunity in terms of businesses, more jobs.

Ashley Scott: [00:18:16] And if you want to learn about being part of this very robust growing industry, right. We have a choice whether we stay frightened or we use Data for a social good purpose because there's a lot of things that, unfortunately, in terms of Data and artificial intelligence that may benefit one group of people and actually limits another group to getting access to access to health or different types of loans. Right. So if we were to think about having good credit, that's essentially a part of Data. Right. And if we just ask certain questions that unfortunately are targeted to one group of people compared to the other, we then form a type of bias and we don't see how this process is actually debilitating for some people. So I think this is really the part that we really have to hone in and think about some of the big Data concerns in terms of privacy, security and discrimination. We have a very vital role in how that information is getting put out there and how we're actually able to cater to more people to make sure that we're not just doing a quick, easy fix. We're doing a deeper dive, and we're being very intentional in how we're helping other people. That's our superpower. We have the Data with us. But it's a matter of how are we going to use it to protect and serve the people that actually allowed us to do it. And I've been guilty of it before. When I download a new app or I want to join a new social media platform scrolling through the legal part in terms of do you agree that your Data is going to be used over again? I just want to download this app, but it really does take  a bigger person to say, hey, if we're going to develop this program or we are going to offer this type of service, we need to make sure that we do have those privacy settings.

Ashley Scott: [00:20:17] And I think that's what actually implemented companies starting to email their consumers to say, hey, there's been a change in how your data is being used that started that wasn't always implemented. That's something that was still relatively new and letting the consumer know this happened because unfortunately, big companies were getting data breaches. And that can be very worrisome. When you think that this is my local pharmacy, this is my local business. I love this place. I love shopping here. I have my credit card sales on my account. And you want to tell me about my Data can be pretty much utilized and I don't know it. That's very scary. So we play a very big part in trying to reassure people that we understand our customers needs what's important to them, and having algorithms that prioritize fairness and be mindful of the biases that are in Data so that we're not actually applying these algorithms that are traditionally biased towards one group versus the other.

Harpreet Sahota: [00:21:24] So as we move towards this vision of the future that you have with all these types of concerns that you've just described. What do you think will separate the great Data scientists from the merely good ones?

Ashley Scott: [00:21:37] That's a good question. I think what separates great Data scientists merely from good ones are the ones that are starting the conversation, building models that have real implications for justice, for health, and really seeing success and opportunities in people's lives. I think it comes down to we all have a moral obligation and any role that we play because we're considering the ethics of the discipline that we're in and we learn the day to day. But I think what really makes great Data scientists are the ones that are not avoiding the bias. They're the ones that are asking questions. They're the ones that are interested in using Data for bigger good. And it actually impacts people's lives in a very meaningful way. I think it's up to Data scientists to focus more on the intelligence connecting with people and the emotional intelligence. I've mentioned that and some of the soft skills that I've learned over the last few years and that I've seen no real change and real conversations with people, because as much as I am a data analyst in terms of my day job, I play other roles as well. I am a woman and Data science ambassador. I'm also a licensed real estate agent.

Ashley Scott: [00:22:58] I'm also a friend. I'm also a sister. I have many different roles. And it doesn't just take a data scientist to know that we all have a moral obligation to each other. Depending on wherever you see yourself and you anticipate to grow, you need to make sure that you have that mindset, that you know, that you have the ability to do better and not take advantage of trying to benefit one group of people versus the other. It should be equal across the boards and having a diverse group of people. I think that also helps, too, because what may be natural to me may not be natural to somebody else. That's how the conversation is used in a really impactful way because you're able to see what's important to other people. You're able to think about a cultural difference. You're able to think about what may benefit one group of people versus the other, whether it's sexual stigmas that we have in terms of like what benefits women, what benefits males, age groups, things like that. I think once we're able to really dove deep in terms of bringing different groups of people together helps to create better or greater data scientists.

Harpreet Sahota: [00:24:19]  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 this show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register like going to Italy 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: [00:25:03] You are a Forbes under 30 scholar, which is pretty awesome. First one I've ever had on my show.

Ashley Scott: [00:25:08] Thank you.

Harpreet Sahota: [00:25:10] So talk to us about what that means and how did you earn this distinction?

Ashley Scott: [00:25:15] I was actually really excited about joining the Forbes Under 30 Scholars program. That's something that I've actually been following for about two years. I was trying to think, how can I be a part of this in terms of how can I be able to even participate aside from being a scholar? And I know this is somewhat of a relatively new collaboration where Forbes actually partners with the nation's top schools and gives a thousand students a free pass to the summit. So the content really focuses on really getting more students together who have an entrepreneurial spirit mindset. And they also have the opportunity to have one on one or small group sessions with top recruiters for some of the best companies and startups. And in the United States, they also have access to a really great concert. They have access to talk to other like minded individuals and go to different workshops. We also have the opportunity to watch pitch competitions and start on really great talks with real game changers that really tailored.

Ashley Scott: [00:26:32] Discussions from Data finance starting your own business, entrepreneurship, diversity and inclusion, so it was a really great opportunity, an eye opening experience, if you will, to have this opportunity and actually participate in it. So what I did was I filled out the application and wrote a few short answer responses and also tailored it to say this is some of the things that I'm studying in my program and why would it be a great candidate to go? And shortly after I got the invitation and it was just some miles from there.

Harpreet Sahota: [00:27:09] That's awesome. Sounds like a really unique honor and really, really distinct honor to have that kind of opportunity. So congratulations for earning that distinction. That's really awesome. So you've got some awesome experience, as we were mentioning earlier, working in the health care space. I'm curious what makes working in health care so unique, especially as a data analyst?

