Speaker 2: (00:01) I want people to know that if you stick with this and if you keep pushing yourself and you keep trying and you align yourself with people that encourage you, you can thrive in this field. What I can tell you is that data science, these everyone, we need men, we need women, we need people of different colors, background, ethnicities, languages. We need to have everyone in this field. Speaker 3: (00:26) [inaudible] Speaker 3: (00:30) [inaudible] Speaker 1: (00:43) what's up everyone? Thank you so much for tuning in to the artists of data science podcast. My goal with this podcast is to share the stories and journeys of the thought leaders in data science, the artists who are creating value for our field, to the content they're creating, the work they're doing and the positive impact they're having within their organizations, industries, society, in the art of data science as a whole. I can't even begin to express how excited I am that you're joining me today. My name is Harpreet Sahota and I'll be your host as we talk to some of the most amazing people in data science. Today's episode is brought to you by data science screen job. If you're wondering what it takes to break into the field of data science, checkout DSDJ. Co//artists with an S or an invitation to a free webinar where we'll give you tips on how to land your first job in data science. Speaker 1: (01:35) I've also got a free open mastermind Slack community called the artists of data science loft that I encourage everyone listening to join. I'll make myself available to you for questions on all things data science and keep you posted on the fiery through open office hours that I'll be hosting for our community. Check that out@artofdatascienceloft.slack.com community is super important and I'm hoping you guys will join the community. We can keep each other motivated, keep each other in the loop on what's going on with our own journeys so that we can learn, grow and get better together. Let's ride this beat out into another awesome episode and don't forget to subscribe, follow like love rate and review the show. Speaker 3: (02:19) [inaudible]. Speaker 1: (02:32) Our guest today is someone who put out some of the best content I've seen during her hashtag 100 days of code. She got her start in data analysis, Alice's Speaker 1: (02:39) analyzing crime data in S P S. S yes, S P S. S. since then, she found that her passion lies in health informatics. Oh, should I say health informatics down here. She's got a ton of experience in the healthcare industry developing data-driven analytical strategies for companies such as innovative oncology, business solutions, loveliest health systems, respect, and is currently contributing her data science expertise. As an institutional researcher at the university of New Mexico, you may know her from LinkedIn where she crashed some of the most insightful posts sharing her story, journey and data science knowledge and I respect her tremendously for being completely unafraid to call our industry out on the shortcomings. So please help me in welcoming our guest today, the soon to be proud holder of a PhD in biomedical informatics, the future. Dr. Angela Valdez. Angela, thank you so much for taking time out of your schedule to be here with us today. I really appreciate you being so generous with your time. Speaker 2: (03:31) Oh, no problem. I'm happy to have you, uh, interview me. Speaker 1: (03:35) So, so let's talk about your journey and talk about how you went from criminology to data science. Speaker : (03:40) My path to data science was nonlinear. I started as a criminology major. The first data set I analyzed was using SPSS. Uh, and I remember this was in 2007. I remember feeling totally lost. But yeah, the criminology major was actually very heavy on statistics and it was very heavy on like a social theory and crime theory and why people do what they do. So I feel that that gave me a pretty good foundation. And then after a vet degree, I moved into public administration. So the skills start to build on one another. Public administration taught us how to think critically, how to analyze a peer reviewed journals, how to comprehend them. Oh, also presentation skills. So that in that degree program we were, um, encouraged to present. And I remember there was a class in which I was presenting some work that I did and I guess I was mumbling. Speaker 2: (04:38) And you hear that person in the back speak louder. I lost it. From there I started stuttering and lost my train of thought, but it was, it was good practice. So it was something that I needed. Um, and then from there, after my public administration degree, I moved into technology because I wanted to work in technology. I really enjoyed, uh, when I did work in technology, I had an internship at Sandia national laboratories and I found that I like this field. So I decided to switch and I pursued a degree in information technology. And after I graduated 2014, I've been working in analytics ever since. Yeah. The first