Data Science Hangout featuring all of us!

Hi everybody, welcome to the Data Science Hangout. I'm Rachel. If we haven't met yet, I lead Customer Marketing at Posit. I'm so excited to have you joining us today.

The Hangout is our open space to hear what's going on in the world of data across different industries, chat about data science leadership, and connect with others facing similar things as you. We get together here every single Thursday, unless it's a holiday, at the same time, same place. If you are watching this as a recording in the future and you want to join us live, there will be details to add it to your calendar below. Just a note to make sure it adds it for 12 to 1 Eastern Time.

Is this anybody's first Data Science Hangout today? Say hi in the chat because we'd love to welcome you in and say hello too. We're all dedicated to keeping this a friendly and welcoming space for everybody and love to hear from you no matter your years of experience, the languages that you work in, titles, or industries. It is totally okay for you to just listen in here too and maybe be a part of the party that happens in the Zoom chat.

There's also three ways you can jump in and ask questions or provide your own perspective. Hopefully there'll be lots of people providing their own perspective today because you'll see we're doing things a little bit different today. First, you can raise your hand on Zoom and I will call on you. You can put questions into the Zoom chat and just put a little asterisk or star as I call it next to it if you want me to read it out loud, otherwise I'll call on you to introduce yourself. And then third, we have a Slido link, which I just remember I forgot to create for today, so I'll make that as we go, but you can ask questions anonymously there too.

Two notes to share just before we jump in. Posit conference is happening in August in Seattle, and I just want to make sure nobody misses the announcement that registration is live, so if you are interested in joining us, Curtis will share the link in the chat where you can learn more about the conference. And then also I am on a mission this year to help connect people who are using Posit across their own companies. We have a LinkedIn group for the Hangouts, which you can sometimes find people from your own organization, but I'd also love to help connect you, so if you want me to help introduce you to people across your organization who we maybe are already working with, feel free to use this just simple Google form I made.

But with that, thanks so much for joining us today, and as I mentioned, we are doing things a little bit different today, and so what that means is today there's actually no single featured leader. You all are the featured leaders today.

So last year we had a Hangout where this leader had to reschedule last minute, and we had this open week, and it turns out the Hangout crew really liked that format, so we said once in a while we're going to have these open sessions where everybody can jump in as the featured leader and share their experience. So if this is your first Hangout today, you're going to see this is a little bit different than how they normally go. Normally there's one featured leader here sharing their experience, but I'm really excited to hear from all of you today.

And so I was thinking I'll kick it off with a topic, but I want to see from all of you in the chat, too, what are some things you want to talk about today or maybe dive deeper into things we've talked about in past weeks. We sometimes refer to this as chaos mode as well.

Networking and building relationships remotely

The first thing I wanted to chat about is I know the value of relationships has come up so much in different data science Hangouts, and we talked about this with so many different leaders, and so I was curious to hear from you all. How do you network within your own organizations, and how do you build relationships with people across the company? I think especially as so many of us work remotely, this can be something that's really hard to do.

I see Jamie had something you put in the chat. You want to jump in and share that?

Sure. So, yesterday, my law firm had speed networking, kind of like speed dating, but not dating, and I think this just goes to show that in this all-remote universe or remote-by-default universe, they really have to try hard because they realize people didn't know each other anymore, but the only people who showed up, with a few exceptions, were people from my floor who all knew each other, so that was kind of fun. But, you know, people are trying, but I mean, there's way more happy hours and things like that going on, but not everyone goes to those. And, honestly, I feel people get to know each other more at conferences, like if everybody goes to a conference, you actually get to spend more quality time with your peers or people at your company when you're out in the wild, so that's kind of another thing I've noticed post-COVID.

That's such a good point, because I was thinking about that, too, when I used to be in sales for Posit, I would travel a lot and go to conferences, and you got to go with, like, one other solutions engineer or sales rep or customer success rep, and you had so many more experiences with them by getting to travel with them, and I really do miss that part.

Tomas, I see you have your hand raised. Want to jump in?