Ashley Scott: [00:27:32] I appreciate that question because I work in the health care space. Yes. And then the reason I say that is because I work in the medical school. So because I work in the middle school, I have a unique advantage compared to my colleagues that may work directly in the hospital. So some of their concerns might be how do we save the hospital money? How do we know how many people to hire for this department? How many people are collecting their co-pays? Another question there might be is in terms of like hospital beds or patient satisfaction. Now, on the flip side and also how well does this practitioner, this physician, how good is this team? I didn't get that yet. My face is the three stage four physicians are even doctors before the researchers are scientists. We're not thinking how can we make sure when you leave this institution you feel that you were awarded a high quality education. Did you get access to the skills and resources that you need to be a well-rounded physician? Do you have the the alumni group, the connections that you're going to need if you decide you want to do a residency program in surgery, family medicine and things like that? And we also have a unique advantage in terms of assessment and evaluations. So if you can imagine when you back when you were back in school and you finished the course as soon as you finished the course, at least for my school, it was always a few years a survey. So I like this class. What did you like? What did you like? What recommendations do you have? That's Data. And as much as we are not thinking about it is it's so powerful and especially how you ask the question. I remember I had mentioned that earlier because if you asked a very vague question, you were going to get a vague answer. And you want to make sure that you were eliminating bias because you wouldn't ask people to put their names or anything you're more concerned about, like the numbers or getting a sample of students to respond to the to the question that you may have.

Ashley Scott: [00:29:45] And this is really good when you're piloting a course, you're trying out something for the first time because you're now able to get insight on is this even valuable to the students, learn anything or are they taking what they learned in their clinical rotations? Are they seeing some of the things and resonate with them? Does it mean anything? Because these are the conversations that are going to last in their small group sessions and their in their assessments when they're thinking about how to answer questions and when they're dealing with patients and when they have these experiences as well.

Ashley Scott: [00:30:21] So I think that's some of the perks that I have working in higher education, because we're working with people that are optimistic about really changing the world through medicine and without having the knowledge of knowing what they need, we will be able to serve them as efficiently as we need to and also create better doctors, better researchers, better game changers so that we can make I know it sounds perfect, but to make the world a better place or you strive to do that.

Harpreet Sahota: [00:30:54] So how is Data bridging the gap between medical education and patient satisfaction?

Ashley Scott: [00:31:00] One of the ways that medical education is bridging that is through the training and also the evaluations that we give to students. So if they are familiar with some of the core topics that are taught in medical education and improving that, they can then assess situations differently when they actually interact with patients. So if we think about the times that we're asking them questions during their small group sessions or we have opportunities for them. Study abroad and things like that, we're literally taking them out of their comfort zone and putting them in situations where they are the experts and they are the ones that are providing that comfortability, that satisfaction for the patient. So if you have a I, I truly believe if you have a strong foundation, you take that with you. I think we've all been in a situation where we can remember something our our favorite teacher taught us. Our mentor taught us that that's stuck with us. And one of the great things that I have the opportunity to be a part of is the educational foundation. We are as good as we can be. Well, of course, when we self educate ourselves, how we self educate ourselves is through learning, through experiencing certain things.

Ashley Scott: [00:32:18] And when you have the opportunity where you can ask somebody their opinion, you can see somebody do it for yourself and you take a mental note. OK, I want to do this so I decide that I don't want to do this, does it? It's my morals or it doesn't.

Ashley Scott: [00:32:34] That's how we become better. And that's one of the really powerful things that we all have, whether it's in medical education, in health care. We all have similar experiences. It's just utilized in a different way, I think, unless somebody is very curious about this.

Harpreet Sahota: [00:32:51] But do you have any ideas for a project that a Data scientist or Data analyst could undertake specifically within the realm of, let's say, health care or health care, education? And maybe do you have any ideas about where some good open Data health care might be accessible?

Ashley Scott: [00:33:09] Right. So that's actually a really great question. You have a great opportunity to find Data because there are so many free datasets online. I remember when I was doing my capstone project for my business program, I actually talked about medical tourism and how to actually implement medical tourism in Puerto Rico.

Ashley Scott: [00:33:32] And I thought about that because Puerto Rico is one of the poorest here in the United States. So I was thinking in terms of, OK, to use our save money, for the most part, they do speak English. So you're technically going out of the United States, but still in a tropical area, so to speak.

Ashley Scott: [00:33:50] And one of the ways that I got that Data was through the library and that was a free resource that I was able to use. There's also a website, I'm not mistaken, simply analytics as well. There's also databases available through Kaggle get help.

Ashley Scott: [00:34:09] That was really great, too. And sometimes you can actually contact medical facilities and ask them, do you have an open data source that we can use? It might actually give you a sample piece of it. A lot of nonprofit organizations have access to databases as well. So depending on if you share with them, for example, if you are able to connect with somebody and share with them your project what you're interested in learning about and solving one of their business problems, or sometimes they may even tell you, depending on who you actually get in contact with, they might actually give you a sample. And you if you would be able to solve that problem for them and send them the results back. So in a way, you you put yourself out there as a volunteer, but you benefited because you had access to that. Probably another person did not have. And that's something really powerful in the sense that you can, one, build connections, build relationships with people. And you never know if you've helped solve something that took their team a little bit longer because unfortunately, they didn't have the resources. So those are a lot of different ways that you can have access to open data sets and also if you want to do some more like competitions and things like that or look, in terms of, let's say, the institution's website.

