SDS PODCAST EPISODE 25 WITH KIMBERLY DEAS · Kirill: This is episode number 25, with Data Analyst...

Show Notes: http://www.superdatascience.com/25 1

SDS PODCAST EPISODE 25

WITH KIMBERLY DEAS


Kirill: This is episode number 25, with Data Analyst Kimberly Deas.

(background music plays)

Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.


Hello and welcome to the SuperDataScience podcast. Super excited to see you here once again, and today we've got an interesting guest. Today we've got Kimberley Deas joining us. And Kimberly is using data science for something very, very noble. Kimberly is a data analyst in the space of medical research, and she uses data science to help provide healthcare and support services for individuals living with HIV and AIDS. So a very noble cause, as you can imagine. And in this podcast, we're going to discuss some very interesting topics relating to things that Kimberly has encountered during her profession.

So for example, we're going to talk about how Kimberly transitioned from using standard academic approaches and philosophy to using data science in her day to day role to help people and fast track her research. Also, we're going to discuss how Kimberly uses two tools. She uses R for the analytics, and Tableau for the presentation. Which I found to be a very interesting discussion, because a lot of the time, people get side tracked with just one of the tools. So either people use Tableau for everything, or people use R for


everything, including visualization. Whereas Kimberly has found that sweet spot of doing the analytics in R and then passing on the results to Tableau and creating amazing reports for management and for the presentations that she does using Tableau.

So that's going to be a very interesting discussion. Also, we're going to talk about women in science, technology, engineering, and mathematics. And Kimberly is going to share some of her experience, how she created a career in this space. Also, she will provide some tips and advice for women out there who are looking to get into the field of STEM or how those who are looking to propel their STEM careers going forward.

And of course, there's going to be lots and lots more value in this podcast, and I can't wait for you to check it out. Without further ado, I bring to you Kimberly Deas.


Hello everybody, and welcome to the SuperDataScience podcast. Today I’ve got a very interesting guest, Kimberly Deas, who is a data analyst at the UT Southwestern Medical Center at Dallas. Hi Kimberly, how are you today?

Kimberly: I'm great, how are you Kirill?

Kirill: I'm good, thank you very much. How's the weather there in Dallas, Texas?

Kimberly: It is finally starting to get cold so I’m very happy, because it was humid and hot up until maybe a week or two ago. I’m actually kidding, but it takes a while to actually get cool here in Dallas.


Kirill: Yeah, I can imagine. How cold does it get, like the coldest in winter? Does it snow there?

Kimberly: You know, I think they get a lot of ice in the winter time. This is actually my first winter in Dallas, so I’m still trying to acclimate to the weather. The biggest issue I’m having is with my allergies. Completely different set of allergens here; I’ve come from the East Coast. But otherwise it’s a wonderful city, lots of wonderful people and lots of career opportunities, especially in data science.

Kirill: Fantastic. All right, so the way we met—for our listeners—is through our courses, right? I think you wrote me a message. Do you remember what that was about?

Kimberly: Actually, I don’t remember what the message was about. But I’m enrolled in two of your courses – the R-programming course and also Tableau.

Kirill: Okay, fantastic. So those are both tools that you use at your workplace?

Kimberly: I do.

Kirill: Okay, that’s great. So tell us a little bit about what you do. What is your profession? How would you describe it? Because I read through your LinkedIn and it just like totally blew over my head. You’re doing a PhD, you’re in medical research, you’re using data and you’re doing tutoring. So you’re in so many spaces. Just in a couple of sentences, how would you describe what you do to somebody off the street, somebody who you’ve just met?

Kimberly: So the basic synopsis of what I do is I basically use information and we try to use that information to – we take data, convert it to information, I should say, and then we try


to use that to impact the lives of individuals who are HIV-positive. So in addition to working with UT Southwestern, the Medical Center at Dallas, I am also employed with an organization called the Dallas Family Access Network. And it’s a Ryan White Part-D funded organization. Again, we provide medical services and support to individuals in the Metropolitan Dallas area who are HIV-positive or have diagnosed AIDS.

So that’s what I do. I try to take the information, analyse it and use it in ways to impact some of the programming decisions that we make about communities that we want to go into to address what has become a national issue of health disparities, particularly in HIV incidence and AIDS occurrence in minority communities.

Kirill: Wow, that’s a very noble thing to do, to apply data science to help people in difficult situations. So how did you get into this field? Like, what is your background? What did you study back at university?

Kimberly: So I originally came into the field through a friend of mine. Her name is Laurie Pressley-Mitchell, and she’s over in an organization called Black Women in Technology. Their main office is in Los Angeles. And her background was in respiratory therapy. While she was working as a respiratory therapist at a hospital, she also acquired a bachelor’s degree in Computer Science. She and I became friends over the Internet.

So having lengthy discussions about ways that her clinical background could be applied to a health care setting, she talked to me about different fields — health information technology, health data science, which is a subfield of data science. So, thinking about ways that I could use the


background that I had already acquired as a research scientist, you know, acquire some additional skills and then be able to continue to use that in a research setting. So shout-out to my girlfriend Laurie.

Kirill: Nice. Big shout-out to Laurie. So what attracted you in that field? Is it a bit different to research science, like what you were already doing at the time?

