comet-ml-ma9.mp3 Speaker1: [00:00:09] What's up, everybody? How's it going? Welcome. Welcome to Speaker2: [00:00:12] The comet and our office hours Speaker1: [00:00:14] Powered by the Theartistsofdatascience@gmail.com and have all of you guys here it Speaker2: [00:00:17] Is, Speaker1: [00:00:18] Sunday, May 9th. Happy Mother's Day to everybody in the audience. I don't think anybody in this room is a mother, but you might have mothers in your life. So I want to wish everybody happy Mother's Day. Speaker2: [00:00:29] It was Speaker1: [00:00:29] My son's first birthday Speaker2: [00:00:31] Yesterday, so that was a fun experience. Speaker1: [00:00:34] We did a a drive by birthday party because of the restrictions. People can't really come over and party and we can't really host an event. So we just had people Speaker2: [00:00:42] Drive up and passed out goodie bags and treated those up for, uh, for some gifts, I guess. But it was a good time. So shout out to my son. Happy birthday. Hi, guys. Speaker1: [00:00:53] Welcome to the officers. How's it going? Got Kevin Martin Speaker2: [00:00:56] Tours in the room. Guys, how's it going? Speaker3: [00:00:58] Yeah, it's another day. Speaker2: [00:00:59] Yeah, right. Speaker1: [00:01:01] Well, if you guys got any questions at all, go ahead. Let me know. Speaker2: [00:01:04] Go ahead and go for it. Kevin, how's it going? Speaker1: [00:01:07] You're still not part of datastream. Did you try to log in there? Speaker3: [00:01:12] No, I haven't tried logging at all. I was disconnected when I couldn't pay anymore. I've been unemployed since April of twenty twenty, so I have not been able to get a job anywhere on it. So it's been crazy. So yeah, I mean, I took the Google Analytics course. I've taken I mean, I continue to try to learn everything I can. Speaker1: [00:01:35] So how did you find that Google Analytics course? Speaker3: [00:01:38] I don't remember. It's all now. I mean, because I follow a lot of people on Facebook and stuff that are in Data sites like you and Kyle, and now it's super Data Science Club and a bunch of stuff. So I'm sure it came up one place only thing I haven't done like my capstone project. Shame, shame, shame. Speaker1: [00:02:01] So what type of roles are you out there looking for? Speaker3: [00:02:03] I'd like to be Data manager. And the reason is, is I have enough technical knowledge and understanding of all the technology involved was a DBA for eight years. So I understand database's. I understand the data and modeling and all that kind of stuff. So I understand enough. And then I've been a project manager, scoutmaster for twenty five years. I'm used to building teams. I build up things really well. I feel I'd be a great team leader for Data science. I don't want people that can just lead teams. They want people that can do all hands on stuff and that's not me. Speaker2: [00:02:38] So yeah, I Speaker1: [00:02:40] Mean Data management, Data governance, all that stuff is Speaker2: [00:02:42] Super, super important. Part of the process. That's like the entire Data strategy kind of stuff is very important. And we need people who know Speaker1: [00:02:52] How to do that Speaker2: [00:02:53] Because a Speaker1: [00:02:54] Data scientist can't really do their work without Speaker2: [00:02:56] Having good data. Right. So that's one thing I've been really working on Speaker1: [00:03:00] At my current job over the last month or so is is the data management type of stuff. So it Speaker2: [00:03:05] Begins with just interviewing a bunch of Speaker1: [00:03:07] People internally, finding out what they Speaker2: [00:03:08] Want and what they need and trying to deliver on that. But what's the job search process currently like? Speaker1: [00:03:14] Like what's where are you targeting? How are you targeting jobs where you mostly look at Speaker3: [00:03:18] My resumes on those monster and LinkedIn and I have been both just applying for jobs posted on LinkedIn or somebody has posted an announcement for job and paying them and talked to them. And I have tried everything and anything. You know, I now have like five resumes to try to make sure that they're targeted to the jobs and stuff like that. Speaker2: [00:03:45] So what's your Speaker1: [00:03:47] Search process like on LinkedIn? I do like keyword searches or is it just a. Yeah. So I guess let me let me try to reframe what the question or what the what the issue might be. Is it a like a are you able to find jobs that you feel are interesting Speaker2: [00:04:02] To you that you want to go for. But they're, they're not converting Speaker1: [00:04:05] Into like interviews and opportunities Speaker2: [00:04:07] And things like, oh you know, Speaker1: [00:04:09] So explain what you're which processes like. So you're messaging people on Speaker2: [00:04:12] Linkedin, sending Speaker1: [00:04:13] Out