Ashley Scott: [00:35:31] Right. And you see an area that actually can be improved or you see that they have a job opening. This is a really great time to get in touch with the recruiter and just mention, hey, this is something that I'm interested in finding out or helping with. And in the midst of applying for the job and making that special connection, you're then able to spark their interest, because if you think about it, everyone else does apply for that job. But you took the time to actually reach out to the recruiter, say, hey, this is something that worth something I'm passionate about. I will love the opportunity to just talk to you a little bit more about what I've learned and get your opinion because you're in that industry. No pressure, but I look forward to hearing from you. Here's my contact information. If you would like to follow up on some of the work that I've been doing. Here is my GitHub. This is my blog post.

Ashley Scott: [00:36:20] And you would be surprised how many people follow up with you and might not just be the recruiter, might be somebody else, because you had started putting out that free information. And in the midst of doing that, I've actually had people tell me, oh yeah, I got this database from this place, maybe we can combined it and this can continue on with your project. So I'm very confident that there's a lot of different ways that you can strategize that works best for you and helps you get the information and get you the experience that you probably wouldn't have gotten had you to spy for the job.

Harpreet Sahota: [00:36:58] Some really good advice and something I didn't even think about that that was possible to reach out to nonprofit organizations and try to get some of their data and help them solve a problem that really helps helps you out in many different ways. First of all, you're doing something good for your community. You're getting to work with some real world Data solving a real world problem. And you could put that on your resume as a bit of actual work experience. So thanks for your insight on that. That's really, really insightful and appreciate that. I know our audience is going to really enjoy hearing that as well. I hope so.

Ashley Scott: [00:37:31] Yeah. Yeah, because as I said, I come from a public health background, so some of the areas that I've worked in were really just sitting on Data. And it's not to say there's anything wrong with the institution or their workflow, but sometimes they just don't have the willpower, not the willpower. They don't have the manpower to help them execute other things, because now is that people are taking on more than the assigned to the fine print in the job description that says an additional task is needed. Those things started piling up. Work environments change just so many different factors. So sometimes in the midst of you taking on more, other things get neglected. But this is where you come in as the aspiring data scientist, assigned Data analyst and offering your help. And it's OK if you reach out and you don't get a response, it's completely fine. I wouldn't take it personally because, as I said, there is other data sources that you can use that are free and you can put it on your website. You can advocate, share with more people, use the power of hashtags and they would engage the audience. That is just right for you.

Ashley Scott: [00:38:44] Just because the person that you wanted to connect with didn't reach out to you doesn't mean that it's a lost completely because something else may have been coming your way that you didn't even see it because you're you're so concentrated and focused on building a data set that is intentional and helps other people that a different set of people actually come your way.

Harpreet Sahota: [00:39:08] So when it comes to careers in the Data space, like there's so many different types of roles. You've that Data analyst, data scientist, machine learning engineer, Data engineer, business intelligence engineer. There's the list goes on and on. How can someone who is new to the space decide what direction it is that they should take?

Ashley Scott: [00:39:28] That was something that I struggle with. I will be completely honest with you. One moment I woke up and I said, I want to be a data analyst.

Ashley Scott: [00:39:36] Next thing I know, I read a job description and I said, I want to be a data engineer.

Ashley Scott: [00:39:40] So it really can be a challenging part in your career when you're trying to convince yourself first that I'm not going crazy. And then the second part is that this is the role for you when I started to do was look at the job descriptions a little bit more and think about is this something that I want to do? Because I notice that I mentioned to you before a data analyst and one role is different in a data analyst at another company. And I think that's what enables people to apply only for data analyst roles is because one job looks so appealing to them in one industry. That doesn't mean it's going to be the same for somebody else. So one of my recommendations is someone was new to this and trying to figure out what to do, take some free online courses. There are a number of courses that are available through you dummy courses. There's certification programs that you can do as well for a few hundred dollars depending on your budget. And I would really take the time to invest to understand what these programs are teaching you. And is that something that you're interested in? In addition to looking at the job descriptions and being real with yourself? One of the things that I've advocated is it's OK to change your mind in your Data career because a lot of people don't know what Data is anyway. And you don't need to worry about, oh my God, I want to be a data scientist. I want to be a data engineer. It's OK. We're all trying to figure it out. Just not a lot of people are talking about.

Harpreet Sahota: [00:41:15] We've talked about which pill we should narrow down. But how about when it comes to industry? You mentioned that a data analyst on Facebook is different than a data analyst and like education. Right. So how can we narrow down a particular industry to. Target, which direction we want to go?

Ashley Scott: [00:41:33] Think it all comes down to a self check.

Ashley Scott: [00:41:36] And think about the type of work ethic that you have and that you're willing to enter into your life, because if you're working in a Data role in an industry that pretty much does not sleep, you have to be OK with getting random calls, getting emails and having deadlines that are a little bit more challenging compared to someone. That's where canidate analysts feel where their job stops at 5:00 and they will continue when they come to work the next day.