Kimberly: It’s actually quite different except that most research scientists are aware that you need to apply statistical techniques in order to verify or confirm some of the information that you’re generating in a laboratory. So I had previously been involved in cancer research. I was pre-doctoral fellow at the National Cancer Institute for a year where I worked in genetic epidemiology. So a lot of the nature of that work involved looking at genetic predispositions for specific types of cancer. We looked at BRCA1 mutations, so a lot of that genomic information can be interpreted as data, and then we would use that information to make changes in how people who carry BRCA1 mutations should be cared for should they be diagnosed with breast cancer.

That’s how I initially got into the field, coming from a bench science. I started off doing laboratory, working with mice and rats and things of that nature, and then gradually moved over to more of the analytical side, actually analysing some of the data that we were generating in the laboratory and again, drawing conclusions about some of the studies that we were doing. So we repeatedly kept coming back to doing a lot of statistical analysis. And so coming from epidemiology, the jump to data science was a pretty logical one and a very enjoyable one too.


Kirill: Okay, gotcha. And how is it different? Can you explain? Because I hear a lot of examples where people use R programming in a research setting. How is the way you apply the statistical modelling in your epidemiology career path, and then how you move to data science, how is that different? What did the transition involve?

Kimberly: So right now I’m using R programming to do logistical regression, where I’m looking at two different populations and trying to determine if there’s any statistically significant differences in those two populations. I would say that epidemiology, at least the area that I was involved in initially – it was infectious diseases epidemiology. And so we were using epidemiological techniques in order to map outbreaks of certain specific diseases.

So, again, I think that this designation of data science actually was probably born out of epidemiology, although there might be some discussions from folks in the field who have a different take on it. But I think it’s just a matter of how you apply the information and how you use it. It’s just that a lot of these same types of techniques can be used. Now, with that said, most of what I found in epidemiology didn’t move in the direction of using complex programs like R in order to draw statistical conclusions. They used programs like Stata and SAS. So I think it was basically just a way to extend some of that knowledge that you’re able to gain from basic epidemiological research and actually draw some additional conclusions using programming languages like R, which are quite easy to use once you get the swing of things.

Kirill: Okay. Yeah, gotcha. And how did you find the difference between SAS and R? I just have to ask this question because


with the trends that we’re seeing, how R is getting more and more popular, like even pushing these more enterprise-based or enterprise-oriented and more expensive tools out of the market. What did you find is the difference between SAS and R and which one did you prefer?

Kimberly: Well, because I’m using R a lot more, I would say R for that reason. In epidemiology we use a lot of SAS. I’d probably hesitate to make a direct comparison because I think what happens is that, because of the interest in public health, I think that they’re more used to using more simplistic ways of doing analysis. Whereas I’ve noticed that people, particularly in cancer research use more programmatic languages, like R, in order to do statistical analysis. So I think they both have a role, depending on what types of conclusions you’re trying to draw from the research and the work that you’re doing. It’s just a personal preference.

One of my favourite analogies, having discussions with people that I work with in HIV who are still basically using Excel spreadsheets and databases sometimes, is that there are two modes of transportation that people can use to get to work. You can either ride a bicycle or you can drive a car. I see SAS more as riding a bicycle. It will get you there, it might be a bit slow, whereas R is a car, the way to get to your destination a little faster. That makes it really simplistic and it might offend some but that’s—again, being relatively new to the field, that’s my initial feeling about the differences between R and SAS. One is just a more advanced way of doing pretty much the same thing.

Kirill: Okay, gotcha. That’s very interesting. Yeah, and again, people have different opinions. Somebody might be more into SAS, somebody more into R, and it’s about how


everybody feels their way around these tools and understands how to use them. And you mentioned you used logistic regression in R, which is interesting. It’s a very powerful technique. I can imagine how that would be helpful in what you’re doing. Can you tell us a bit more about how exactly you apply it? More specifically, what kind of challenges does logistic regression help you solve in your day-to-day role? Again, if you can disclose this information, because I can imagine some of it would be sensitive.

Kimberly: Right. So, because we deal with datasets that involve actual patients, I can’t go into too much detail because of PHIPA regulations. But what I can say is that—again, it’s relatively new, I think, to use a programming language like R to make conclusions about different populations in terms of the field overall. Most of the people that I notice in the literature that are using R are people who—actually, a lot of them are actually basic scientists, which is really interesting. So to be able to use it in a public health realm is relatively new.

I think for me, though, it’s trying to move the HIV world into using some of these more advanced techniques. It’s a great idea in general, although like I said, it’s relatively new. And again, it’s a really quick and easy way for me to—something that I might notice graphically, like if I’m comparing two different populations. Again, I look at health disparities and barriers to care. So if I’m looking at one population where, for example, they may have more health care resources, and then I look at another population, I’m able to use logistic regression to say if there are really any statistical differences between the two populations in terms of who may be at a greater risk of being HIV-positive.


And then the program that I work with uses that information to make some designations about how and where to apply resources. So that’s the power of what I’m able to do with R, to be able to help the program that I work for make decisions about how they want to allocate resources. But again, it’s relatively new and most of the people that are in the field are using more basic techniques, but I think the power of R is that you’re able to generate this information and it’s statistically sound and able to challenge it.

And that’s no matter what you do, when you’re trying to get your information published, is that you have to be able to draw conclusions that others are able to duplicate. I think that one of the powers of using R is that the results are pretty irrefutable. You’re able to demonstrate, or not demonstrate, depending on what your hypothesis is, that the conclusion that you’re drawing is actually quite accurate. So that’s, for me, the power of using R for logistical regression. I’m able to back up what I think I can see graphically when I’m looking at two different populations, so that’s really powerful.