Yes, and I'm not sure if there's anyone from FDA on this call right now, but we have this, and it's kind of an interesting experience, because we started just as we went remote because of COVID, we started this sort of an informal Teams channel that was not supposed to be work, it was kind of just like a hangout, we would, like, just share nonsense and just talk about anything, and memes and whatever, right? And it became a really useful venue for this kind of stuff. It's not, I think, it's not, you can't have too many people on it, but you can have, like, you know, a good amount of people that are disciplined enough to operate in that kind of space.

I think it's a little, the problem is that it's a little, there's a generational gap, so you don't necessarily, older people are not comfortable with that kind of interaction, so we're kind of, like, maybe struggling with that a little bit, although we don't, like, look at it specifically, but it's been really useful to just have something, have this channel open all the time, and it's, like, very immediate, and when you're in the middle of something and you're chasing a bug and you're frustrated, so you just complain there and you start a conversation and someone's going to come up with something, but I think the secret is that it's totally not structured.

It's really just transient, and there's no record of it, there's no expectation, and to me, that was really useful, and it really increased my circle of people that I, you know, can interact with if I need something more specific. And I know that, you know, it's sometimes difficult, especially in industrial and government frameworks to justify this kind of, because people will see it as a waste of time, but I think that kind of unstructured communication is super important. The moment you put a structure over it, an agenda, and a treasure or whatever, then it just kills it, but when you have it like this, open, transient, it can be super useful.

The moment you put a structure over it, an agenda, and a treasure or whatever, then it just kills it, but when you have it like this, open, transient, it can be super useful.

Yeah, no, that's great. The experience, you have to have people that are comfortable with using the Teams channels and also comfortable with having that kind of conversation and not take over, because you will have people that will just take over a channel, and so you have to be disciplined in that. But other than that, I think that's a really good venue for this kind of communication.

Definitely, and I was just going to confirm, you said it's a Teams channel? This is a Teams channel, but it could be, you know, it could be Slack, it could be whatever you guys, your company or your workplace is using.

I see a few other hands raised here if we want to keep going on this topic. Steve, you want to jump in next?

Yeah, thanks, Rachel. I was just going to share that our department has a spreadsheet and the statisticians randomize anybody that wants to be involved, you put your name in, and then within the whole list within the department, they randomize each other together on a monthly basis. So every month you get a new person, you can schedule a 15-minute or a 30-minute call and just visit with each other and get to know each other. So that was one way that our department was doing that. We need to do it across departments, I think, too, but within the department, I mean, we've got a big department, so there's a lot of people that I don't know, and that gives me a chance to meet with them.

Absolutely. Arati, I see you mentioned you have like a data science Hangout internally. I'd love to hear about it.

Yeah, we actually have multiple groups in here for each organization in the company. They started something called the Data Science Study Group, and people would just get together and from various departments, whoever is wanting to transition into analytics or data science, and they would share goals, and there used to be case studies on how they did data science in their own teams, and it was a really good forum to exchange knowledge, for sure. And I also see that there are some guilds for knowledge exchange going on now. They meet biweekly, and they usually feature a guest speaker who comes in and talks about the model that they're developing right now, and they kind of brainstorm about it, and it's pretty cool.

Hey, Rick. I see your hand raised. Want to jump in?

Good morning, Rachel and colleagues. When I was an industry practitioner for four decades, I found lunch to be the most productive way to exchange ideas, so for example, I was with a major consulting firm, and those of us involved in these kinds of activities would meet for lunch maybe once a week or so, but we would discuss models and issues we were having with modeling, and it was like taking the right approach. And I remember one day having a very vigorous discussion about should we have log charts, log log charts, or just regular non-log charts as an example, which would be more effective in communicating the story we were trying to tell. And we would talk about how to resolve data wrangling issues, because rarely is the data sets coming out of transactional data stores useful for us in analytical settings and things like that.

Then, when I retired the first time, I joined the faculty of a university, and I became a professor for another decade or so. And about 10 years ago, we started something we called the Data Science Initiative. Now, I think it was Tomas that spoke a few moments ago from the FDA. I'm going to respectfully disagree with Tomas. For the Data Science Initiative, we wanted to reach out to the campus and our community, because there were technically oriented organizations in our broader community. And what we did for years while I was there, until I retired the second time, we would host a once-a-month kind of tutorial about some topic about modeling or analysis or interpretation.