Ashley Scott: [00:42:10] So I think it really depends on, as I said, having a reality check with yourself. And also I think this is a great opportunity to really ask the company as well. You can ask the recruiter that is talking about their job on their LinkedIn. Can you tell me a little bit more in terms of how involved, how involved this job? Is this a space industry? Is this something that gives me mentorship on the job training and things like that? The recruiters are there for a reason. They want to recruit people. So if they know that you have an expressed interest, they have the opportunity to access the hiring manager. Hey, these are some of the questions that you're getting in the job description, whether they can change the job description or not. But at least you've opened the conversation and then you can start asking what is it that they want from you so that, you know, when you're applying for jobs in this specific industry and you keep getting the same answer, you know, OK, this works from my lifestyle or does it? I think that's really important. And that's an underutilized tool, that or skill that people are not using sometimes. And it's no fault of their own. I think sometimes we get scared for writing the recruiter. We get scared in terms of, like rejection or we'll be right back. But as a recruiter job, they need to recruit people. So get in. So the more that they can help you narrow down, this is the career for you or not, this helps them, too, because they don't have to go through your resume if you are not interested in this job, or they can help communicate that information to the hiring manager to be more direct so they can get the applicant that they are looking for, for that role and help you actually go into the field that serves you the best and caters to your goals, your lifestyle and other commitments that you have.

Harpreet Sahota: [00:43:57] Thanks for sharing. That's valuable information. I know that the audience is going to take a lot away from that. I want to switch gears a little bit, get into emotional intelligence. I recall seeing a presentation believe presentation or a slide that you did on emotional intelligence. I love that you're bringing this to the forefront of the conversation. What is your take on Data professionals needing emotional intelligence and how can we develop these skills?

Ashley Scott: [00:44:24] My take on data scientists meeting emotional intelligence is critical. I think when it comes down to working on projects as much as it's an independent project that you may be working on. It also depends on your team and it depends on when you're working with people. If you're comfortable asking them, can you help me? Can you give me some insight on this? Because you may or may not have ever seen it before. And you know that at least when you take that information to your manager or send it over to your stakeholders through digital format or visualization, you know that it was great quality because it stemmed from getting more insight, having that time to ask people, hey, what are your thoughts about this, making them feel included, asking different groups of people. And I notice, too, sometimes when we think about Data, as much as we're collecting data at a very quick rate, we have deadlines to be met. Right. So if you know that you need something from somebody in a different department, you do not work with them. You probably see them in the hallway and you're thinking to yourself, hmm, this person never writes me back on my emails.

Ashley Scott: [00:45:36] Right. But I know their assistant.

Ashley Scott: [00:45:39] I am able to then ask the assistant if, you know, your boss is coming back in. I need a little help. I'm in a jam. And then you just never know if that helps to expedite the process a little bit more so that you can make sure that you're meeting that deadline. Or sometimes you have to reach out to your manager and say, hey, I'm in a bind right now. I have what I need, but I'm missing parts of it. And you can only do your job when you have Data, right? Because it's up to us to clean it and to really make sense of what it means. But if you don't have the Data, you're kind of stuck. So having that emotional intelligence is really important because it helps to make your job a little bit easier. And I see that because this is a this is an industry that's changing rapidly. And if you don't have an outlet, you don't have an opportunity to contact reach out to people to help you develop these skills in terms of. Communication in terms of time management, in terms of research that's going to show when you speak to your stakeholders and you're telling them through their through your visuals, this is what you need to do. This is what you need to do. It's hard when you're coming from and from a place where the stakeholder has been doing certain things for several years, maybe the start of the company.

Ashley Scott: [00:46:55] And you're coming in and you're shifting the whole perception of what they need to do or even saying what they're doing is wrong, but not as bluntly as I'm saying to you now. Right. So having that emotional intelligence engages whether people will be receptive to what you're saying, because having those soft skills also goes into storytelling. Can you go into a room with people who don't know what you're talking about, who don't know what code it is, who don't know your day to day and make it make sense to them? That's a skill, and it's not something that comes up overnight. It's something that has to be practiced. And when you are able to have this human component to it, you're able to connect more with what scares them and how you can help them not feel huskier, help them reassure them that their business is not going to crumble. And everything that they've ever dreamed of is not pretty much going to waste because you're going to be able to use the data to say, hey, this is what we found. This is what we are recommending you to do. And we take the next steps. As much as we try to give each other a reality check sometimes is hard.

Harpreet Sahota: [00:48:01] So you talked about data visualization and storytelling. I just wonder if we could dig into that a little bit. So what makes a good data visualization and what are some common mistakes that you see people make when visualizing their data?

Ashley Scott: [00:48:15] I think what makes good data visualization is knowing what's important to your company, what's your company's values and mission and what they're looking at, because you can make a beautiful visualization. But if it doesn't answer any of their questions that they've had, if it does engage conversation, what did you really do? And that can be a hard pill to swallow. When you think about the amount of work that it took you to get to this point. It's not that your visualization is bad, per say, but it's not as useful to whoever you're showing it to. I think some common mistakes people make when they're making visualizations. I've seen consistency is off. The way that they present the data is not to them. Your audience gets bored. They're not excited. They don't see consistent colors. Right. There's art is a blue. Sometimes it's just better to stick with your company's colours or keep it to like two to three common mistakes. I see people make that they have the data, but they can't explain what it means. It's one thing going from like a spreadsheet or whole screen of code to then just show numbers to people or to show a visual that has too much information.

Ashley Scott: [00:49:30] And I've heard good things and I've heard bad things about using pie charts and visualizations. Some people like no, do not use a pie chart. It's it's misleading sometimes because how you may orient it may look like it's favoring one one area compared to the other. Sometimes the visualizations have too much details where you can't process what's going on. I think those are some common mistakes that people make when they're making visual. But the bright side is when you make visuals, you can then ask people outside of your tribe, spend five minutes with you depending if you are allowed to share their Data or so. But just your technique, their style, are you engaging? Does it make sense to people when you tell them about the information that you found on your visual? Because that can go a long way because you only have a few minutes to capture the audience and show them that you're an expert in the Data that you were working with, and you want to make sure that you're able to hone in on those skills and you're able to answer their questions effectively and give really great recommendations to move forward with.