the resume Speaker3: [00:04:14] And applying for jobs. Do I mean going through the application process? If it's a job I really like and then I'll go ahead and ping or email the person that posted the job to give them a little bit more about me or stuff like that. Speaker2: [00:04:29] So but and how is the response rate on that zero. Speaker1: [00:04:35] Yeah. What what do you say in your in your message, have you tried testing different messages Speaker2: [00:04:39] And and Speaker3: [00:04:41] I haven't, I haven't done that yet. Yeah. Speaker1: [00:04:44] So I mean the biggest thing Speaker2: [00:04:46] I could I could say is Speaker1: [00:04:47] Definitely continue to reach out to people, but make sure you're targeting the right people on LinkedIn to reach out to the person who posted the job. That's usually a good person to reach out to, but also going into like the LinkedIn AIs the companies kind Speaker2: [00:04:59] Of LinkedIn page Speaker1: [00:05:01] And then filtering that down by like Speaker2: [00:05:03] Recruiter, technical recruiter and even like. Manager or higher up people in the Data food chain, I guess, and just because animal credits are, you know, you can burn through Speaker1: [00:05:18] Them rather quickly. So you would make sure that whoever it is that you're targeting is actually somebody who's active Speaker2: [00:05:22] On LinkedIn quite regularly. So when you Speaker1: [00:05:25] When you go to your I'll pull it up just to show you guys. So if anybody is actually listening to this on the podcast, Speaker2: [00:05:32] I'm going to YouTube right now. I mean, YouTube and what a Speaker1: [00:05:35] Linkedin right now. So you go to YouTube to see what it is that I'm doing. But let's just say, for example, here, let's go look at jobs here. Let's let's make this a real case study. Where are you looking for jobs at? Speaker3: [00:05:45] Charlotte, Washington, D.C., or really only to Florida or Florida? Those three opportunities up Speaker1: [00:05:53] Put Washington, D.C. in the search just because it's because and you can search by title skill keywords and so forth. Right. So something you might want to do Speaker2: [00:06:02] Is Data man. Speaker3: [00:06:04] Yeah, well, I use Data period, but yeah. Speaker1: [00:06:08] Yeah, I make it, you know, as target as possible. Right. So Data management or even Data governance Speaker2: [00:06:14] Is another way we could put that here. Right. And here. Speaker1: [00:06:18] Let's see what what happens here. So what I'm Speaker2: [00:06:20] Going to show you here is how I would go about applying for a job. Right. OK, so here I Speaker1: [00:06:26] See enterprise Data policy, governance, senior sociological. Let's say that I am interested in this Speaker2: [00:06:30] Job like oh by the number in the description. Looks awesome. Cool. Does it have a job poster in this case. Speaker1: [00:06:37] Does not look like it. So I'll go to the company website in this case, Fannie Mae, which Speaker2: [00:06:41] I think is a huge excited credit type of place who I owe a lot of student loans to. Um, yeah, no kidding. But you've got two people, right? Speaker1: [00:06:52] And you search, you can search by title keywords also. Speaker2: [00:06:55] So I would go technical recruiter, OK, just because those are the type of recruiters I would tend to hire for Data type of roles. Speaker1: [00:07:03] And then when you go to technical recruiter, you'll see that, you know, they'll probably have a bunch of people pile up, obviously on target, people who are Speaker2: [00:07:08] In the location where you're applying for and bunch of people come up. Speaker1: [00:07:14] Right. So how do I know who to target? Right. I've got like five email credits and I want to maximize the probability of my message actually Speaker2: [00:07:21] Getting returned and maximize Speaker1: [00:07:23] The probability that that message is going to result in Speaker2: [00:07:26] A phone call at least. Right. Speaker1: [00:07:28] So I could go through each person kind of Speaker2: [00:07:30] One by one, and I'd go directly to the page here and I'd look at their LinkedIn activity. And this is kind of like a lightweight version of stalking on people, but it's all good. But I don't know why my Internet is so slow, but you go to their profile and typically, like one of the first few boxes up here Speaker1: [00:07:50] Is right under the about section is going to be Speaker2: [00:07:52] Activity. And if somebody, you know, you could see how often they've posted, how often they've liked or shared or done something on LinkedIn. Right. So in this case, this guy has activity here. Speaker1: [00:08:03] And we could see when he was last liking and posting stuff. Right. Because if it was within the last day or two, then that's probably a good person. Speaker2: [00:08:11] They got a warm