We are our evangelists, I want you to know that. In fact, I have a great picture with me with Hadley Wickham, and we're doing the American Sign Language of R. We were great R advocates. We were trying to equip others to use these incredible tools. But we used the tutorial, about 15 to 20 minutes, as a draw to get people to show up. And then we would spend at least an hour, hour and a half, one-on-one, you know, talking with people about their specific problems. Things like that, very practically oriented problems. So that was our approach, and generally I would agree with Tomas that having a rigorous scheduled thing might not work, but we used it as a draw to get people there. So they knew they would get some practical little tip like on RStudio or something.

Yeah. I think especially for like community groups too, I've seen that having a bunch of different options works well too, like having the Teams channel, but then having the recurring meeting as well. Because people want to engage sometimes in different ways.

Eric, I see you just raised your hand.

Yeah. Just kind of a general comment in terms of like meeting people and stuff and talking at work is when you work from... because in general, like you come to work and like no one can just work nine hours straight like with zero breaks. But when you're in an office, the alternative to work is usually like going to grab coffee or having lunch or something. But when you work from home, the alternative to work is like maybe you live with someone else who works from home or going for a walk. So I've noticed I have to be a little more intentional about communicating with people and being like, it's okay to go off topic on a Zoom call for 20 minutes because we're really just like... Because when you're actually at the office, those things just kind of happen organically. So that's just something I've noticed that you have to be more intentional about making sure to do it. Because if not, you could just do work and then not talk to anyone about anything outside of work because the alternative is like you're already at home, so you can just do other stuff.

Yeah. That's such a good point too. And like reminding yourself that you would have walked around or taken a break at the office. You don't have to just be sitting in your home office.

I want to encourage people, if there are other topics that you want to cover today or questions you want to ask everybody here to just post them in the chat and Curtis will help me grab out the questions too. But I did want to share something we do at Posit in case it's helpful for people here too. We have these virtual coffees with our president and they started, I don't know, I've been here a while. I've been here six and a half years now. So they started a few years ago, but people can just sign up to join the virtual coffee and just ask him questions or get to chat with people you might not normally work with. And so it's open to everybody across the whole company. And there'll be only like five or seven people in that format. And so I found it really nice for getting to meet people in different departments and just getting to hear it's something they're interested in and we'll end up working on a project together because I learned about something from them in those spaces.

And so I kind of took that idea and started doing it with a few of our customers and our president Tarif as well. So occasionally we'll have these just virtual coffees with five or seven people from the community as well.

Arsene, I see your hands raised, want to jump in?

Yes, ma'am. So just wanted to throw in a couple of things that I've done. So one that I was a part of when I was at AWS, there was a, they had their own internal messenger and all that, similar to Slack. And so they had a bunch of different rooms that were for either location, you know, what location you were working out of, what type of role you had and hobbies and all sorts of stuff. So that was one.

Another experience that I had was I was actually coordinator of the R users group at the U.S. Bureau of Labor Statistics for a few years. And I did the same thing at Deloitte for a bit. But at the BLS, one of the things that I found really useful, you know, we have all these tools, Lightning Talks, you know, Brown Bag Lunches and all this. One day I just rented, I just reserved a conference room and I said, hey, you know, I'm inviting everyone to just come in and let's just code together. You don't have to talk, you don't have to do any of that. Let's just sit and code. And it turned out that like three or four projects came out of the first one. Because people, one, I remember one instance, it was somebody needed help debugging and they just, they ended up, three people ended up chatting, three or four people, and a project came out of that.

So I ended up doing that a few times and that was really useful because one of the things that I found with other types of networking events is that there was this social pressure that seemed to be there, that people had to talk or participate or something. And some folks just didn't want to do that, but they wanted to be around other people that were coding or doing something.

So I guess maybe before I skip to the next question, I saw there was a discussion happening under I think Jenna's comment about office hours. Jenna, would you want to share that?

Yeah. So I'm a data scientist on a global advanced analytics and AI team, and we started a few months ago hosting weekly office hours and it's open to everybody in the company. And so sometimes we'll prepare like a specific topic or presentation just to kind of get the conversation going. But it's been a really good way to meet new people that we haven't worked with before. We've had a lot of projects that have come out of it and a lot of people that were wanting to learn more advanced analytics skills, but just weren't really sure how to start. So maybe we could help them with their Python skills, for example, with an applied project in their role.

This morning we had one specific conversation on AI risks and like data privacy. So we'll do like specific topics sometimes in addition to just sort of open hours where people come with questions. But I think the key has just been to make sure we do it consistently. And we're looking to maybe expand since we have employees globally. So maybe every other session hosting at a different time to catch people in different time zones as well. But it's been really a lot of fun, a great way to meet new people, help them upskill and also come up with new projects for our team as well.

Transitioning from SAS to open source tools

So the question is, my org is moving away from SAS to open source tools. Anyone have experience helping SAS folks and people new to programming upskill in R or Python? And if you had to pick R or Python to focus on, which would it be?

Sure, yeah, I was just mentioning that, so I work at the public health department at Oregon Health Authority, and so we're doing some change from SAS to R. I think the thing that's been really interesting is kind of like meeting people where we're at. The transition from SAS code to R is like better in some projects than in other projects. So there was like a lot of buy in for converting projects where we were doing like a quarterly report. And that was like really easily converted to R Markdown and made like formatting a lot easier. But like some things we haven't been able to transition over. And I think, yeah, meeting people where they're at, figuring out what people want to upskill in, I think has been really helpful for us. We obviously picked R.

So I think that like in my experience, so SAS programmer for 25 years. Led my company, was on the steering committee of my company, SAS User Group, for four or five years before I stopped a year or two ago. I think that if you want to, if you what you really need to take into account is that SAS being in today's terms, ancient in a sense, and that's not a dispersion, I'm just being silly. SAS being ancient, you may have a lot of folks heavily invested with years of experience without any other, without really any other language background and crossing that barrier can be very difficult, whether it's R or Python. I don't know if either language, doing both, having done both, I don't know if there's any like one is better than the other to transition folks from SAS, from just like a syntax level.

You know, the jury's been out for me in terms of when I have like SAS co-workers who I've tried to either evangelize R or Python to. I find that neither language has a great, you know, acceptance rate for someone who's been very invested in the other, in SAS. My experience with R and Python and which one to choose, if you're working closer to a production line, Python, I think is better because I think Python having a longer background and broader background in a wider space. It kind of replaced Perl as a systems language. And so I think if you're working closer to the production people, Python might be better. Whereas R, if you're more in an R&D role and you're not really rolling your stuff directly into production, there's no reason not to use it.

I work in an organization that transitioned from SAS to R in the past couple of years. But that aside, I've had to teach Python, R and SAS at different times. And what I find is, yes, when people get invested in one language over the other ones, they kind of like have tunnel vision and they don't see anything else. But I also find people who do SAS, the way their brains are wired, it's really a lot easier to transition them to R. Yes, you'll find that one or two, you know, who will take Python pretty well, but most people will transition, you know, almost seamlessly to R. And you see light bulbs go off in their heads and they go like, Hey, I mean, I could actually do this in like one line, like t.tests replaces, you know, whatever else I would have done in SAS, you know? So I find it's easier to transition people from SAS to R. Like I said, it's not general for every single person, but for the most people, trust me, R is the way to go.

Yeah, it's, I appreciate you saying that because the, the thing I hear a lot of people might work, say SAS is my gateway drug into programming. And it's in a lot of ways, SAS is like the worst place to start with programming because it abstracts away so much of the actual programming. But you know, I did computer science stuff later and everything made a lot more sense. I spent a year working in R and that was a bit more comfortable. And then I moved to Python just, just because of my tech environment. But I hear a lot of people coming from SAS say that R is like, quote, less scary. And like the syntax is a little bit more approachable. And the fact that, you know, RStudio is so great. You're just in an environment that feels much more comfortable.

The problem is, I think from an enterprise level, you need to sort of build some more infrastructure, I think, for, to get R working at enterprise level. But that's, I'm glad to hear that you, that, I appreciate that experience because I'm hearing that a lot too, because we have, we have a lot of folks that are just, they're cooked in, they got 20 years in R, just like the guy before you said. And they're just, you know, we're trying to figure out where to get them next.

So I just wanted to, uh, say, you know, it doesn't have to be one or the other. I mean, when you have a, like a woodwork shop, you know, you've got all different kinds of saws and, you know, whatever is best tool that you need to use at that point, you can grab and use. And, um, you know, and I don't know if a lot of people know this, but with, with SAS Viya, there is, um, you know, in their SAS Studio interface, you can program, um, in different languages in there. So you can, you know, do Python coding within there. And it's part is as part of the Viya install, SAS Viya install. Um, there's just some flags that you can specify and say, Hey, install Python and R as part of the install. So, you know, there's no need to, you know, lock yourself in just one vendor when, you know, some situations, you know, it may be better to use one, one solution over the other.

I guess I can't leave this question without having you jump in here, Tomas, with your FDA background, uh, showing up here.

So, uh, I'm not going to rehash everything that was already said. I just want to say that, um, I'm a, I'm a grant reviewer for the, for the NIH, uh, for extramural grants. And usually in, in biostatistics and clinical trials and stuff like that. And, and usually if a grant has to have a, um, a, um, a statement about, about sharing of the resources, which include code, and usually it's, it's, it's expected that there's going to be an open source code that's going to be shared. If there's a new method developed as a, as a pack, as a package or as a, as a, and usually it doesn't make a difference whether it's a, it's a Python library or a, or an R package, but it's usually between R and, and Python. Um, and if, if a grant doesn't have that, then it gets a ding. It gets, it gets scored, score subtracted. So there's a great incentive from that end, from the, from the academic end, uh, for R and Python.

And I will completely agree that it's more a matter of culture right now, whether, whether you choose R and Python, because they really will do the same thing. The only thing that I can, I think R will probably not be not very good is if you want to like control a robot or something. I'm not sure if there's R packages for controlling a robot, but, or things like that, like that kind of application development. Um, so, uh, I came from SAS and Perl or, I mean, I came from Fortran originally, but, uh, the transition from SAS to, to, to R was all through, um, you know, stack overflow and kind of like self, self, self learning. There's so many, no one can complain that there's not enough resources to learn R. You can go on YouTube and start with the basic tutorials. And if you want to build, we at the FDA, we have sort of an upscaling library that we keep and we direct people to people that are completely new to coding or want to transition to Python and R, we, we kind of funnel them through the, the basic courses because before they join the discussion on like real data and stuff like that.

Eric, I see you had a few thoughts here too.

Yeah, I couldn't resist this one. And for those that I will do a quick plug, you definitely want to check out the back catalog of the Hangouts, because we've had a few of us from the life sciences comment on this from time to time, but I'll reiterate some points I've learned over the past month since I was on the Hangouts, being accepting that the fact that the way you as a SAS programmer, if you're coming from that, the way SAS thinks is absolutely different than the way most open source languages would think, and that's not a negative or a positive, it's just different. So being able to embrace that mindset is for step one, it's kind of like acknowledging that there is a difference.

I will say that you are seeing many more resources, at least being spun up, and certainly in my industry of life sciences, we're transitioning to R itself based on ecosystems like the Pharmaverse, ecosystems like the Tidyverse that are pretty approachable to many of the SAS programmers that are coming from the data set processing and whatnot. So I think you're going to see a bit, and again, nothing against Python in that regard, it's just, you're going to see more stuff approachable, I think, to get you started, but also don't get into the trap of trying to replicate absolutely everything SAS does to a T, embrace that there are going to be differences in some of the statistical methodologies, and that's not a bad thing, they're, again, just different. But so some people get in the trap of, oh, I didn't get to that one hundredth of a decimal point accurate. No, no, that's not the right way to think. The right way to think is the analysis makes sense for your given problem.

The only thing I'll take a bit of umbrage to what Chris said earlier is that I think R is most definitely in production workflows, especially in the industry I'm in, and frankly, to be perfectly candid, we've had now multiple pilots to the FDA themselves on transferring mock trial submission packages, and they were all built with R all the way, and they had no issue with it, so I think as an organization, if you're set up for R in production, that's a different matter. Once you're set up with it, I think you can do just as many wonderful things in production as you could on the Python side, so I think either way you go, the production thing should not be an issue in my opinion.

Skills from non-data jobs

So the topic that I have been thinking about a lot lately, because the job market is kind of awful. And a lot of people that I know have been transitioning into retail food service, um, areas where they feel like they're going backwards and I don't think that's going backwards because that's where, you know, a lot of people come from, um, those jobs taught me so much. So I'm wondering if anybody has anything to share about, um, the experiences or the skills that they learned in non data jobs that have shaped the way that they work now. And for me, I'll say being a server waiting tables, um, really taught me how to talk to people that I don't know and forced me to, cause I'm a huge introvert, um, but I learned a ton being a bank teller. I learned a ton working in technician roles and engineering and architecture. I did AutoCAD for a long time. That was my first command line interface was AutoCAD. Um, and so those things really shaped who I am, even though they have nothing to do with data. So I'd like to hear some other experiences.

Um, yeah, what I mentioned was that I worked at a writing center in college. Um, and basically that was just helping other people with their essays and, uh, basically helping those get better. And I think that's greatly like shaped my written communication at least. Um, there's obviously like a customer service element of that as well. The other one I mentioned was working at a farm market for a while, which is directly customer service. Um, I definitely dealt with a few difficult people at the farm market. People go crazy for fruit. Um, but I think that's translated well to working with stakeholders or clients. Um, and basically coming to solutions that work for everyone.

Travis, you want to jump in?

Sure. I was a long time construction worker. Um, and then I went down the HVAC route. So working on air conditioners and heating systems, um, because it was very logical and technical in nature. Um, just like writing code, you know, building stuff, you go, you walk in. Something's not working. You've got to find some documentation about like, why did someone put the blue wire to the yellow wire and they probably didn't document it and then it's hard. And then you learn not to do that yourself. Um, yeah, that's very much like data science.

Rohan want to jump in?

Uh, yeah. So I'm a statistician by trade, but my side hustle is a little bit different. My side hustle is actually as a powerlifting coach. So I coach people online with, uh, weightlifting training, things like that. And I find that there are a lot of intersections in communication. So one thing that happens, I'll explain a concept with, uh, with just movement or kinesiology. They won't get it and I'll have to explain it in another way. That happens a lot as well in my day-to-day job as a statistician, I will teach people about a model. They don't get it, uh, communicate in different ways and they finally do get it. Another thing that comes up is with powerlifting training. Uh, you want to change as few things as possible and see how the trickle down changes, what the trickle down effects are. A lot of that happens with my work since I do a lot of research.

Chris, your hand's raised. Want to jump in?

Yeah. Um, just as kind of adding to that. So my, my original background was, uh, I was an anthropologist for a while. So very qualitative, very, very far from programming anthropologists say, you know, statistics. Um, but a lot of that background really isn't, you know, like passive observation, a lot of stakeholder engagement, a lot of cross communication, usually bridging multiple camps and, uh, just developing a lot of patience really. And hopefully just kind of teasing out common threads. So that's been generally pretty important as far as being able to actually effectively communicate between all the different departments that the data has to get translated to. Um, you know, some patients actually with just understanding, you know, what really is a need, because what someone asks for isn't always necessarily what the actual nature of the need is that they have. They'll ask for something cause they think that's what's available or, you know, what, um, kind of the previous person gave them.

Absolutely. Libby, it sounds like you have a lot of potential guests for the data humans podcast right now.

I love Azuka's comment there in the chat. I was a volunteer for a hospital in my community when I first started in college and I thought it was a relatively positive experience I got to be able to help patients navigate their experience on campus. Hospitals can be scary and hard to understand when you're especially vulnerable and the experience gave me a new perspective on healthcare and who the faces are of the data we process from patients as they enter and leave our hospital networks.

Darren, I see your hand raised. Want to jump in next?