Harpreet Sahota: [00:50:37] I've definitely been guilty of that in my career, creating dashboards that really look cool, that do a lot of things, but at the end of the day weren't useful at all. And that was because I didn't talk to the stakeholders. I didn't get it tested by other people. I just wasn't understanding what their needs were, just create something that looked cool to me, not useful to them. But talk to us about if we find ourselves like let's say the scientist finds themselves in a room full of executives, they need to communicate their Data. And you mentioned sometimes people might be guilty of not knowing what their Data means or guilty of not being able to explain what they did in a non-technical kind of manner. They do you have any tips for for Data scientists who find themselves in those situations?

Ashley Scott: [00:51:26] Right. I think one of the recommendations I would have. Is to practice and practice in terms of looking at your Data and even if you didn't work on it from start to finish, be a part of that process, be a part of the conversation when you get Data, don't just accept it with a blind eye.

Ashley Scott: [00:51:48] I recommend you look to see, OK, this compares with that like it makes sense to you. You can map it together because I can I can give anyone with this spreadsheet that has, for example, think of a sample in higher education, that has a class name, that has the grades, that has the teacher's name. Right. But are these scores consistent of quizzes? Are these exams? Are these assessed? Like what kind of assessments are these?

Ashley Scott: [00:52:20] And once you're able to check yourself, like, what did I get? What is this? Data mean, right. Then you can think, all right, what was the question? I was asked if what was my goal? What was I supposed to figure out? Because as I said, you can have all the data, but it's not helpful for you. So once you're able to think more in terms of what is required and asking specific questions, then and even so, when you you take that data back with you, when something comes up, it's OK to ask, because what's happening is now you're building that mindset to be an investigator, to solve the problem that everybody's trying to figure out the answer to. And as I said, you can get a lot of data, but it doesn't mean that it's useful. So one of the things that I would also recommend is just thinking about honing in on.

Ashley Scott: [00:53:12] Sometimes you've got to keep it simple, keep it simple, because sometimes when you try something out that you're not confident in using, you can stumble. You can then second guess yourself in the middle of actually presenting your data, whereas somebody if they were to ask you what's your name, you can confidently say first middle, last name, birthday date, because you know that you own that you're knowledgeable about it. But if somebody asks you about something that you're not too familiar with, your voice kind of goes a little down, at least for me. My voice gets a little bit lower, not as confident I can embrace it. And that's why, yes, you can try new things. And I'm not encouraging that you don't. But make sure when it comes down to the final stage, you're able to be confident in what you're saying. And you can also ask during the presentation. If you're interested. I can show you a different perspective or I've been working on something else so that they know it's in the works and you can do to see if this is even valuable to them.

Harpreet Sahota: [00:54:17] What tips do you have for data scientists or any Data professional that's on a team environment? And let's say they're scared of looking like they don't know something. They don't want to openly communicate that with their team mates. What would you say to somebody who's in that mindset?

Ashley Scott: [00:54:33] I would actually see if it's possible to ax colleagues who are Data scientists, but not in your role, because I think there's a way of communicating that you're unsure or you're not knowledgeable about something, but phrase it in a different way. So if you have and what's worked for me is like having a Data science mentor, a coach, these are the people that you can access and feel that you can be are open, honest self, because as as we've seen in the workplace, it's not always going to be an opportunity where you can say, oh, I don't know, because it's excited for you to know. But when you have somebody in your corner that's been in the game a little bit longer than you, you can have these conversations. You can go play on how the answer will be. And you never know if that person can even help you. Right. So that is something that I would really recommend, is just seeking out those relationships with people. And I don't see that so that it can be one sided. I see it so that you can also offer insight to them, tell them what their workload and have the conversation and also make it meaningful. I think when you're able to do things like that and take it back with you and you share that, it's nice. It's nice to hear that somebody is insight had helped spark a conversation or solve an issue that they probably didn't even know that that information was sitting under them this whole time. And this is where you're able to get out of your comfort zone. Remember, we had a conversation earlier. If it doesn't scare you, right, you're not getting better. You condition your mind. You condition your soul so that you can break these challenges, because every day we're facing something new, but we don't have to go it alone. And I think that's really key.

Harpreet Sahota: [00:56:26] It's excellent advice. I absolutely love that. So I was wondering, can you speak to. Being a woman in Data, and if you have any words of encouragement for the women in our audience who are breaking into or currently in the Data world, I would just tell them, congratulations on even considering this.

Ashley Scott: [00:56:47] This is a really great time to be part of Data. This is a really great time to reach out for help, because a lot of us have questions. And as I mentioned before, a lot of us have questions and we're already in the industry and we're just trying to think, what is the next step? How can we be better? How can we change the narrative of what a data analyst, data scientist looks like and what are their roles? This is a time when we can help bridge the gender gap and really be part of the conversation. Have an interesting perspective to Data make it more meaningful and use it in a very powerful and justly way. And one of the recommendations I would have for them is to actually reach out to people in the field. There's really a really great set of resources that are available. One of the organizations that I'm a part of is Women and Data, and I've done a few talks with them as well. And one of the perks that they have is their mentorship program, where they can actually help you with careers by attending events, getting access to resources, class settings, as well as like a group setting where we would learn how to code together and building those relationships.

Ashley Scott: [00:58:01] I feel like when you have a team with you, you feel confident. You move in a way that allows you to be assertive and be able to say, I can do this because there is going to be days where you don't feel that there's going to be days where you're sitting at a computer screen and you keep saying you keep the area because of your data set and or you're trying to do a visualization.

Ashley Scott: [00:58:26] And everything is not singing when you put on a filter. Right. But these are the times when you reach out to your community and you ask them, hey, Is there a way that I can automate this? Is there a way that I can share my journey? And there's a lot of great resources online, like medium dotcom and GitHub, where people actually share some of the challenges that they face. And this is a great opportunity to reach out to them to say, hey, I would love to connect with you or learn a little bit more about your journey, this project that you're working on.

Ashley Scott: [00:58:54] Maybe you guys can collaborate, you can possibly do a competition and things like that. And these are really great things to list on your resume as well, because you're doing the Data work. But just in a different way. You may not be doing it in your day job, but you still have that mindset.

Ashley Scott: [00:59:08] And that can really take you a very, very long way. 

Harpreet Sahota: [00:59:12] Talk to us a little bit more about the women in Data organization. So does this organization, is it nationwide or are there specific chapters and specific cities? How is it structured?

Harpreet Sahota: [00:59:21] So it's actually a nonprofit organization. They have a few international chapters, but it's based in the United States.

Ashley Scott: [00:59:29] And that group, the community of women, have really been very helpful, very sincere in how they're able to connect with other people who are actually in the field or what we would call as Data newbies. And I'm very passionate about this organization because I just started sharing a little bit about my Data journey and I put it on LinkedIn Océane pictures.

Ashley Scott: [00:59:53] And they actually reached out to me and said, hey, you know, we're doing a webinar series, we're doing a talking conference as well. And we think that you would be a great fit. And I thought to myself, me, no, no, no, no, I'm not. If you didn't check my profile, I don't have a computer science major. I don't come from a very robust industry in terms of data analytics. No, no, no, no. You're just the right type. And as I said, having this group of people backing up, supporting you, giving you resources really enabled me to feel confident and feel secure in saying like, hey, this is my journey.

Ashley Scott: [01:00:33] These are the challenges that I'm facing. These are the things that I've encountered. And if you feel like I'm happy to connect and we learn together and I'm actually in the short amount of time of me being in the organization, I've actually expanded my network and shared similar experiences with women not only in the United States but in Africa and Ireland and Russia have been able to connect with so many different women that I probably would have never been able to communicate with them had I had not shared what I was going through.

Ashley Scott: [01:01:05] And that's why I decided to start a social media campaign where I do a lot of talks about Data and I call myself the Data girl, more specifically Data girl Ash, just so I can help build the conversation and really help people understand that it's not just a woman thing, it's not just a child thing. This is a everybody thing. We all are.

Ashley Scott: [01:01:30] Unique in our own way, we all have a Data set, right, that's unique to us, so we can all contribute to how Data is being used and really building that community together so that we don't feel threatened or we don't feel discouraged actually apply to it.

Harpreet Sahota: [01:01:46] And is membership to this organization free and open? And if anybody in our audience wanted to join this organization, how would they go about doing that?

Ashley Scott: [01:01:55] So women in Data is actually a nonprofit organization. So we do have some memberships that are as low, I think, as maybe for twelve dollars a month. But if you'd like, you can also join their YouTube channel. We have a really great set of webinar series as well that talk about how to use data.

Ashley Scott: [01:02:14] We've also shared in terms of people's stories on how they got into Data careers, the resources that they can use as well. Their website also gives more information in terms of the different memberships.

Ashley Scott: [01:02:24] But even if you didn't want to pay to be part of the Women and Data organization, we do have a lot of free resources available. And you can join our mailing list as well. And you can also get in touch with some of our chapter leaders as well. And another organization that I'm a part of is Women in Data Science in Association at Stanford University. So what happens with that organization as well is that we're able to host sessions virtually and in-person in our specific city and then talk about specific Data, science related career opportunities and trending topics that's going on right now as well.

Harpreet Sahota: [01:03:05] And what's the impact that you hope to have with your involvement in these organizations?

Ashley Scott: [01:03:11] My hope is that I can help inspire people and help to motivate someone who may not feel as confident or unsure about the Data field and really think about how they can contribute to it. I know that's something that I faced in my career because I had start off with public health knowledge and then honing in on business and also being a licensed real estate agent. And I did know the commonality among those industries was Data because a simple example would be your health really can impact where you live or certain resources that you may have in your area and also the business mindset of what's available to you and those resources. And Data helps to support all of those initiatives. That's why we have specialties such as community health, population, health industries as well. And sometimes it even goes to say, like where you live actually shows you how healthy you are. You can imagine that if you live in an area that is underdeveloped, you would have certain health care conditions compared to people that live in more developed nations and countries. And how do we know that the data supports it? So in another way we're able to think about that is when we think about like our real estate and how we're able to get access to live in certain areas, or if we think about the whole process of buying a home. Right. There is data that supports that. Some people felt that they were not capable of getting a loan or mortgage loan because of other underlying factors. But we wouldn't know that if we didn't take a look at the data and see, OK, what type of communities live here. And what I mean by communities is the type of income level that the median salary of people in this community or in buildings or establishments that are around their area compared to a more suburban or a more industry or a city like area.