lead, so to speak, that you can Speaker1: [00:08:14] Reach out to. Right. So this guy is in about a week ago. OK, well, to me, you know, I'd look for somebody who's more active Speaker2: [00:08:20] Within a day or so. Right. And so this guy doesn't really look active. Speaker1: [00:08:24] I wouldn't waste any more credit on him. I just make sure that you're Speaker2: [00:08:26] Tailoring OK, call Speaker1: [00:08:28] Resume itself. I don't think I mean enough advice on what to do with a resume. I think it'll all cancel out to zero eventually. But I am lazy, so I just have one Speaker2: [00:08:38] Resume and I just have one eye for all jobs. But I'll tailor my message that I reach out to with you. No more custom tailored. Speaker1: [00:08:48] I guess Speaker2: [00:08:49] There's all kinds Speaker3: [00:08:50] Of things that'll that'll help with something I haven't done before, so I appreciate it. And now you can. Let's let Martin ator bug, you know. Speaker1: [00:08:58] Yeah, definitely. I mean, I know a lot of people are getting value from this as well, even the ones listening on the podcast. But just finish my thought on that real quick. There's another website called Speaker2: [00:09:09] Eyo where you can Speaker1: [00:09:11] Essentially get the email pattern Speaker2: [00:09:13] For a given company to to see what their email standard is like and try to reach out to people inside the company directly Speaker1: [00:09:22] To the email. I wouldn't reach out to Speaker2: [00:09:24] Like a data scientist or Speaker1: [00:09:25] A hiring manager. That way I'd reach out to recruiters Speaker2: [00:09:28] Because that's their job to do that. Speaker1: [00:09:30] So definitely do that. And then there's a few emails that you can get on. I've got this email list that I'm on that Speaker2: [00:09:36] Gets you know, I get a bunch of messages every day about openings, Speaker1: [00:09:41] See if I could find one really just to give you the name of that Speaker2: [00:09:44] Company. But there's Speaker1: [00:09:46] Like listservs and stuff that you can get Speaker2: [00:09:47] On in this case. This company is called AIs Solutions Inc. That's oh, Speaker3: [00:09:55] Yeah. Yes, there are there are big company. And I don't care for AIs solutions that much because most of the times they're recruiting for another company. OK, they're always just discounting the rates. Because they're third parties. Speaker2: [00:10:09] Yeah, yeah, yeah, but I Speaker1: [00:10:11] Mean, so hopefully that's that's helpful, like, just make sure that the message is clear. Like I've got a standard kind of template that I will manipulate. Speaker2: [00:10:20] But the entire Speaker1: [00:10:21] Thing I'm doing with my message and I reach out to somebody is just telling them what I could do for them, not just telling them how awesome I am, Speaker2: [00:10:27] But like talking about how my awesomeness is going to benefit them. So that's a subtle shift. Speaker1: [00:10:32] But thanks for the question. We'll continue. We want feel Speaker2: [00:10:35] Free to hop on with any other questions at any other point, if you'd like. So let's go to either I'll put you on on mute here, Kevin. Just minimize background noise there. Yeah. Speaker1: [00:10:48] Let's go to Martin. Martin is a new face. I don't think I've seen my tore. My good friend here is he's definitely only facings been around. Speaker2: [00:10:55] Martin, how's it going? Speaker4: [00:10:57] I'm fine, thank you. Speaker2: [00:10:58] Are there any questions or anything? Let me know. Speaker4: [00:11:01] Um, my question is how to to to to keep on learning what what what is the next step for me. So I started to learn Python two or three years ago and then I come to to Data signs. I did a lot with Data and learned did you pendas course. And actually I think it's time to to enter the machine learning thing. But I'm I'm not sure how to start this for. Speaker2: [00:11:49] Yeah. I'd start by doing like a like Speaker1: [00:11:51] Many projects as much as possible just to start developing the skill set, developing and understanding of the workflow, Speaker2: [00:11:58] So and so forth. So I just Speaker1: [00:12:00] Kind of backtrack here a little bit. So give me a little Speaker2: [00:12:02] Better sense of your background here. So you so Speaker1: [00:12:05] You currently you've been studying Python for three years. Speaker2: [00:12:08] Like, do you have like a Data background, stat's, background, math, background or anything like that? Speaker4: [00:12:13] Well, I'm actually a civil engineer and I'm working with long term traffic models since 15 years or so. Yeah. So so there are some Data which I use all the time, but not not very in a statistical way, just applying some functions or changing some Data to get to court and prediction. Yeah. Speaker1: [00:12:47] So that when you say you want to get into machine learning, is it like you've never studied it before, you just kind of wanna get your feet wet with it to learn and understand some of the common algorithms and what Speaker2: [00:12:56] This thing is all about? Yeah, it's about OK, so I'm a big fan of books. My favorite book is called Speaker1: [00:13:04] Hands on Machine Speaker2: [00:13:05] Learning with Python Speaker1: [00:13:07] Or Secular Hands on Machine Learning. This I could learn and tensorflow. Speaker2: [00:13:11] That's a good one. And just this Speaker1: [00:13:13] Introduction to machine learning. Those are good Texel. I'll pull them up in a second here. But once you go through one of those tags, I would say the next thing is try to find the intersection of civil engineering Speaker2: [00:13:23] And machine learning. So as you were speaking, I pulled up real quickly, just a Speaker1: [00:13:30] Quick Google search, civil engineering, machine learning. And it looks like there's, you know, machine learning techniques for civil engineering. Speaker2: [00:13:35] A primer on machine learning applications in civil engineering. Speaker1: [00:13:38] So you want to study machine learning and civil engineering, right. So I would try to find the intersection between Speaker2: [00:13:44] Machine learning, civil engineering, because you've got extensive experience in civil engineering and now you're trying to blend that with Speaker1: [00:13:51] Machine learning. That just makes for easier Speaker2: [00:13:54] Learning experience, in my opinion, more enriched learning experience. Speaker1: [00:13:58] So I can give you a link to to this. Most of these type of Speaker2: [00:14:01] Papers will kind of walk you through Speaker1: [00:14:03] A general review process of what machine learning is all about. Speaker2: [00:14:06] Um, so I'll link to this as well. But the two books I was talking about in particular there was introduction to machine learning with Python. Right. Speaker1: [00:14:17] And it's this one right. Here is the O'Reilly Speaker2: [00:14:19] Text, the O'Reilly text here. But if you Speaker1: [00:14:23] Prefer online courses. So, yeah, I guess such as what Speaker2: [00:14:26] Type of matter you like to consume your Speaker1: [00:14:29] Your educational content. Are you more of a book scholar. Speaker2: [00:14:31] More of a. Speaker4: [00:14:33] Yes, I like, I like online courses but often they are not going deep enough for me. Yeah I did some, I started one machine learning course but it was only shown how it is applied in Python. So just but but not what, what is going on behind all of this. OK. Speaker1: [00:15:00] Oh yeah. OK, so those two books will, they'll give you mostly just a familiarity with the secular and a. But they probably will go in depth in the math Speaker2: [00:15:10] As it seems like you want to go. So for that I would there's a free book actually that that is available, I think it's called and I'll get Speaker1: [00:15:19] You the link for it if you go to my LinkedIn. PDF must have shared it recently, but it was like Speaker2: [00:15:23] Immediately there's Bitly. I think it was like mathematics or something like that. What did I make the. That might not be it, Speaker1: [00:15:33] But I can I can give you the Speaker2: [00:15:34] Link Speaker1: [00:15:36] In a second here. I'll remember Speaker2: [00:15:37] What it is, but it's a Speaker1: [00:15:39] Book that goes really, really Speaker2: [00:15:40] In depth. So, you know, let me just try to find it real quickly. Speaker1: [00:15:45] But that's what that's what my approach would be. I'll pause here to see if there's any questions from you or any comments while I Speaker2: [00:15:51] Try to start to dig this link up for you. But but please go for it. Martin, if Speaker1: [00:15:56] You got any follow up questions Speaker2: [00:15:57] Or comments, let me let me know. Speaker4: [00:15:59] Um, yeah, I heard some from Kiru, you know, I guess. Yeah. And I heard he said that it is better to to start projects than to learn just by courses. Speaker2: [00:16:16] Yeah. Speaker1: [00:16:16] Projects are definitely, definitely the way to go. Speaker2: [00:16:18] Right. So um so once Speaker1: [00:16:20] You like the foundation knowledge you definitely Speaker2: [00:16:23] Start doing actual projects. Speaker1: [00:16:27] But the projects I would recommend you do are ones Speaker2: [00:16:29] That that are rooted with civil Speaker1: [00:16:31] Engineering. So there's that intersection of civil engineering and machine learning just because it make it so much more enjoyable. You might even find opportunities for you Speaker2: [00:16:38] To, uh, to employ it in your current job. Other projects are 100 percent like the best way to learn. Um, but I mean, you have to at some point start from a bit of source material. Right. Speaker1: [00:16:50] So I found the link here. It's mathematics for machine learning. Speaker2: [00:16:53] It goes super, super in depth. Um, so just a warning. Let me get you link here. All right. I'll pull it Speaker1: [00:17:01] Up real quickly as well. Sorry for everybody listening on the Speaker2: [00:17:04] Podcast, stalling here for a second. But yeah, this Speaker1: [00:17:07] Book is called Mathematics for Machine Learning, and it goes super Speaker2: [00:17:09] In-depth with all the Speaker1: [00:17:11] Behind the scenes math and stuff, if that's Speaker2: [00:17:13] What you're interested in. I know some people like to, uh, look, Speaker1: [00:17:17] It's not satisfying enough just to see what what the API does. It is good to see what's Speaker2: [00:17:21] Going on behind the Speaker1: [00:17:22] Scenes of this book is really, really good for that. So when Models meets Data Speaker2: [00:17:26] And a few other things and I can give you a few other links, but I absolutely like by all means, do projects, you must must do projects to, uh, to Speaker1: [00:17:35] Really solidify understanding. And when you do projects, Speaker2: [00:17:38] You post them on like GitHub. Right. And you can share them as proof of work, put them on your resume when you start looking for jobs. So these are all added benefits of of doing projects. Let me know if that was Speaker1: [00:17:51] Helpful or not or if I was just being too vague. Speaker2: [00:17:54] I'm happy to be to. Speaker1: [00:17:57] And then let me give you a Speaker2: [00:17:58] Link to this paper that's doing machine Speaker1: [00:18:01] Learning techniques for civil Speaker2: [00:18:02] Engineering problems. Um, I think that would be really helpful. Again, just because Speaker1: [00:18:07] You're already so knowledgeable about civil engineering that seeing how this new thing applies in your Speaker2: [00:18:12] Domain would make that learning curve a little bit easier. And then here's Speaker1: [00:18:17] Another book as well, Machine Learning Speaker2: [00:18:18] In Action, and I'll give you that link here. Awesome. Cool. Speaker1: [00:18:23] Well, Martin, let me know if there's any other questions as we progress. Speaker2: [00:18:26] I'm happy to, uh, to take them. Let's continue. Go got there a. in the House and two years ago when, Speaker1: [00:18:33] Um, Granada is also here. Good to see you, Renata. Any questions from anyone? Speaker4: [00:18:37] Jill, let's find out a vehicle that thinks that. Speaker1: [00:18:42] Awesome. Well, let's open for prayers for whoever else would like to go for it. Speaker2: [00:18:46] So, Jill, Renata, um, a.. No questions from Jill. Awesome. To have to go and get any questions or comments. Speaker3: [00:18:54] No questions. Just a small comment. I was just looking at that book you just listed. I got to page six, seven, eight, all Greek. To me, Speaker1: [00:19:06] It's literally literally all Greek. Yeah. That that first link, that mathematics from machine learning, that is definitely for for people who want Speaker2: [00:19:13] To know what the hell is going on under the hood, which Speaker1: [00:19:17] Sounded like Martin wanted that type of reference. But the other one I gave him I think is a little, uh, Speaker2: [00:19:22] More lightweight, not lightweight. Speaker3: [00:19:24] You have to be into math and really understand formulas, I guess. Speaker2: [00:19:28] Yeah, definitely. Definitely an important part of Data science machine learning for sure Speaker3: [00:19:33] By itself isn't really covering up to that level. So I would like to go. Yeah, that's going to be my biggest challenge. You know, it's one thing I can't mention. I understand in principle some that I'm starting to get now, but to go from that and then actually being able to utilize those skills, that means I would have to kind of go back on my path all over again in a few years. Speaker2: [00:19:56] Yeah, I mean, but that ground up approach to like it might Speaker1: [00:20:00] Work for some people, might not work for some people, but Speaker3: [00:20:02] It won't work for me. I'm a hands on. So yeah. Yeah. Get my people up and running. That's the first step. Yeah, you know, trial and error till you kind of get the basics. And that's how I approach things like, Speaker2: [00:20:15] You know, I like that approach. Speaker1: [00:20:17] Hands on is, you know, you always learn better by doing on the Speaker2: [00:20:20] Job type of type of thing, so. Exactly. Speaker1: [00:20:24] Let's see if we had any questions that we had a.. Speaker2: [00:20:27] Or not A, no questions from a.. Speaker1: [00:20:31] Just been grinding, studying on evenings and weekends Speaker2: [00:20:33] And gradually also Speaker1: [00:20:35] Using Data since I was Speaker2: [00:20:36] A little stream project at the moment. Yeah. So as much as you can try to Speaker1: [00:20:40] