Panel: Career Advice for Data Scientists | RStudio (2020)
Featuring: Gabriela de Queiroz, David Keyes, Sydeaka Watson, and Jen Hecht This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion. We'll be touching on topics like the different stages of career growth and how you find a community you can go to for support. If you're just getting started in your career, looking to make a transition, or interested in learning some great career-building skills, be sure to join us! About Gabriela de Queiroz: Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she manages and leads a team of developers working on Data & AI Open Source projects. Her team contributes to projects such as TensorFlow, PyTorch, Apache Arrow, Apache Spark, and several other open source projects inside IBM. She works to democratize AI by building tools and launching new open source projects. She is passionate about making data science available to everybody and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 50+ countries. About David Keyes: David Keyes is the founder of R for the Rest of Us, which teaches people and individuals to use R through online courses, custom trainings, and public workshops. With stops as an elementary school teacher, PhD social scientist, and program evaluator, David brings his unconventional career trajectory to his current role, helping those with who don't think of themselves as typical R users embrace the power of R. About Sydeaka Watson: Dr. Sydeaka Watson is a native of New Orleans, Louisiana and currently lives in Dallas, Texas. She is Founder and Owner of Korelasi Data Insights, LLC and a Senior Data Scientist at Elicit Insights, LLC. In these roles, Sydeaka uses predictive analytics and visual tools to draw insights from diverse datasets. Sydeaka earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. In her 5 years as Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams in The University of Chicago Medical Center, specializing in statistical analysis and experimental design for clinical research studies. Her current research interests include applications of (1) image recognition for computer vision and (2) data science for social justice. Sydeaka currently serves as Organizer of the R-Ladies Dallas Chapter. She also volunteers in the Dallas chapters of Girls Who Code and Black Girls Code. About Jen Hecht: Jen Hecht is the VP of People Operations at RStudio. She was first introduced to R in 2013, as a non-programmer seeking better ways to manage analytical projects - a quest which was aided both by the RStudio toolchain and the welcoming support of R Ladies, R meetup groups, and other wonderful open resources. Ever since, she has been captivated by open data science tools and the communities that build them. Before joining RStudio in 2018, Jen held HR and People Analytics roles in a variety of industries, including financial services, biotech, and used record shops. Outside of work, Jen loves books and music of all kinds, and is a novice fly caster
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Transcript#
This transcript was generated automatically and may contain errors.
Hi everyone, I'm Jen Hecht, and I'm the DP of People Operations at RStudio, and I'm thrilled to be on the stage today with some folks who are going to help us talk about building a career in data science, in particular around the use of R, but not exclusively R. So let me just introduce the panelists that we have today. Sitting right next to me, we have Sadiqa Watson. She's the founder at Corolasi Data Insights, and also senior data scientist at Elicit Insights. We have David Kyes, who's the founder of R for the Rest of Us, and we also have Gabby DeQuiros, senior engineering and data science manager at IBM, and also founder at AI Inclusive and R-Ladies.
So I thought we'd get started by asking each of you to talk a little bit about your own experiences sort of growing your career in data science. I'm interested in hearing a little bit about, you know, how did each of you experience milestones in your data science career? Or perhaps if you're a team manager or somebody working with people that are learning as data science, how do you think about, like, what are the stages of this, and how have you moved through them?
Panelists' career paths
So let me just, like, summarize kind of, like, my career path, because it's interesting and different from all of us here. So I was in academia for about five years doing research with air pollution data. So then I moved to the industry. So I had this transition point where I'm like, I want to try something else. I want to go to work for a company. So then I became a data scientist. So I was a statistician before doing research, and then I became a data scientist. Also I moved to San Francisco. So statisticians in San Francisco, they all became data scientists. So I was first working for a startup, and then doing some kind of, like, consulting. And then I moved to a different startup, and then became a lead data scientist, and then moved again, senior data scientist, moved again, became a developer advocate, and then I got to a point, an opportunity to become a manager. So it's kind of, like, I don't know, like, exactly what I, like, you know, you wait two years, and then you did this. It's kind of, like, it's when I felt ready. And I think becoming a manager was a big jump from all the other transitions that I had to go through.
So my path into data science definitely has not been linear, particularly because my motivations, my just thoughts about who I was, and what I wanted to become, and the types of things I wanted to do, they changed. So, for example, when I was in high school, I thought, I love math. The people that I see around me in high school are my math teachers. They love math, and they became math teachers, and so I'll be a math teacher, you know. And then at some point, I discovered that there were some other things beyond education that you could do with math, and heard about being a math professor, you know, and then found out about biostatistics after I became interested in that type of career. So I ended up becoming a biostatistics researcher, and then, you know, as other people have drank that same data science Kool-Aid, heard about all of the cool projects and the great salary structures associated with data science, and decided that that was something that I wanted to do. So some of it was just exposure, where people told me about other different types of paths that I might not have thought about, and some of it was just also just restlessness, change in interest.
So first of all, I should say, I think my current work, as well as my path, is pretty different from the other folks on this stage. Unlike them, I work for myself. My business, as Jen mentioned, is R for the rest of us, and I primarily focus on training folks to use R. And I particularly focus, I think of it as kind of like people who are scared about R but want to learn it. That's kind of my niche. And how I got into it, I think I, it's very much by accident, which I know is a story that resonates with a lot of folks here. I'm actually trained, I'm not very quantitatively trained. I have a PhD in anthropology. My dissertation was entirely qualitative. Worked on some mixed methods projects, kind of butted, or not butted up against, but came in contact with some folks who were using R but never actually used R in grad school. And it was actually only when I started doing some consulting work where I was doing what's called program evaluation work, looking at kind of applied social science research, looking at the impact of programs run by non-profits, that type of thing. And the organization I was working for allowed me to use whatever software I wanted to, so I taught myself R. And basically since then I realized that there was a need for other people in that industry as well as others to learn R.
Building community
I think one theme is that it's non-linear. It's not like there's a set of predefined steps or ladder, or you've got to do this before you move on to the next thing. How did you build that kind of community, whether you were in industry when you were learning or you were coming out of academia, kind of go out and find it for yourself?
Yeah, so I didn't have this community before back in Brazil, so when I moved over here and created R-Ladies, that was one of the intentions. It's like, I don't have a community, so I'm going to create it. So that's how it worked for me.
So I could answer this question in two different ways according to the period of my life that it relates to. So in the earlier periods, of course, where I'm new to the different types of mathematical concepts and different statistical, I guess I didn't mention this before, so my background is in math and statistics. So I have a bachelor's in math, a master's in math, a master's in statistics, and a PhD in statistics, and no student loans. Yay. And so all of those different types of educational experiences put me into different types of environments where I've got a particular mix of students, a particular set of faculty who are trying to, you know, together help me to learn what I need to learn from whatever that perspective is. But one thing that I've noticed as a professional is that you don't always have the benefit of having those readily made communities. Sometimes you just have to either create your own community, as our esteemed panelists did for R-Ladies. I'm also an R-Ladies Dallas chapter organizer as well. But sometimes you have to be just very comfortable being a community of size one, right? And especially in places where maybe you're the only one who cares about data or you're the only one who cares about R or whatever this new model or tool is or whatever, where you're either internally motivated and you're just reading and going online, looking at blogs or, you know, different types of user groups, or you're also just going out, finding meetup groups, finding different types of organizations, just to connect with people who have similar interests to you.
And for me, I think the answer is actually pretty straightforward. Online communities, and particularly Twitter, have been really fundamental in terms of my learning of R. I mean, I can say without reservation that I've learned more about R from Twitter than any other single source. But also finding a community and building connections with folks. I've only used R for three years. You know, only really kind of got more involved with this community in the last couple years. The major benefit, if you're not already aware, of the Twitter community and the R community in general is that it's extremely welcoming. And so I know I felt coming into it like I'm an anthropologist. I'm not, you know, I don't have much quantitative background. Am I going to be accepted? And I have found I've never not felt accepted. I think because there's such a diversity of the things that people do with R, everyone, and a number of other reasons, including strongly the work of R ladies, have made the R community extremely welcoming.
I can say without reservation that I've learned more about R from Twitter than any other single source.
Advice for early-career data scientists
So let's make a broad assumption that a lot of people in the room are maybe in the earlier stages of their data science career. If you had like a few minutes to spend with them and give them advice about how to grow that in in the best way possible, what advice would you share with people?
So my background, I mentioned that I actually went to school for statistics and actually went into a biostats career. And that was before I even knew what data science was. So when I learned what it was, I realized, especially as I started looking through some of the different job opportunities, that they would ask for this long list of skills. And it was very intimidating. We need a person who knows this, and they know this and know this. And it was just, how can I get that expertise so that I can just get my foot in the door into this first role?
And so that would be one thing that I would advise you to think about is to just not be overwhelmed. Just think about some of the key principles, I guess, that I would say are fundamental to data science. I would say that there are three key areas of data science that you'd want to have some proficiency in. One would be, there's obviously the mathematical component. So there's where your machine learning or maybe even your deep learning, your clustering algorithms, all those kinds of different algorithms, even logistic regression, ordinary least squares regression, basic data analysis. The next would be programming. So obviously having proficiency in something like R or Python, some type of general use language such as one of those is ideal. Not just understanding specifics of how to use this package in that module, but knowing how to program, right? How to be a programmer. How to actually do the loops and if then statements and actually put together some piece of code that accomplishes some task. And the third, I would say, would be the big data technologies. So some of you might be familiar with SQL or Hadoop, Spark, Hive, things in that ecosystem.
Yeah, I agree with like the job descriptions and all that, but I think if you are a beginner, even like big data, like some companies they are not there yet and they are still on the Excel spreadsheet, CSV files. So like if big data is like a big thing, like you are not there yet, don't worry because I still think there are some, several companies that are still doing small data, working with small data. The companies, they want the unicorn and you as a data scientist, you don't have to know everything because if they are looking for the unicorn and you are the unicorn, you are probably not going to work for that company. So if you are trying to get into data science, I would say, you know, apply for jobs. You are going to feel that you are not ready and that's the feeling that you are going to have forever. You are never ready, right? So just apply, apply, apply, apply and try to get a sense of like how the interview processes are because they vary so much. So I just would say if you are looking for a job, just apply. Don't wait because you will never be ready.
You are going to feel that you are not ready and that's the feeling that you are going to have forever. You are never ready, right? Don't wait because you will never be ready.
So my answer will be very much tailored to my work which is basically as a consultant. I think I would offer two pieces of advice. One is to try and find a niche if you are thinking about trying to do some consulting. I mean the advantage or one of the major advantages of R is that it is multifaceted, right? You can do so many different things with R. And as an R user, you know that and you get very excited. I think I found with the consultants or as a consultant working with folks, it can be overwhelming to come in because I do training and then I also sometimes actually will do some work for folks. So for example, I focus when I'm not training, I mostly do data visualization because that's an area that I'm pretty strong in. So having that niche I think is important.
Okay. Cool. I'll just add that in my experience working in HR, most job descriptions are terrible. Honestly. Especially in data science, you know, half the things on there, there's no human that could possibly do it. So you shouldn't let that get in your head.
Technical track vs. management
So with your permission, panel, I'm going to go to some questions from our folks out here. First one is, has 30 people asking this question. I'm trying to decide whether to maintain a technical role or try to shift into management and decision making. Any tips on what to do if I want to do both?
Yeah. That's a struggle that I go through. And before becoming a manager was the thing that I was most afraid of is like, am I going to be in this sea of bureaucracy and then I'll lose all my technical skills that I worked so hard for several years. I was not sure. So I talked to a few folks in the company, especially the company that I work for, asking them, you know, how was your path? And then how do you feel about keeping up with the technical piece as well? So in large corporations, in particular, you can kind of like, you don't have to go one way or the other. You can kind of go in the middle and you can also like always go back and forth. So you can become a manager and go through the management track, but you can also come back to the technical track for a few years and then come back again to the management track. So that's the beauty of like some companies where you can go back and forth. The other thing is try to, you know, get some time for you. Like, let's say on Fridays, I try to schedule no meetings and then I go to a corner in the room in the office and then I put my headphone on and then I just do some coding or code review. So that's my strategy to keep me happy because there is a piece of me that I miss if I'm not doing like technical work.
Yeah. I've definitely been, I've had that same fear actually. So I was actually kind of interested to hear what other people had to say about this because I've been invited to go into management and, you know, into director level roles, but I like having my hands on the data. I like having my finger on the pulse of all the different types of cool tools and strategies that are out there. And so I don't want to lose that. I would say that something that I really like those tips, doing things that help you to continue to work those muscles. So I know some people who actively had to go on hacker rank every day and do some coding exercises in the morning or, you know, actually participate in code reviews and so on.
Yeah. Just one thing that I was thinking now. So if there is anything that I need to measure, like, let's say I need to measure something on my team and then I'm like, yes, I have some, you know, time for me to create R Shiny where I'm going to measure something. So I try to translate tasks that I need to do in a coding exercise so I keep up with my coding.
Yeah, we can talk, like, it's a big jump. It's a big jump for sure. It's so different. But also, you are developing other pieces of your brain, your skills that is going to help you also in the future as a coder, a programmer. So another example that I was thinking is one day I had to write something and then it came, like, things were coming through my mind. And I'm like, okay, I didn't forget those things. And then I became a better searcher or, like, I don't know how to say that. But, like, I can now search even better because there is some piece of my brain that got developed a little bit better. So I know exactly how to look for answers where before I would struggle much more. And if you have the ability to, you know, I want to become a manager. I'm going to give, like, a time. I'm going to time box this for two years. I'm going to try it out for two years. If I don't like it, can I go back?
Salary negotiation
Another very popular question up here is, can you talk about salary negotiations? Any tips?
So I was a contractor working at a company along with a friend, a male colleague who was also a contractor. And he ended up deciding to go into another company. And as they were negotiating his salary, they asked him, you know, how much are you currently making? Right? Which, of course, is the trap. Right? Because the next company wants to just kind of keep you around where you are and maybe give you a little bit more. Right? And that is the trap question that keeps the income inequality for men versus women so prevalent. But he got that question. And so he gave a very creatively high answer. Right? Which I don't necessarily recommend. But I think he said something like, I'm currently making $200,000. And he wasn't making $200,000. He said, I'm currently making $200,000. And so the response was, oh, there's no way in the world I can pay you $200,000. The best I could do is $175,000. Is that okay? Right? Which to me, I would have never even dreamed of, you know, giving some sort of number that high. Because I think a lot of times we don't value ourselves to be that. I'm not worth $175,000. You might be saying that to yourself. Well, maybe I'll give them a lower number. Or I don't want them to think that I'm just too greedy or something like that. So I think what helps in those types of situations would be to first of all, understand the market. Right? Understand what a reasonable amount of money would be for that particular role for your area. Like geographic area. And also for your specialty. And for your years of experience. And the worst that they could do is say no. Right? I mean, you give them a reasonable number that is in line with a salary that you can justify. The worst they could say is no. It's a little bit lower. But I definitely just encourage you to, like I said, just think a little bit more outside of the box. And, you know, value yourselves, I guess, maybe higher than potential. Because they obviously have a vested interest in keeping your salary as low as possible. They just want you in the door in the cheapest way possible. So, yeah. I highly encourage you to actually negotiate your salary.
You raise a great point about the prior salary. That's actually illegal. I know it is in Massachusetts now to ask that question in an interview. I don't know how many other states have gone there. But that's the reason why. Is that it automatically kind of drags people down. Especially those who are unwilling to get creative in their ask.
One thing that I kind of learned to do is to have a network of people that I have some kind of, like, open space or open conversation where I can ask the questions. You know, I can say I'm looking for a job. I have this amount of years of experience. And how much do you think I should ask for? And they say, oh, you know, I'm a senior person. I know that my company is paying around this. So, I kind of, like, did a survey with, like, my friends or, like, the people that I know and ask them what is the average or the interval that you think I should be asking for. The other good thing and tip is if you have any friend that is manager or hiring manager, ask them. Because they know a lot about the salaries in the industry.
Choosing which technical skills to invest in
How do you decide which technical skills are worth the time investment to really learn versus having a passing acquaintance with?
I think it would depend on your specific role. And whether or not that is something that is needed for your job or for your growth. So I think about, for example, going back to that list of that massively long list of different types of machine learning tools and software and models and all these different kinds of things. In your particular job, maybe you only need clustering. Like, they need somebody who's an expert at clustering. And they need somebody who understands all the different ins and outs of a very few, you know, a very small number of models. In that sense, you know, maybe it's worth the effort of really digging in and understanding those. But the other side of that, obviously, is that what that does is it keeps you in the expert role for where you are now or maybe with the team that you're currently in. So another view of that would be to say, where do I want to go? What is the type of job that I want to have and the type of career that I want to have for myself eventually?
When I answer that question for myself, I want to be not just the best data scientist at my company. I want to be the best data scientist possible so that any company can look at me and all the different types of experiences that I've had and say, wow, she really understands a lot of different kinds of tools. She understands that, you know, AI, for example, that's the buzz word of the day. And a lot of companies are interested in that. And so she's the person that I would want to call because she has that type of expertise. So I would definitely challenge you to think about your career and the path that you want to go on and figure out whether or not some of these different types of tools will be useful.
Data analyst vs. data scientist
What is the difference between a data analyst and data scientist in your experience? Is there a difference?
So in some companies, there is no distinction. It's more like a title. But like, from my experience, data analyst is doing more, you know, querying the data, getting the data from a database, let's say using SQL, creating reports, where data scientists go one step further, where they also do the data cleaning, managing and querying the data, but also is involved on the modeling itself. So that's my experience. But I think it's kind of like a gray area. Like some companies, they call data analysts what I consider being a data scientist.
It's kind of, I mean, these are all squishy terms, you know. So I would, I guess maybe just thinking about some of the different roles I've had, people who've had the data scientist role, they tend to be, they tend to have a little bit more training, maybe a little bit more education, like formal training in maybe like a math or statistics or physics or something like that. They tend to have a little bit more expertise in some of the more advanced types of modeling scenarios, whereas maybe a data analyst would be very proficient in some of the data engineering tasks or like pulling different types of SQL queries and maybe creating some dashboards or some reports. When I think of a data scientist, I think of a scientist, right? Where you actually have to start from the beginning. Nobody's handing you a CSV or telling you specifically which types of data to look at or what types of models to use and so on. You're starting from scratch and you're able to go through that entire scientific process and figure out where your hypotheses are, testing those, maybe validating those, maybe circling back and going back to the beginning.
And one last thing is data scientists, they usually make much more money. So if you are in doubt, if you should write in your resume that you are a data analyst, a data scientist, go for data scientist.
Dealing with lack of mentorship
How do you handle being in a job with little or no mentorship opportunities?
I've been that person that has the, the, the set of skills, that one particular set of skills that nobody else in the company has. So they literally cannot speak to me about it, you know, on some kind of, not to say that I'm like inherently smarter than them necessarily. Everybody has their own specialty, but maybe I'm the only one that has studied this particular area. And so what I have to do is sort of create my own network. I have just in the course of being, um, a person who's interested in developing my network in Dallas, for example, I go to different events, I go to different talks and, you know, just talk to people and find out what they're interested in. So then when I come up across some kind of problem and I just, like, oh, I don't know what to do or, you know, where should I go next with this? Oh, wait a minute. David knows this. You know, I know I see, he sounds like the kind of person that would have that kind of expertise. Maybe I can tap him. Um, so that's one area where it's, when you think about technical mentorship, but the other path would be just growth for your career, right? So just figuring out, um, I want to be a director someday, or I want to be a technical leader someday. How can I do that? And so some of that is, um, just finding and finding people who have gone through that path that you're trying to, to go through. And that could be within your own company. That could be, you know, for people who work in similar types of roles as you're trying to get into maybe who've been in different companies. Um, so trying to just be creative about finding people, um, that can inspire you and the ways that you need.
Twitter accounts and resources
Can you recommend any good Twitter accounts to follow to learn more about data? Um, well, if you're not already following Mara Averick, that's probably the best place to start. Um, definitely where I think I've learned more than anything. I would say this isn't an account, but the, um, and particularly when you're developing in data visualization, especially the tidy Tuesday hashtag has been really helpful for me to see folks who are doing some really interesting data visualization. Um, that's been a helpful one.
What about the rstats hashtag? Yeah, sorry. I assume that was, uh, but yes, rstats hashtag if you're not already following that as well. The other thing actually, actually looking over at you for a second, Gabby, makes me think that, um, I really like the R-Ladies. Uh, we are R-Ladies rotating curator, um, because it's really fun to see kind of each week. It's a, it's a different person who curates the account and it's really fun just to see the different types of work that people are doing and the different packages they use. So I really like that account as well.
Generalist vs. specialist
I'm fundamentally a generalist in my overall skills and my R skills, breadth, not depth. Is that a viable way to build a career or is specializing essential?
Well, I think, again, there is a broad of like jobs. So you can be a generalist or you can be a very specific, it all depends. I think there is space for everybody for being a generalist and to be a very like domain expert.
Right. So, um, and I think I touched on this a little bit in the beginning where I was thinking about the different types of ways, um, to classify the different types of skills that are required as part of a data scientist. And so I was saying, if I wanted to just be a person that a general data science company would find attractive, then I would have proficiency in machine learning, programming, and big data technologies. And as our esteemed panelists said earlier, some of those companies might not necessarily care about some of those, right? So they might not care about the big data part. Maybe they only care about the other two. But when I think about it again, unless you've already decided specifically, I know that this job in particular is what I want. And I know the specific set of skills that are required for that job. And I'm just going to keep on honing those skills. Um, unless you're doing that, I would challenge you to branch out a little bit more, learn about other types of things, but obviously don't, you don't want to get to the point where, um, you're just picking up every single kind of tool, every different, every type of model or expertise. At some point, you're going to have to specialize because the people who have very specific domain experience like baseball, like, you know, telecommunications and so on, they really understand the data. So they can go beyond just saying, I understand that this column seems to be related to this column. They really understand all the different ways that, um, that extra knowledge can inform how well you can get some insights from some of those different types of projects.
Being the only woman or person of color on a team
How have you dealt with being perhaps the only or first woman or person of color in your organization or team? Do you have any advice for people who might be, in addition to trying to get into data science, also dealing with, um, issues in that area?
So I have been in that scenario from the woman and also the person of color scenario in so many of the roles that I've been in. Actually, I want to say every role that I've been in. And so, um, some, one of the things that happens commonly is that, um, as the one person, you feel like you have to be the representative, right? If I mess up, then now all black people mess up, right? You know, that's just how we are. All women mess up, right? So some things you just kind of have to forgive yourself and realize that you're human just like everybody else. You make mistakes just like everybody else and just, you know, take those as growing opportunities to get better. Um, but some of, some of the challenges are external, some of them are internal. So I definitely would just encourage you to, you know, realize, you know, yeah, I'm a good person too. I'm very smart and, you know, I have a lot of great ideas and good things to offer. And, um, so just kind of keep that in mind as you're growing in your career.
Yeah. Also, like, don't feel like you have to be the one responsible. Like, let's say, uh, there is no diversity efforts and then you are going to be the one doing the whole work because, so like try to get other people to help you as well to have allies. And also the outside network is very important on those times. So you don't feel lonely. So we have this support around you that keeps you going.
Large companies vs. startups for first jobs
For a young data scientist trying to get their first job, should they be looking at large companies or startups?
So I came, so I always worked for startups until moving to IBM two years ago, almost two years ago. So I went from a company where we were 20 people to like 300,000. Uh, it's a big jump. I don't know what is better. I think if you are a beginner, the best company is the company where you're going, again, you are going to have support mentorship because it's so hard to break into this, you know, career without having someone mentoring you. So if there is a startup that you have a good network and support, yeah, go for it. But if you have opportunity to work for a big company, you probably have more mentorship, but also like you are going to be more specialized than a startup where you do, you have to do everything from getting the data, cleaning the data, data engineering to modeling and putting production, for example.
Can I also just give a pitch for the idea of potentially considering consulting? Um, because I think it's a really good way that you can gain experience. And, um, you know, even if your ultimate goal is to become, say a data scientist, I think there are a lot of opportunities to use the types of skills that you have as an R user. You know, outside of that, that title, um, in ways that can really be impactful for the organizations that you work for. I mean, just as a, as a minor example, um, one of the clients that I was training recently, um, was working, doing an evaluation of, um, an afterschool program and they were working at a hundred different schools and they had to produce literally a hundred reports. I taught them about parameterized reporting and it just like completely blew their mind. The idea that they could, you know, automate that and not have to manually produce a hundred reports. They're not a data science firm that, you know, that's not how they identify themselves, but those types of skills can, can be really useful and serve a wide variety of organizations.
Um, so I started out consulting, um, the honest answer is because I had twins three and a half years ago and I wanted more flexibility, um, than in the job that I had at the time. Um, I started out finding clients. I've mostly done online marketing. Um, I, I offer online courses as well as trainings for organizations. Um, and, um, I've tried to basically become a resource for, for other folks. So I write a lot of blog posts, really pitched it like newcomers, you know, learning about packages to make, um, effective tables or that type of thing. And that's really been my most effective strategy to reach out to clients.