Kirill: Yeah, that’s definitely a huge advantage of R. And also that it’s free, right? So if your research is available publicly, then anybody who has a computer and has a desire to just recreate your results can just download R, install it, run the same packages that you ran, and just see the results for themselves and make some minor tweaks. So they can build upon your research.

Kimberly: Exactly. And the R community that’s available for asking questions is very active. You can go on Google, you can go on Kaggle, a number of different sites where you can ask these types of questions. And again, because the software is


free, there’s a very active R community out there always willing to help. So it’s been a relatively smooth transition into data science in large part because of the large community of people who are using programs like R and are so willing to help people who are new to the field like myself.

Kirill: Yeah, I totally agree. And are you seeing any initial results yet from using R? Like, have you been able to deliver some insights that have, for instance, changed the way things are currently done in your organization or other places?

Kimberly: Well, I actually have. I recently was able to prove my first hypothesis. I should also specify that the nature of the work that I do for the organization that I work for is more along the lines of quality improvement versus clinical research, which is significantly different in terms of your hypothesis testing, in terms of the population sizes that you’re dealing with. And so I’ve absolutely recently just confirmed my first hypothesis and I’m actually in the process of writing my first manuscript in a number of years. So when I return from the holidays, I will be speaking to my advisers—both my [inaudible 16:42] adviser and obviously my adviser where I work at UT Southwestern—about some of the preliminary results and I’m tentatively scheduled to present these results at an HIV clinical conference that UT Southwestern has weekly on HIV. I’m scheduled to present that in April of next year, so I’m very excited about the results and looking forward to submitting the manuscript for publication some time in the near future.

Kirill: Okay, fantastic. Congratulations on that. It sounds like a big step ahead. You’ve mentioned you’re in a different space to clinical research, you’re in quality improvement, was that right?


Kimberly: Exactly correct. So we do quality improvement. Again, Ryan White supported through HRSA, and a very significant part of being able to address what are clear and distinct health disparities in terms of specific populations who have higher incidences and prevalence of HIV and AIDS in the community.

Kirill: Okay, gotcha. So that’s a very interesting role that you’re in right now. And I noticed from your LinkedIn that you also are the founder and president of Kimist Konsulting and Tutoring Services. So you’ve been doing that for 4½ years now. Is that like some side hobby that you have? It looks like something that you’re passionate about if you’re doing that in parallel with your job.

Kimberly: I am. So that actually was born of—I had worked in the pharmaceutical industry doing basic science research and had found myself in search of challenges where I wouldn’t have to make decisions about good science and integrity. That’s a politically correct way of putting that. So out of that experience, I decided to create an organization that would allow me to continue working with younger people, particularly minorities and especially women, to encourage them by being an example of how to navigate the STEM field – Science, Technology, Engineering and Math.

And initially I started off as just a tutor. I worked with medical students because I’ve taken a number of medical school classes at Georgetown Medical School while I was a graduate student there. And I also worked with graduate students preparing for the GRE, pharmacy students preparing to take the PCAT, all the way down to elementary school students who are taking basic science and math courses. So in a lot of ways, it’s my way of giving back to the


community. There were so many people that were instrumental in helping me get to this point that I wanted find a way to give back and, again, I had some challenges working in the pharmaceutical industry, so I started working as a tutor and decided to found my own organization. Again, it’s been going on for about 4 years.

I don’t have the time that I used to have in my schedule now because in addition to working full-time as a data analyst, I’m also a PhD student and attending Tarrant County College, where I’m pursuing a Certificate in Health Information Technology. So I’m extremely busy, and so I don’t have as much time as I would like to have to work with people in a tutoring capacity. But that’s one of the things that I certainly hope to build upon in the future once I complete my PhD.

Kirill: Yeah, I can totally imagine how you’re short on time and you’ve got so much on your plate with your PhD and the job and also this tutoring setup or tutoring business that you’re running and giving back to the community. And I think that’s a great thing that you’re doing. It’s amazing to be passionate about something like that when you’re helping other people. So just through the understanding that for you to get where you are other people helped you.

I have the exact same sense of gratitude for those people who helped me in my journey. So you mentioned that you have a passion to help women navigate the space of STEM. For our listeners, STEM stands for Science, Technology, Engineering and Mathematics. Historically, it has been a predominantly male-dominated area. What would you say are the main challenges for women to get into STEM and be successful in the area of STEM?


Kimberly: I would probably say the largest challenge is that even after all the successes that women have had in the area of STEM, that being stereotyped into specific roles and specific fields is still a big problem. I think the second most important thing is being able to identify mentors, people who are there to encourage you to commit to the field. Interestingly, I read an article last fall that said that Calculus One is the great limiting step, to use a biochemist term, in why women don’t stick with STEM.

It’s interesting because I also had to retake Calculus One, but I was fortunate in that I was surrounded by people who encouraged me to hang in there. That repeating Calculus One is not indicative of your abilities in the future in STEM, but I think the message a lot of women are clearly getting—based on the numbers—is that if you can’t be successful in that one course, or general chemistry or general biology, then you won’t be successful in STEM. So I think the conversation needs to turn more towards how internal biases can affect how we communicate with male students versus how we communicate with female students. It’s a big problem, but I think that it begins with an open and honest conversation about what some of the real issues are. And then from there we can work on making some changes.

Kirill: Okay. Yeah, I totally agree. I’ve met a lot of very smart women through my bachelor’s in Physics and when I was working at Deloitte. There’s lots and lots of very successful, very smart women who have overcome these challenges and have found their path and their career in the space of STEM. It’s a very sensitive topic that has been floating around for quite some time and finally it’s surfacing that yes, there is this kind of prejudice and it is hard for women to find their


path in this space. And there’s lots of organizations that are helping out so it’s fantastic to see that you’re doing the same.

What would your one most important piece of advice be to those of our listeners who are female and who are listening to this podcast right now who want to get into data science or some other fields of STEM and need some encouragement or guidance? What would you say is your one most important piece of advice for them?

Kimberly: I would say the one most important piece of advice is to identify someone, male or female, who believes in what you’re trying to do and stick to them as closely as you can. One of the interesting aspects of my graduate education is that I initially relocated to Texas with the idea or with the intent of attending graduate school in biomedical informatics at an institution here in Texas. But after a year in the program I found myself not getting the support and encouragement that I needed to get to the point that I envisioned myself in terms of my career. So I initiated a transfer and have been very happy to be at Rutgers University, where I do feel a very strong sense of support and encouragement.

So I think the most important thing would be to find an institution or an individual, preferably both, who will support and encourage you, but more importantly not be afraid to make a change if you find that what you’re doing or the decisions you’ve made isn’t working for you. There’s nothing wrong with making a change. Sometimes I feel that we have this idea that if we start somewhere and we don’t finish, that that imparts a sense of failure, and nothing can be further from the truth. There’s a lot about finding a place


where you fit in and where you’re going to be supported and encouraged. So seeking those people out would be the most important thing, I think, about going into a field like data science.

Kirill: Fantastic. That’s some very valuable advice and it all revolves around the notion of mentors which we’ve—several guests have mentioned this on the podcast. We’ve had some in-depth discussion how mentors are extremely important in every person’s life, regardless of whether you’re going into data science or not. It’s just an important thing to have somebody to share your thoughts and feelings with and also get some feedback and have these on-going discussions. Somebody who’s going to, like you say, support you through your career and life. It’s something that helps in difficult times and helps guide you as well in good times, just to understand in which way to develop further.

Kimberly: And I just want to give you a shout-out for making R and Tableau so interesting. Your classes are fantastic. I would strenuously encourage your listeners to take one of your courses. It has made a significant difference in my being able to learn Tableau. And I’ve actually looked at some of the courses that are available on Coursera in particular, because there was a lot of positive feedback about one particular set of R courses, but it wasn’t until I came across your course where everything started to click and make sense for me. So I just wanted to thank you for providing that information to those of us who are trying to get into the field of data science.

Kirill: Oh wow, thank you very much. Thank you. I always appreciate great feedback. Well, that’s kind of my way of giving back to the community, like you give back with your


consulting services and helping women navigate their STEM. That’s the least I can do, to help people navigate the waters of R programming and Tableau, which can be extremely complex. So I’m glad that you enjoyed those courses.

And that’s a good segue moving back into R and Tableau and the tools that you use in your day-to-day role. Can you tell us a bit more? So, we talked about logistic regression. And just like all these tools that you are using or you have used, such as R, Tableau, and you mentioned even Excel, and that you do some database design, so I’m assuming there must be other – so there’s some MySQL there and Access. What would you say are some other interesting techniques or methodologies that you use in any of these tools that you would like to point out to our listeners so that there’s something that they can digest and maybe look into for their own careers and professions?

Kimberly: I think that one thing I would encourage people to do is to not be afraid of what can be seen as a steep learning curve with some of these more advanced techniques. Specifically, I’m referring to Tableau, and it’s not inherently difficult to learn. It can be real difficult to use the different dashboards and figure out what all the features are. But I think that what you get out of that are amazing data visualizations which can be critical to communicating whatever point you’re trying to make to audiences that may not be as STEM-astute. So I use Tableau extensively to make points that again, make it more clear for some of the people, individuals that I deal with on a daily basis who may or may not have the scientific background to understand. Most people, you can explain something in words, but if you’re able to make a visual presentation of it, it makes a lot more


sense. So that’s the extent that I use Tableau pretty extensively and it’s my preference in terms of all of the data visualization software that’s out there. It’s the one that I use most extensively.

Kirill: Gotcha. And that is actually something that I find very forward-looking in your position because I’ve seen a lot of situations where people use R in medical research. It’s one of the most popular tools and I think that the Johns Hopkins University is all about using R for medical applications and research as well. And a lot of the time I find that that’s where people limit themselves. They just use R and then they use ggplot2, which is an R package, which is fantastic, and they use it for visualization. Ggplot2 is fantastic for visualization, like ad hoc visualization for research, for trying to understand how something works, trying to build it. As you’re doing the analytics, you need this ad hoc research.

But if you’re creating a full-blown presentation that you want to, like you say, get across to people who might not be as STEM-astute as you are or as other people that you normally interact with, or even if you want to create a beautiful presentation for a conference, then I see Tableau having so much advantage over ggplot2, or for that matter any R visualization package, that I just don’t understand why people choose to limit themselves and not explore the world of Tableau.

Perhaps it could be due to the steep learning curve of Tableau, even though I think Tableau is easier to pick up than R. And also it could be because maybe people just want to stick to that one tool, R. But at the same time, it’s great to see that you’ve chosen this pathway of separating the two.


You do your analytics in R; you do all the regression and all that other type of modelling in R, and then when you need to create a presentation, you switch to Tableau. So, how would you say you actually discovered Tableau and why did you decide to make the separation between the analytics and visualization and find the right tool for each of the jobs? What prompted you to venture down that path?

Kimberly: You know, I’m actually thinking. I’m not really sure how I even learned that Tableau existed. I honestly think it was through—you know, I can’t remember. I’ve only been using it about a year. Maybe not quite a year, maybe I started in January, I think. But if I had to guess where I think it came from, I actually think it came from looking through the courses that you were offering that you teach through Udemy. Is that correct? I think I got that right.

Yeah, I think that’s where I actually learned about Tableau, it was through looking through your course listings for different data visualization tools and recognizing that, again, because a lot of the information that I generate is going to be used to not only communicate with physicians and clinicians but also with people in the community who are HIV-positive and are part of the various Ryan White committees that most local cities, large cities have. And so trying to find the best tool that would be understood by people of various backgrounds was what initially prompted me to start using Tableau. And it’s worked quite [inaudible].

Kirill: Yeah. And so would you recommend Tableau as a tool to people in professions similar to yours?

Kimberly: I absolutely would recommend it, although someone recently turned me onto Microsoft—


Kirill: Power BI?

Kimberly: Right. (Laughs) Microsoft BI. I have it downloaded on my computer. I haven’t started playing around with it just yet. As a competitor, I would imagine—like I said, I use Tableau, I’m happy with it. Someone else suggested that I look at it. And I think that one of the other members of one of the other research groups that I work with in the infectious disease department actually uses BI. But yeah, I’m happy with Tableau. It works for the needs that we need it for, and I absolutely would recommend it. And as you mentioned, ggplot2 works well, but when you want to make an impact I think Tableau is the way to go.

Kirill: Yeah, I totally agree. And just on Power BI, there’s actually a couple of tools that are in the space of visualization. And the leaders, I would say, are Tableau, then we’ve got Microsoft Power BI, we’ve got QlikView, we’ve got QlikSense, and then we’ve got a couple of others. There’s an IBM tool which they’re promoting and so on. Yeah, I would say Tableau is by far the most robust tool. And then I would say next, in my view, would come Microsoft Power BI, the one you mentioned. They are really putting a lot of effort into it. They only launched like a couple of years ago. Tableau has been around for 10 years or so—as far as I remember, 10 or 6, like, many years. And Microsoft Power BI has only been around since like 2015, or somewhere thereabouts. But they’re already really catching up to Tableau.

And the big difference is that of course Microsoft BI is free, whereas Tableau has a very large fee associated with it. And so it’d be interesting to see how that goes. And if anybody listening is into visualization tools and wants to pick the right one for their job, there’s a great report – it’s called the


Gartner Report, it comes out every year around February. And what you need to google is just google the “Gartner magic quadrant for BI (Business Intelligence).” They’ve like set up this quadrant on the Y axis, I think, you have completeness of vision, and then on the X axis you have the—I forgot. I think it’s like what they have on offer—what kind of functionality they have on offer in that tool, and then they plot out all these different visualization tools. So it’s really cool to observe that. So that’s very interesting, that we went into a bit of visualization.

So tell us how your PhD is going. You mentioned before the podcast that you just started your PhD. What is your PhD going to be about?

Kimberly: My PhD is in biomedical informatics, and I’m looking to concentrate in public health informatics. And given the fact that I’m very happily working with HIV and AIDS individuals in terms of quality improvement type projects, I’m looking to have a long-term career using informatics tools to improve the lives of people who are HIV-positive and have AIDS. So I’m hoping to focus my dissertation and my coursework in public health informatics. So that’s the concentration I’m actually looking to as a PhD student at Rutgers University.

Kirill: Okay, gotcha. And if you can disclose this, what are some of the ways that you look into that you can improve the lives of people that are HIV-positive?

Kimberly: So, again, there is a significant health disparity with who is HIV-positive in this country, and it unfortunately tends to fall disproportionately on poor communities and communities of colour. So what I’m hoping to do by using advanced techniques in informatics is to be able to impart some changes both at the clinical level and the community


level which will allow us to address some of these disparities and improve the lives of people who are HIV-positive and who are looking to seek proper care for their HIV with the ultimate goal of preventing the progression to AIDS. I’ve been an AIDS counsellor when I was an undergraduate student, so that’s how I first got exposed to working with the HIV and AIDS community. So I kind of moved away from it a little bit and then have come back to it and have been very happy to be back, being able to apply some of this new technology in the field of HIV and AIDS.

Kirill: Okay, gotcha. I apologize for my ignorance on this topic, but could you explain how is HIV different to AIDS? You’ve mentioned that HIV progresses to AIDS. What is the main difference between the two?

Kimberly: So, individuals who are HIV-positive, it means that they have shown a reaction to the virus which has been linked to AIDS. AIDS is the more advanced form of the disease and unfortunately it can be lethal for many individuals. Until the advent of AZT, HIV was pretty much a death sentence. But now thanks to the discovery of a lot of new drugs and drug cocktails, people who are HIV-positive are now able to live—as long as they take their medication – they’re able to live lives that are comparable to people with other types of chronic illnesses like diabetes.

So the big idea now is to keep the viral load of people who are HIV-positive at an undetectable level. It does not mean that they cannot transmit the disease, it just means that there is no detectable level of virus being actively replicated in their bodies. So that’s the condition again, that people are able to live long, healthy, extended lives as long as they are compliant with their physician’s orders in terms of taking


their medications. So it’s a very different picture than when AIDS first came on the scene, in the 80s I guess it was. Initially it started off as a death sentence and now it’s treated just like any other chronic condition, like being diabetic.

Kirill: Okay, so basically if somebody discovers this early on, then they have a much higher chance of treating it properly and still living a very healthy, an interesting and good life while they take that medication. Is that correct?

Kimberly: Exactly. So initially what happens with HIV is when a person is first exposed, the literature seems to indicate that that’s the period when the person is most highly infectious. And a lot of times, when people are in that highly infectious stage, they are not aware that they’re HIV-positive. So that’s why it’s critical for people who engage in activities which put them at higher risk, like being IV drug users or having intimate relations with people who are IV drug users, that they regularly get HIV tests so they can stay on top of what their HIV status is. It’s a significant portion of people in this country which are believed to be HIV-positive and unaware of their status. So those are also another group of people that many in the HIV and AIDS community are trying to target with programs that allow for free HIV testing with the goal and with the idea that if we can catch people early, particularly in the infectious process, then we can prevent the spread of the disease in the community.

Kirill: Alright, thank you very much for that explanation. I definitely learned quite a few new things from there. And I must say it’s very noble of you to choose to do your PhD in this area and actually dedicate a large portion of your life to helping people that are suffering from this disease. And


speaking of your PhD, I wanted to ask you this question. Based on what you’ve already done in your previous roles or in this role previously, and the way you see your PhD coming together, the way you’re thinking about structuring it, what role do you think data will play in your PhD? I understand you’re still at the very start of your PhD, but at the same time, looking forward, how do you think you will use data in your PhD? Do you have any plans for that?

Kimberly: I absolutely have plans. And again, I kind of very deliberately started putting this plan together over a period of years of meeting with different people in the field and figuring out ways to create something that would be both a personal and professional interest. There hasn’t been a large application, from what I’ve been able to gather in the literature, of using data science to specifically address health disparities. So that’s one of the ways that I hope to develop my career in the future, to use this information that I’m able to have access to, de-identified of course, turn that data into valuable information and use that information to make quality improvements in the lives of people who are HIV-positive. It seems like it should be a logical application of the use of data science because it’s using every other industry, obviously.

And now I think that the public health field is starting to come around to some of the advantages that this data that we already gather lots of information on can be used to basically decrease disease burden in our communities and improve overall public health. So that’s a pretty good synopsis of what I’m hoping to do with my work in health data science in the field as it applies to public health informatics.


Kirill: Fantastic. That sounds like a huge challenge and we wish you the best of luck with that. I think that can make a very large positive impact on many communities, not just in the U.S. but that research could be extrapolated to other countries as well. And speaking of that, do you actually work internationally? Have you done any collaborations or something like that in terms of international impact?

Kimberly: Actually, I haven’t done any. I’m not currently participating on any international type projects but obviously, because the HIV burden is also pretty heavy in the continent of Africa—as you mentioned before, a lot of the knowledge that we’re able to gain from the application of data science to public health and HIV and AIDS incidence can be applied internationally in places like Africa. And so while I’m not currently involved in anything on the international level, there’s absolutely no reason to think that some of the programmatic changes that we can make or create here based on some of the analysis that I and others are doing can certainly be applied to other places where there is a high HIV and AIDS incidence.

Kirill: Yeah, totally. I think it will be an interesting PhD that you’ve set up for yourself because it combines both data science and something that you’re very passionate about. So I’m very excited for you going there. And speaking of accomplishments and using data, what would you say has been your biggest win that you’ve had recently using data science? Again, if you can share that with us. So something that you’re proud of, something that you were able to use data for to do differently and to create something unique.

Kimberly: Again, because we’re in the process of writing up a manuscript, I can’t go into too much detail about it. I forget


what the phrase is when someone beats you to the punch in terms of publishing, but yeah. So I’ve been very pleased that basically we’ve been able to analyse this data and we have some very important stories to tell in terms of those barriers to care that disproportionately affect poor minority communities. So, again, I can’t speak in too much detail about it but I hope that information published—again, it’s something that, because of the nature of what we do, it won’t just be applicable to the Dallas Family Access Network where I’m currently working with UT Southwestern, but it could be applied to any area of the country where there are high HIV and AIDS burdens in the community. Again, I can’t give too specific detail right now, but I’m very pleased with some of the results that we’ve been able to generate thus far and I’m looking forward to 2017 when hopefully all this hard work will come to fruition.

Kirill: Yeah, gotcha. And is it going to be a publication that people can just read on the Internet?

Kimberly: Absolutely. Obviously, I will provide a link to it on my LinkedIn profile after it’s accepted for publication. And I’m actually looking forward to participating in the International AIDS Society meeting in Paris. That’s a dream, to go there and hopefully be able to present this research to a larger international community. That’s my top goal for next year, but there’s obviously some other AIDS organizations and meetings that are going to be held next year that would also be a wonderful addition to my background in HIV and AIDS research. So, yeah, 2017 is looking to be a big year for our group.

Kirill: Fantastic. Yeah, and Tableau will help you with that presentation and hopefully we can get a link of that and put


it on our show notes as well so people who are interested, who are listening to this later in 2017, can get a glimpse of what you’ve been up to and what results you’ve come up with.

Kimberly: Absolutely.

Kirill: Wonderful! So we’ve got some rapid fire questions here leading up to the end of the show. What would you say is the biggest challenge you’ve ever faced as a data scientist?

Kimberly: I would say just breaking into the field. Coming from a bench science background, it was extremely difficult to convince various individuals and HR departments that I did have the skillset that would be required to succeed in data science. And I would say the most important personal characteristic you need to succeed in the field would be tenacity. You have to be willing to start over, go back to the drawing board, reanalyse, analyse. So coming from a bench science research background, I had those skills, but convincing people to give me a shot when I didn’t have the formal training was quite challenging. So that was a large part of why I knew I had to go back to school, because it’s getting to be pretty competitive, and a lot of the opportunities are open to people with computer science backgrounds, which are helpful, but in the field of health data science, if you don’t have some sort of background, either in the biological sciences or in the medical sciences, you’re at a pretty significant disadvantage. And so I was able to parlay my interest in data science into an area which would allow me to use my background and then be something that I could build on for the future. So I’m very excited about how that eventually turned out for me. Again, it was challenging breaking into the field.


Kirill: Gotcha. And what would your advice be for somebody who is trying to break into the field who is also coming—was the term that you used “bench science”?

Kimberly: Yes, bench. That means I was in a laboratory.

Kirill: Oh, gotcha. So what advice would you have for somebody coming from a bench science background trying to break it into the field of data science? Like you had to convince people to give you a shot. What would you recommend them to do to get to where they want to?

Kimberly: I would recommend that they take advantage of any number of big free courses that are available – specifically yours. I would start with yours, although again, some folks recommend others. Whatever works for you, I guess, is fine. The idea is that you can get into it virtually for free; all you need is an Internet connection and a computer.

So I would first maybe take a year, start with the programming course and then work your way from there. I call it trial by fire. The first programming course I actually took was upon the recommendation of a family member who’s a software engineer, and he suggested I take Java. And I thought, “What does Java have to do with anything?” But what it actually was, it taught me how to think like a computer scientist which is, you know, a big part of being a successful data scientist.

And although I don’t think you have to be a computer scientist to be a data scientist obviously, being able to think along the lines of a programmer has repeatedly proved to be one of the smartest things I ever did. Because it made every other language I got exposed to that much easier. Again, my recommendation would be, take about a year, start with the


programming course – I would not suggest R – something like C++, just so that you can get used to some of the syntaxes used in programming, and the way of thinking and looking at problems, and then build from there. That would be my suggestion.

Kirill: Gotcha. Great advice. Thank you very much. Next question is very interesting: What is your one most favourite thing about being a data scientist or being able to apply data in your day-to-day role?

Kimberly: This one is really easy. One of the things that’s particularly frustrating about being a bench scientist is that it can take anywhere from years to decades to see something you’re working on, an idea you have come to fruition. One of the great things about data science is that with a couple of manipulations you can tell quite easily whether or not you need to make, in my example, program changes or you need to target this community versus another for HIV/AIDS testing. Data science allows you in a relatively short amount of time to get answers to questions. And I actually like that part about it, as opposed to waiting months, years, decades to answer a question. So that’s the part about it that I enjoy the most.

And that it’s never the same thing every day. That’s the other part about it, that on the outside looking in it may appear to be mundane, but it actually isn’t. You may go in thinking you’re going to do this set of analysis and then find that you need to tweak some of your variables or something like that. So I think the fact that it can be different every day and that you can get an answer to a question in a relatively reasonable amount of time is a large part of what I enjoy about data science.


Kirill: Fantastic. Very insightful. And I totally agree with you that it’s very fast and it’s also very interesting in terms of it changes all the time. And the big question: So, from where you are—and I think your answer to that is actually going to be very valuable to lots of our listeners because you’re in a very different space, you’re in biology or in medicine, and very few of our guests are operating in that space. So the question is: From where you are, and from what you’ve seen about data science, where do you think this field is going? So, what do you think is coming for the world in terms of data science and specifically in your profession? And what would you say listeners need to prepare for to look out for the future so that they can build their careers in that space or just be prepared for what’s coming in terms of data and medicine?

Kimberly: So, I think one of the bigger, more significant applications of data science in terms of medicine is that information that is collected about you on the data side can be used for anything from collecting genomic data that can be used to determine which drugs will better treat your cancer, or coming from HIV, what areas of the county should we be looking for to see the next outbreak of a certain disease. So I think in medicine, the sky is the limit in terms of how useful it can be. And ultimately, with the goal of saving people’s lives and improving people’s lives who are dealing with diseases which can many times be fatal. So that’s the biggest thing that I see data science being able to be used in the medical space, is that it has an overall goal of improving care.

I think one of the challenges to data science being applied heavily in the field of medicine is that oftentimes people are


reluctant to change, and that there is a big learning curve with it. I’m seeing that a lot with the implementation of various EMRs in hospital settings, that there is a learning curve involved with learning how to properly use an EMR or EHR to document information in a chart for a patient, but I think that the overall goal of saving lives and decreasing health care costs makes it a challenge that’s worth people in that profession and in that space meeting. So I guess that’s my comment about medicine and data science. I think the sky is the limit. I think ultimately it will improve the health of people and that will ultimately drive costs down. At least that’s the hope.

Kirill: Yeah, I totally agree with you. Data science can definitely help with all those areas. And what you mentioned now just sparked an idea in my mind. I’d really like to get your opinion on this. What do you think of what’s going on with data science and the genome and genetics basically? I think it’s like $1,000 you pay and then you get your whole genome code and basically they can predict what diseases that you might have as you age and what diseases you’re most susceptible to. What’s your opinion on that?

Kimberly: I think my biggest concern about some of the information that can be gathered about individuals genomically has to do with the ethics of how that information is used. One of my biggest concerns with that in and of itself is that insurance companies could use it to charge you different rates for health care insurance. We need to be thinking about some of the ethical implications of some of these technologies before they become a problem. I think that we tend to—to use the country slang—tend to run after the cow after it’s already left the barn. I think that’s how it goes.


So I think that there probably needs to be some concurrent conversations held about, “Okay, so we have this information. We know this person is genetically predisposed to developing Alzheimer’s. How are we going to use this information to help people and not to charge them more for their health care insurance?” That’s the thing I worry about. That the ethical questions around some of the information that we’re gathering are not being answered or even addressed. And I think it could really be huge.

On the other hand, I think that that information could be useful to people to know, again, what types of drugs might be better suited based on what their genomic liver profiles shows, things of that nature. So I think we have to be careful to address some of these ethical and legal concerns before we get too far ahead of ourselves in the science.

Kirill: So, like, on a community basis, it’s the ethics that are a bit concerning that need to be addressed. But on an individual basis, like just for an individual person or somebody listening to this podcast, you think it’s not a hoax. If they have the finances to do this and they’re interested, then they can go and just get this genome done and you think that can add value to their lives.

Kimberly: Absolutely. The only thing I would caution is that you speak maybe to a genetic counsellor because you may find out things you might not want to know. You might not want to know that you’re going to be bald by the time you’re thirty. That could be problematic for some folks. Obviously, I’m being facetious with that example, but I would certainly caution the ethics and also some counselling if you know that you have a genetic predisposition to certain serious medical conditions, you’ll certainly want to seek a genetic


counsellor to get their take on what the results mean and what they would mean for your life.

Kirill: Yeah, fantastic. I actually want to do one of those. I don’t think they offer them in Australia, so the next time I’m in the U.S. I’ll get one of those done. It sounds very interesting to find that out. That’s good advice, as well, to get a counsellor so you’re not just reading these results on your own and getting all depressed. You actually have somebody guiding you through this and helping you acknowledge what you’re seeing in that paper.

Kimberly: Exactly.

Kirill: Okay. Thank you so much for coming on the show. It’s been a very unique episode, speaking of data science and health. And if any of our listeners would like to follow your career or learn more about your PhD and read your research papers, what would you say the best way or ways is to contact you or just follow you?

Kimberly: So, I’m actually on LinkedIn. I pretty much accept invitations from all positive people, especially in data science. It’s been a really interesting way to connect with different people and how they’ve applied the field of data science to their individual careers. I’m also on Twitter, having recently found it and it has now become somewhat of an obsession, at the Twitter handle @DataKimist to combine my love of data and my name, a take on my name, I’m also formally trained as a chemist – shout-out to UNC Chapel Hill. Yeah, so that’s where I can be found. Either on LinkedIn – Kimberly Deas, or I’m on Twitter @DataKimist.

Kirill: Wonderful. We’ll include those in the show notes. And for all of you out there who use Twitter, definitely follow Kimberly


on @DataKimist. I’m sure you’re up for some very interesting and insightful shares that she’ll be doing. And one final question I have for you today, Kimberly: What is your one favourite book that you can recommend to our listeners that will help them become better at data science?

Kimberly: You know, I thought about this question a lot. I found the O’Reilly series of books on R programming and Python to be extremely helpful. When I was initially going into the field, or contemplating, the series of books – “Bioinformatics for Dummies,” “Python for Dummies,” “C++ for Dummies” – those were initially very helpful. I think that the thing about data science and data analytics is that there’s not any one book, or at least I haven’t found one, that kind of encompasses everything, because it can on a certainly level be a very broad field. But in terms of how I got into it and books that I found useful, breaking it down into the language that I could understand, I would definitely say the O’Reilly books and the series that are written for dummies.

Kirill: Gotcha. So we’ve got the O’Reilly series on R programming and Python programming and the series “…for Dummies” books on different programming languages such as C, C++, and so on. Thank you so much, Kimberly, for coming on the show. It’s been a great pleasure having you on and I’ve definitely learned a lot from the insights that you’ve shared.

Kimberly: Great. Thank you for having me. And thank you for being a fantastic instructor in R and Tableau.


Kirill: Thank you. It’s my pleasure. Bye.

Kimberly: Bye.


Kirill: So there you have it. I hope you enjoyed the today’s show. And personally, my favourite part was when we talked about where the field of medicine is going hand in hand with the field of data science. I always find it very exciting to talk about how medicine is evolving, how rapidly it’s evolving and where it’s going. And that discussion about genomes and how data science is used to generally help people better understand what diseases they might be susceptible to, what medicine might be more efficient for them, I think that’s a very exciting space. I think that’s a future of us as a human race and it’s always interesting to get people’s opinions on it, especially when people are actually in that field. So I really enjoyed that part of the discussion, and I hope you did too.

I hope you enjoyed lots of takeaways from today’s episode. And if you’d like to get the show notes and links to all the resources from this episode, then head on over to www.superdatascience.com/25 and there you’ll find the transcript, links to resources and also links to Kimberly’s social media where you can go ahead and follow her and find out how her career evolves in the future. So make sure to connect with Kimberly. And finally, if you’re listening to this podcast on iTunes, I’d really appreciate if you could click that button and rate this podcast and give us an amount of stars that you think we deserve. That would be great help for us. And on that note, I can’t wait to see you next time. Until then, happy analyzing.

SDS PODCAST EPISODE 25 WITH KIMBERLY DEAS · Kirill: This is episode number 25, with Data Analyst...

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