Harpreet Sahota: [01:05:26] So what can the Data community do to foster the inclusion of women in Data science? And I I guess if you could speak specifically to the dudes out there the day to do that, they're like, what can we do to to make sure that our sisters and Data feel like they belong in our welcome in this space along with us?

Ashley Scott: [01:05:48] That's a good question. I think.

Ashley Scott: [01:05:52] It stems down to the emotional intelligence part where somebody would feel comfortable asking specifically like women  or asking for their involvement right now, making them feel that this is a new type of industry because it is male-dominated. If we look at some of the higher executives and really big technology firms and corporations that are generating a lot of money, there's not a lot of women there, unfortunately. And this is a time when we're starting to. Have opportunities where women are getting there, and I think it is a work in progress, but I think it takes time for people to understand that we bring value and be willing to accept a different perspective. I think that's where it stems from. And also, I think it comes down to like bringing diversity in the group. I wouldn't want somebody to have it forced upon them. I would want them to be open to it because people can tell if it's genuine or not and you are able to gauge and see what the environment is like. And if you feel comfortable, because a lot of times people say no, the great opportunity is not because they were incapable, but they didn't feel comfortable. So once people start to feel like, OK, I can take baby steps and welcome us in, then that really helps. But unfortunately, they have a closed mindset and not thinking about wanting to bring women in as it gets a little hard. 

Harpreet Sahota: [01:07:42] We love that message, It's a great message for for us out there.

Harpreet Sahota: [01:07:45] So last formal question here before you jump into a really quick lightning round, and that is what's the one thing you want people to learn from your story?

Ashley Scott: [01:07:58] I think one thing I want people to learn from my story is it's OK to change your direction. I say that because I come from a public health background, come from business. I come from the same background. But before all of that, I was a lab researcher. I worked at the Department of Health and I was doing research in a microbiology lab. I worked on a research paper. We got published. And in that mine, in that time of my life, I was confident I was going to be a scientist. Life happens. People come into your lives, you learn certain things, you adapt and you find that you may like other things. And that's OK. I want people to own their story.

Ashley Scott: [01:08:45] It's OK that you're not traditional, that you're a superpower. That's something that you put in your cover letter that nobody else can put. That's something that's in your bio that no one can take away from you. And I think that's a very powerful.

Harpreet Sahota: [01:09:00] I agree. That's absolutely amazing. I love that. So let's jump into a lightning round here.

Harpreet Sahota: [01:09:06] So first question, if you could meet any historical figure, who would it be and what would you ask them?

Ashley Scott: [01:09:15] I think I would love the opportunity to see our former first lady. This is Michelle Obama.

Ashley Scott: [01:09:26] And I say that because after seeing how beautiful she was to  our conversation in terms of seeing how she really embraced a lot of challenges that came her way, she was really able to show us it can be done and it will be done. And one of the things that I would ask her is what is something that she would have changed in her career, knowing what she knows now? What is something that she would have changed? And I see that because I watched her Netflix documentary recently.

Ashley Scott: [01:10:05] I know that she comes from a background of law. And I'm just curious in terms of knowing what she knows now and how she's taken the direction of her career and is owning her presence and her power. I'm just curious in terms of what would she have done differently, knowing, you know, the potential that she had to be the phenomenal woman that she is today?

Harpreet Sahota: [01:10:27] What do you believe that other people think is crazy?

Ashley Scott: [01:10:32] I believe it's OK to ask questions. I think sometimes people take things for face value and sometimes it comes off is that they're being nosy or they're not adding to the conversation. But I think questions are a great way to find out what the underlying meaning of something is and to really dig in terms of why that is a question and not a statement.

Harpreet Sahota: [01:11:00] If you could have a billboard put up anywhere, what would you put on it?

Ashley Scott: [01:11:05] I think if I was to have a billboard anywhere, I would put something powerful, something that says being you is your superpower.

Ashley Scott: [01:11:16] And I say that and I believe it so much is sometimes I think we get caught up in social media and we compare ourselves to what we think we should be or be destined to be. And we might not be there yet. And it just really goes to show that you're describing discrediting yourself when you compare yourself to others. You're fabulous, you're unique, you're special.

Ashley Scott: [01:11:40] You bring something that nobody else does. And once we start thinking about ourselves in that manner and not to be arrogant or anything like that, but just to be OK in your skin, I think that is something that is an ongoing process. Some days are better than others, but it's something that we can all implement.

Harpreet Sahota: [01:12:00] So what do you love most about being in the Data world?

Ashley Scott: [01:12:05] One of the things that I love most about being in this role is that every day is not the same. And I own that because the conversations I have probably a year ago and knowing the challenges that's going on in our world right now shows that there's room for improvement. There is ways for us to stay on our toes. There's an opportunity for us to continue learning and get insight from other people and really break the mold of what we used to do and change it up. I think that's one of the great things that I love about being in the Data field and knowing that my work can impact really great decisions, help businesses accomplish their goals and also build conversations as well. Because something that is groundbreaking to some industries is something that honestly can then be implemented in the future. And that's how we get better.

Harpreet Sahota: [01:12:58] What do you wish you had known when you started out?

Ashley Scott: [01:13:03] That's a good question. What I wish I knew is that there's different types of people, different types of career paths that you can be in, in the Data path in the Data career. I see that because I was very confused when I first started and I didn't know which opportunity would be most beneficial to me.

Ashley Scott: [01:13:27] And I think when I found out, like there is a Data analyst, there's a business, Aliceville, there's a financial analyst for all of these things are analyzing. Right. But which one do I want to be a part of and how do I know how to get to my point? B I think that was something that I wish I would have known starting out. But I'm glad to know that people also had that problem. And now there's like quizzes online that people can actually date and help them expedite. The process is a lot better.