job that I, uh, started working in, one of the first jobs was health analytics. And from there I went and pursued biomedical informatics. So, uh, that's how I got started in this field. I kind of fell into it, but the skills gradually built on one another. Speaker 1: (05:32) Awesome. To see how you've taken all those skills that you've acquired over the course of your career and really applied it during a fricking a hundred days of code, which I thought was really awesome. Yeah, I've seen a lot of people do this a hundred days of code, but I, I really liked your content. Can you just talk to us about, uh, what inspired you to take on that challenge? Speaker 2: (05:50) This is one of the first times like, uh, to me I kind of viewed it as like a viral challenge. You don't remember the ice ice bucket challenge? Was that what it was called? I viewed this is like, okay, it's the ice bucket challenge but you're actually doing something for yourself. And so I was watching what people were posting about their 100 days of code journey and uh, they were, they were gaining something from it and they continued on and the people that finished seem to, um, I, I just assumed that, well, they, they feel good about themselves. They accomplished something. They've learned something, they challenged theirselves. They push themselves out of that comfort zone. And that's what inspired me to do it because, uh, I was feeling, I was feeling, I had imposter syndrome pretty strongly last year in 2019. Every place that I worked, I found that there was something I didn't know about data science and it bothered me and I was like, you know what, I I think I could do something about it. So I use that coding challenge to do something about how I felt about my skillset. Speaker 1: (06:51) Do you need to find some way to wrestle that kind of fear wrestle the imposter syndrome to push you from behind rather than the standard front of you. And it's really is in that zone wonder where you're feeling that discomfort, where you're feeling the agitation that a lot of the, the growth really happens both, you know, professionally and personally. So it's awesome that you found some way to Hower through that and let that be a positive factor rather than, you know, a derailment. But could you walk me through the process for how you laid out the a hundred days? Did you have it all planned out from the beginning or did you kind of just pick the topic the day of? Speaker 2: (07:24) I had a general sense of what I wanted to focus on beforehand. I knew I wanted to focus on my [inaudible] overall Python coding skills. I wanted to focus on like a general machine learning. And so I, uh, spent some time online and I pick up some courses that I, that I thought were interesting that would, um, help me to gain some of that knowledge. I felt I was lacking. I also did too. You Udacity a nano degrees. So I did the machine learning engineering and a degree and the data scientist manager degree in the myths of like all the books that I purchased and uh, all the courses I signed up for. So I, I had a general list of things that I wanted to study. Speaker 1: (08:02) Oh, that's awesome. To ask you to have a plan to kind of lay it out for yourself and say, okay, cool. Now I know where I'm going. So that's awesome. Speaker 2: (08:08) Yeah, I just felt that if I kind of picked it the day of, I didn't like not having some structure. Speaker 1: (08:14) What day was your favorite? Is there a particular day that stands out? Speaker 2: (08:18) Day 96 stands out to me because at that time things were really starting to form up and it's like, it like came to a head like, wow, I did all this, I learned all this and I actually put together my dissertation proposal and uh, there was something about having that PowerPoint document along with all the writing that I was doing in addition to the classes I was taking. It just felt like, this is what I've done, this, this is the final product. And I just felt just over the moon. Speaker 1: (08:46) That's awesome. I must be good feeling to just make it that far. And you're like, all right, almost to the end Speaker 2: (08:51) that, that and I had something to show for it. That was, that was a great feeling. Speaker 1: (08:55) Sorry. Let's keep it real. Out of the a hundred days of code, was there a one day or one topic that you're putting off for as long as possible? Speaker 2: (09:03) I, I didn't totally understand hyper parameter tuning, uh, that will. So, um, I think I was putting that off because it was, it didn't, I understand theory but it didn't totally make sense how I did it. Like how, what way do I do it? I'm Sarah method to it. And so I, I hated it only because I didn't really understand it. I recently just took another bootcamp, um, by data science dojo. They were in Albuquerque and they explained these things and having someone in class explain it to you and like show you examples of walk you through and answer questions when you're stuff that was really, really helpful for me. Speaker 1: (09:47) Are you an aspiring data scientist struggling to break into the field or then check out dsdj.co//artists to reserve your spot for a free informational webinar on how you can break into the field that's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out. DSDJ.Co//artists Speaker 3: (10:11) [inaudible] Speaker 1: (10:14) how did you stay cage and discipline during that a hundred days? Like, were there days where you're just like, ah, fuck it out, I want to quit. And then how do you power through that feeling? Speaker 2: (10:23) Um, you know, for me, I'm, I'm the kind of person that I hate to start something and never finish. Like it nags at me. I don't feel right with myself. So I've powered through those days in which I really wanted to just give up because one, I felt committed because I was posting this on LinkedIn every day and people were inboxing me, asking me about the challenge. So I felt like I owed it to myself and I owed it to other people that were watching me to finish. Uh, plus I hate being, you know, I hate quitting something before I, you know, halfway through. Plus I knew that once I got through the other side, I'd feel better and I knew I'd get something from it. So I just stuck through it just because of those things. I wanted to prove that I could start something and finish something. Speaker 1: (11:08) There you go, man. I love that. If you don't mind, I wanted to want to know if we can get into a little bit about emotional intelligence. You're bringing this to the forefront of the conversation with a lot of the posts you're doing on LinkedIn. What's your take on, on data scientists needing emotional intelligence? Speaker 2: (11:24) The way I see it is when I started my career, it's always been about the algorithms and what you know technically and how much you can grind away with um, creating great solutions. It's never been brought up to me that there is a great deal of your role that will be dealing with people and that will be working with others. Very rarely will you never interact with people. And I actually found out learning how to work with people. Well that will set you apart. As a data scientist. I feel that we as a community should start also incorporating soft skills because they're not talked about, but they are actually totally necessary for you to continue and to thrive in this field. Speaker 1: (12:06) Yeah, absolutely agree with that. Right? Cause there's always going to be somebody who knows one more algorithm than you. There's always going to be somebody who knows how to optimize a bit of code, just a little bit better than you can. But it's what you bring to the table, you know, from the human side that really is going to create that chasm between you and the competition. Speaker 2: (12:23) That's exactly it. And it didn't occur to me until very recently. Yes, you, you need to have those technical skills. You also need to have, you also need to know how to work with people to, uh, understand how your actions affect others on the team to really, I don't want to say, you know, smooths and kiss let or anything, but you have to understand the human element a little bit more than, you know, than I even realized when I started this field. Speaker 1: (12:52) Yeah. So kind of, kind of piggybacking on that, uh, so I have posted, made recently on LinkedIn, which I absolutely loved about some of the skills this new generation of data scientists are struggling with. Uh, I was wondering if you could talk to us a little bit about that. Speaker 2: (13:05) Yes. So one thing that I've struggled with when I first started was knowing your audience, knowing how to present to that particular audience, knowing when to scale back and knowing when to give them exactly what they want to know. Some audiences just want to know the bottom line, show them their, uh, PowerPoint presentation, make it succinct to get to the point. Some audiences you can go into details about the methods you chose to get to your solution or the algorithms. Some audiences might need an Excel spreadsheet. So I actually, when I started, I had no idea when to say what I also like, here's this paper, it's awesome. Read it. And some people would just aren't interested in that. So you, you have to know when to present what that takes. Some practice honestly takes a little bit of finesse and you'll make mistakes. Also, uh, aligning your skillset with the business. Speaker 2: (14:02) That is like critical. You can have a cool algorithm that does nothing for the business. It adds no business value. So knowing how to bridge that is pretty key. So, uh, one thing I've learned is, you know, sit in some business meetings, do some research on the company, do some research on how they make their money, how they've made their money. Align your skills with those needs. Find out what their pain points are. When I started, like I said, I had no idea. I was like, all right, we're going to just do some cool machine learning. It's going to be awesome. And not everyone's interested in that. If