Yeah. So, um, as an undergrad, I was in my time as an undergraduate student. Um, I worked at the IT help desk. Uh, so helping students, faculty, and staff with their technical problems. And this is my first, like actual job. And it was also my first time, like interacting with people that I didn't really know and getting myself out of that shell of talking to people and listening to their problems, communicating technical things in a non-technical way. Um, writing emails in a clear way tone, right? All those things are so important in data science too. And most data science roles, cause it's all about communication and people. Um, and, and so that was, that was really helpful for me. And also just the IT side and learning all that stuff, um, kind of was a gateway to learning more about systems administration and Linux and, um, you know, Mac windows, like I, I, I can, I'm comfortable on all of those systems. And that's, um, been very helpful in managing the Posit infrastructure I have today.

Keeping up with new tools and resources

So I see a lot of great comments here in the chat. I think I'll, I'll jump to another, a question I had wanted to ask everybody, but I think it ties into a question somebody put in the chat a bit earlier. Um, but I know we've, we've talked about in Hangouts that it's kind of hard to keep up to date on new things in data science or in technology in general. And so I was just, I wanted to learn from you all, maybe you even just want to put in the chat. Where do you go to learn about new tools today?

I know a lot of people used to turn to Twitter, maybe aren't using Twitter as much, and I know some people like rely on medium articles or coworkers or friends. So just always curious to hear what, what people are using.

Rick, I think you had a kind of a follow-up question on this, perhaps, maybe a little bit different.

Yes, thank you, Rachel. Yeah, I'm curious and would welcome input from all of our colleagues here. If you could type into the chat, what data science-y types of professional societies, journals, maybe websites, podcasts, which we've just learned about, et cetera, do you rely on for practitioner-oriented information? I'm getting, many of my former students are asking me for this and I'm going, well, you know, these are the ones I use, but I'm sure I've missed out. So if any of you would be so kind as to suggest in the chat, a data science-y resource that you find especially helpful, journal, articles, professional societies, whatever, that'd be enormously appreciated.

Eric, want to jump in?

I promise I'm not just plugging the podcast. I want to sympathize some other advice I'm seeing from other people too. It's, I think in this new age of, especially since COVID, the pandemic of, you know, more tools around these virtual communities, I think pointing people to some of these communities that do have that focus on data science, but yet different aspects of data science, whether it's like the networking piece, technical help, learning together via book clubs. I mean, certainly I can't resist plugging, you know, John Harmon's newly renamed group, which I literally just forgot. It's a new, it used to be R for Data Science. It's a different name now, but he can chime in with the name of it. That's one great place to go.

But there are other communities, whether they're on Discord or Slack or whatnot, that I think are, you know, they're opening up some of these newer perspectives, but you are seeing some, I'll call myself an old timer, trying to frequent some of these too, to really get in touch with people that are just starting their journeys. Because sometimes I lose that perspective of what it's like to be new in this field. I've been in the industry for now almost 15 years. That's changed so much since when I started working. So, I think, you know, reaching out in some of these other communities, it may not necessarily be a journal focus or a formal type setup, but they're very much collaborative platforms. And also, I think Mastodon is starting to get a bit more traction on the data science side as well. A lot of the people I see on here, I'm following on Mastodon as well.

Greg, want to jump in?

Yeah, just for podcasts, any of the ones that have puns in the name.

Matt, want to jump in?

Yeah, we, my company, we work in a very clinically regulated data environment. So, one thing that we rely on a lot, our data team, both of us are members of the Society for Clinical Data Management. And so, it's not as much on the programming side per se, but it's really good to kind of keep up what current thinking is in terms of data management and clinical data science. And then taking that and helping them apply it into the programming or do one of them. It really has been helpful for us.

Rachel, may I make one more remark? Yeah. I'd certainly like to acknowledge the medium forum, I think. I'm not sure what the correct term is. It's called Beyond Data Science. I'm on that almost every single day, always learning great things. If any of you are contributors to that, thank you. That is a very valuable resource. I'm just looking for suggestions in the Macedon group. Eric, that was great. I had not thought of that.

Alan, I see you had a follow-up question here on this topic. Want to jump in?

Yeah, sure. Thanks. I think my thinking about it was just how to balance that. We want to learn. It feels really important to be continually learning and being overwhelmed by the number of resources that are out there, of communities, of slacks, of discords, of forums and stuff. And so, maybe partly just sort of throwing out a line for validation to how hard that is. And maybe to make the comment sort of constructive, I'm finding more and more that I'm leaning on my team members to bring stuff to me, and that has its own kind of reward in sort of if my role is pivoting a little bit to be more enabling of them, then to the extent that they're finding things, they're surfacing, they're bringing them forward, then that's also a kind of success. And I can be really encouraging and enabling that and realize that when I can't be super focused in those kinds of spaces, I can still be helping the folks in my team get rewarded by finding new ideas, finding new tools, bringing those into the team, and we can bring those into our office hours, into our forum, et cetera. And that can be a really nice way to find things at maybe a more manageable scale without feeling like I just have to be like drift-netting the internet at all times.

And that can be a really nice way to find things at maybe a more manageable scale without feeling like I just have to be like drift-netting the internet at all times.

Mark, you want to jump in?

Yeah. I've actually, I find myself getting into like the, I think it was Patrick Tennant who gave like a talk at Posit Conf last year about like the fear of missing out on like awesome projects or whatnot. I find myself falling into that a lot. So my solution to Alan's question has basically just been to try to ignore as much as I can and just basically focus on whatever it is that we need to do within the org and then like, you know, learn what's needed as it comes up, right? Because like what you see out, and I'm guilty of this too, of like, when you share something like, hey, I worked on something really cool, you share that out with other people, right? You share that out with the world and it kind of hides like the 99% of the time where it's like, okay, I had to like figure out how to clean whatever wonky data set and that wasn't fun, but let me show the output, which was really cool.

So I don't know if that answers Alan's thing, but just another also like public statement of like, yeah, like it's, there's, that's a very real feeling, the fear of missing out there. And I, I think it's also to, it's so helpful to try and find ways to connect with people who are outside of your typical network as well to like learn new ideas from them. We went to this like data content creator meetup in Charleston and I got to hear about like all these different tools that I wouldn't be hearing about normally in my everyday work. And one of them was, is actually this AI tool for making clips automatically, like short little clips. And I was thinking, how can I go and use that for the data science Hangouts? And so this week I've just been like experimenting with trying to make a few YouTube shorts from the data science Hangouts. So there's only three of them right now, but if you want to check it out, I just put the YouTube playlist there in the chat.

And Jamie, I see your hands raised when you jump in.

Well, I just wanted to mention as much as people are really interested in groups to meet up to find out what's new and exciting in data science. I think we also have to remember that for a long time, there is this issue with sort of like things breaking because of new versions, right? And so there was this big movement to come up with like stable package repositories so you could learn, so you could always load the same version across everyone who's trying to compile your code. And rest in peace, Microsoft repository. And, you know, Groundhog sort of took the place of the Microsoft repository. But I think like there are some things like when RacePredict first came out, I was like, oh, that's so cool. I want to use that right away. But like for the most part, I want other people to use things, find out if it's stable, find out if it's buggy, that sort of thing. And I don't want to be on the cutting edge unless like maybe there's no other way to do it, in which case I'm all in. But like I was using like it took me a while to convert from Plyer to dPlyer, right? But dPlyer did so much more than Plyer ever did. So like that was easy. But I think it is sort of like interesting like workarounds, techniques, like the meetups, the value I get from the meetups are like how to deal with the reality of computers that don't have an infinite amount of memory, that kind of thing.

And thank you all so much for jumping in today. I always get a little bit nervous for the open weeks because I don't know what to expect. But I can't believe how fast the hour goes by. And it's so nice to get to hear from so many people too.

If you felt inspired to maybe one day be a featured leader, feel free to reach out to me directly on LinkedIn. But I just wanted to say thank you all so much for taking the time to join us today. And I'm really excited to have Greg Schick here with us next week as our featured leader. So Greg, nice to see you here today too. But have a great rest of the day, everybody.