Harpreet Sahota: [01:13:57] So what's something that you're curious about right now?

Ashley Scott: [01:14:00] One of the things I'm curious about is how is Data going to be viewed in another five years? Because right now  it's the sexiest job, right? How are people going to see another five years, another 10 years or another 20 years? Is it still going to be that or is it going to be a thing of the past? Not a thing of the past, but not as glamorized, because I remember I was trying to think when I was in school, like what was the most glamorized job at the time? And I don't remember Data being one of the trendsetters. So I'm just curious if this is really going to be something that people are really appreciative of or how  they really nurture the Data field.

Harpreet Sahota: [01:14:52] What is an academic topic or just a subject that you currently like outside of Data science that you currently are really into?

Ashley Scott: [01:15:02] One of the academic topics that was really interesting to me outside of Data science was taking communication classes. So when I was an undergrad, I actually minored in rhetoric and communication and I didn't know in terms of having the ability to speak to people and speak to them in a way that allows them to feel what you're feeling, to be passionate, to be genuine.

Ashley Scott: [01:15:31] That's something that across the board, I think when you're able to connect with people with words and actions that shows and communication can be nonverbal communication. And that's why I say it can be for your actions and also stemming from communication linguistics.

Ashley Scott: [01:15:49] I think that's very powerful, too, because when you're able to have the opportunity to speak to someone who knows your native language when you can connect with people who don't come from the same background as you and you can pick up on their language, you could pick up on their culture, that that is something that I'm so glad that I have the opportunity to do, because our world is  our reality is so much smaller than we really think it is. Like I'm based out of the New York City area. And for me,this is everything. Or if I was to go around to another country, another state,  this is not their normal. This is very different. So I think that's something that still is very important to me and something that I pride myself on when it comes to traveling in and doing the things.

Harpreet Sahota: [01:16:44] How about the number one book, fiction or nonfiction or maybe one one of each that you would recommend our audience read.

Harpreet Sahota: [01:16:51] And what was your most impactful takeaway from it?

Ashley Scott: [01:16:55] Well, one of the number one books, and I'll speak it into existence, that I will have my own book one day, but I can't talk about that yet. It's still in my mind.

Ashley Scott: [01:17:05] But one of my favourite books was The Spirit Catches You and You Fall Down. And this was a really great book that I had read. I actually did a summer program when I was in high school at Cornell University. Why did I decide to go and take college credits as a high school student? I cannot answer that for you, but I would strongly recommend  one of these types of books is because it talked about some of the struggles that a non-Western family had experienced in terms of trying to do a traditional how I see this in terms of trying to learn how to adapt to Western culture, medicine and also being mindful in terms of certain things that work in cultures, in terms of not using medicine or probably praying or certain rituals, is not always advocated in American culture. When it comes to medicine, sometimes doctors may recommend, hey, to save this, to take this prescription and things like that. And this book really opened my mind to say not everybody is actually interested in this type of healing. There's different ways that people decide to heal that work for them. And this was really a great moment for me to learn that there's different perspectives. And it's nice to see that that person's wrong. They just have a different way of doing something.

Harpreet Sahota: [01:18:34] What song do you currently have on Repeat?

Ashley Scott: [01:18:36] Oh, one of the songs I have on Rupi. That's a good one. I think I have a listen to music in too long, so I'm trying to think I don't have a current song on my playlist, but if I could throw it back I have to throw back one of my songs would be. So I should have thought about this a little bit more.

Ashley Scott: [01:19:02] But I'm trying to think like what is my favorite song right now. Brown skinned girls by Beyonce.

Harpreet Sahota: [01:19:11] Ashley, how could people. Back with you, where could they find you online?

Ashley Scott: [01:19:14] Yeah, so I am available on LinkedIn as Ashley M, Scott and I'm also available on Twitter and Instagram AIs Data girl Ash and I will be launching my official website, Data Grosh dot com within the next month or so.

Ashley Scott: [01:19:33] By the time our viewers get to see this and it will be up and running. And just to show my passion and really my excitement for Data and girls into the Data field, I'm interested in having merchandise that's related to women to advocate what is the importance of Data careers and how they can get involved, like making sure it's like this and hoping that I can really show people it's OK to be in Data, it's OK to be this Data girl. This is OK. And it's a powerful statement and it's a way for us to change what's going on in our culture and be part of the new faces and really to be the leaders in these in these type of industries. And in terms of moving forward, if anybody has any questions, you know, I'm most available on LinkedIn and the social media platforms. So these are some of the things that I'm hoping that I can get in contact with more people and hopefully connect. And you never know. We'll probably be able to go to a conference or have participants come together and we can be part of Data and other organizations as well.

Harpreet Sahota: [01:20:47] Definitely. I'll be sure to include the links to all those in the show notes. And once the website is up, I'll definitely put links to the Data grassroots that you've kubernetes.

Ashley Scott: [01:20:56] Well, I appreciate that. Thank you again. Sorry for just inviting me. It's so great to know that the power of the hello and is reaching out on LinkedIn got us to this point today. So thank you for that. I'm really looking forward to getting in touch with you.

Harpreet Sahota: [01:21:14] Definitely. Thank you so much for taking time out of your schedule to come on the show and be here today. Appreciate you stopping by.

Ashley Scott: [01:21:20] No problem. Have a good rest of your day.