it doesn't do anything for them. Speaker 1: (14:38) Yeah. I always have to tie it back to the bottom line because you know, your job as a data scientists really is to to solve problems, to help the company either make more money or reduce costs. Uh, it's not necessarily just to sit there and look up the coolest algorithms. You know, you're in a research position. But one thing that I liked that you talked about, cause I struggle with that at times too, is when it comes time to present findings, I'm like, why aren't you interested in all the cool math I did behind the scenes? What are you talking about? You don't want to, you don't want, you're not interested in the math. Yeah. So, yeah. Speaker 2: (15:09) Yeah, you can, I've seen people glaze over and so I'm like, Oh no, I've lost them. Um, by that time you're scrambling and you're panicking. And so kind of gauging your audience of before you walk into that room, kind of, you know, develop your presentation around who you know is going to be there that say cause kinda like shattering your confidence shakes. You hail looking at people just like drooling. And I hate that. And so I'm like, Oh God, they don't like it. So just gauging that. Speaker 1: (15:43) That's awesome advice. Yeah. Uh, do you have any tips for networking on LinkedIn that you'd like to share with our listeners? Speaker 2: (15:48) Really what I've done is, um, I have, you know, uh, posted on other people's, um, posts and I've, I've engaged with other data scientists and when I've engaged with people, I'll add them. A lot of the times if you've already engaged with someone, they'll accept your, uh, request, you know, look at what types of things people post. Uh, I like to connect with people that are adding content that's meaningful and useful. Yeah, we're a big community. Just engage and request, you know, connections. That's what I've done. I've, my, my, uh, network has really grown just because of that. Speaker 1: (16:27) F artists check out our free open mastermind Slack channel, the artists of data science loft at art update, eScienceloft.slack.com I'll keep you posted on the biweekly open office hours that I'll be hosting. And it's a great environment and community for all of us to talk all things, data science. Looking forward to seeing you there Speaker 1: (16:53) So do you think that when somebody sends a request to connect, should they include a personalized message? Speaker 2: (16:58) A personalized message is nice. Um, I always appreciate those. I have accepted requests that did not have personalized messages just because I looked at their profile, I saw that they post quite a bit. I see what type of work they've done. I see that they're, obviously, I'm involved in the data science field. So I would say add a message. Having a message will probably, uh, get someone's attention and it will be appreciated. But I don't think it's a guarantee necessarily. Speaker 1: (17:28) Do you have any tips for our listeners on how to present findings and how to develop projects that add business value and address the bottom line? Speaker 2: (17:38) So yes. Um, what I have found is if you're not in business meetings, and I know that a lot of data scientists dread being in meetings all day, that takes away time from developing something. But I would recommend sitting in on some business meetings when possible. I'm also doing research on the company, doing research on their, uh, products, doing research on, um, their history, how they used to make their money. Also finding out what the pain points in your organization are. Like I said, when I started, people would be like, Hey, go and do this thing. And I would just go and do it and not even really think about it. But I encourage you to think about how to help this company make more money, reduce costs, um, aligned resources, excuse me, align resources. And uh, as far as presenting findings, gauge your audience, get an idea of who's going to be in the room. Not everyone wants to hear a lot of details. Sometimes they very to the point, PowerPoint of presentation is necessary depending on who it is that you're talking to. Really, you know, look at that, uh, invite list before you go and develop your presentation around who will be there. Speaker 1: (18:51) I was wondering if you could speak to your experience being a woman in tech, your involvement with, uh, and if you have any advice or words of encouragement for our listeners. Speaker 2: (19:02) What I can tell you is that data science needs everyone. We need men, we need women, we need people of different colors, background, ethnicities, languages. We need to have everyone in this field. Women, I will say that I've, I have had a challenging time, but stick with this. This is such a rewarding field. You may have a hard time initially align yourself with people that will encourage you, cut off toxic connections sometimes. So you may land on a bad company, keep moving. We need everyone and don't feel that you can't contribute just because you're a woman or you know, you are this color or anything. So keep at it. And I think that our strength is in numbers, this, the field of data science. And I think our strength is diversity in thoughts and knowledge and background. Speaker 1: (19:54) How do you find fulfillment outside of work? Speaker 2: (19:57) So I, um, try to have hobbies that I do. I like to exercise. Uh, my husband and I, we hang out. Uh, I really liked to, uh, watch like a cheesy, uh, eighties cartoons. So I, I just, I, when I'm like not at work, I try to just totally just leave work behind and just do something that I love. And sometimes they're mindless activities like watching eighties cartoons. Right now I'm watching X men from the 90s. Speaker 1: (20:27) Love those cartoons. What was other eighties cartoons or Speaker 2: (20:31) so? Yeah, I like, I like he man and the under Katz Speaker 1: (20:36) vendor Katz. Do you ever watch a duck tales? Speaker 2: (20:38) I did watch duck tales. Speaker 1: (20:41) Duck tales . And then shipping Chippendales rescue Rangers. Yeah. Speaker 2: (20:44) Yes. Tailspin, Speaker 1: (20:46) tailspin, dark wing dark. Speaker 1: (20:48) Oh man. All those duck cartoons. They were funny. They had the best jokes. Speaker 1: (20:54) Yeah. So, so you were going to get into our last question here and then jump into the, uh, the lightning round. But what's the one thing you want people to learn from your story? Speaker 2: (21:03) I want people to know that if you stick with this and if you keep pushing yourself and you keep trying and you align yourself with people that encourage you, you can thrive in this field. It may not always be easy and you may feel imposter syndrome, like hardcore. Um, and that will probably be off and on throughout your career as, as this industry just keeps growing and evolving and it's so fast, it's hard to keep up, but stick with it. Uh, it will pay off. And so we are a growing field and we're, hopefully we'll have some, uh, standardization. And, uh, right now it kind of feels like the wild West stick with us and it will pay off. Speaker 1: (21:44) So let's go ahead and jump into our lightning round here. Python or R, Speaker 2: (21:49) I really am on team Python. I do appreciate R but Python, Speaker 1: Favorite algorithm. Speaker 2 I like naive base. Uh, I find it to be, um, easy to interpret, easy to understand, explain to others. It's just one of those algorithms I find it can work very well with textual data. And so I use naive Bayes in my dissertation. I'm always happy with it. So that's like my go to, I'll start there. Speaker 1: (22:19) What's the title or a topic of your dissertation? Speaker 2: (22:22) It is analyzing cardiac medical device failures with a machine learning approach. Speaker 1: (22:27) Ah, nice. Look for that. In your local academic journals folks, do you have a favorite data visualization tool? Speaker 2: (22:35) I love Tablo. I am on team Tablo so hard. I we use it at work. It's, it's intuitive. It's easy to use. They make, if you haven't seen like Tablo public people are just creating the coolest stuff on there. Yeah, yeah. Coronavirus so, yeah. And all everyone's doing that, so, Speaker 1: (22:59) yeah. Speaker 2: (22:59) Yeah. Speaker 1: (23:01) What would you say your data science superpower is? Speaker 2: (23:05) Hmm. I would say that, um, my dated storytelling skills are probably my superpower. I like to walk people through things. I like to just like, I like to build a narrative for you. And you know, so I like, I love storytelling. I feel like once that you could see when that light comes on for people and that's, it's like, yes, they get it. Speaker 1: (23:31) Do you have a favorite data science book? Speaker 2: (23:33) I do. It is a hundred page machine learning book. I love this book. It's easy to understand, well written. So this is not a plug for him, but you know, it's a great book. Speaker 1: So what's the largest data set that you've worked with? Speaker 2: So far, It's been the mod dataset. Um, M a U D and that was for my dissertation and that was 13 million records. Working with that was, was interesting and challenging, but in healthcare, um, and places I've been in the past, the datasets are generally never bet big. Um, and for the university where I'm at now, um, they're also not that big. The populations we deal with are pretty, pretty particular. This falls specific. Speaker 1 (24:15) Where can people find you? How can people connect with you? Speaker 2: (24:19) I am on LinkedIn and LinkedIn, Angela Valdes and uh, also I have a personal webpage. Um, Angelavaldes.com. Yeah. Those are the two best places to reach me. I use my LinkedIn pretty, uh, often, so either place. Speaker 1: (24:37) Awesome. I'll be sure to link that into the show notes. Angela, thank you so much for taking time out of your schedule and being so generous with your time, so much that people can learn from your story. A lot of great tidbits of wisdom in our chat today, so thank you so much. Speaker 2 Thank you. Speaker 3: (24:54) [inaudible].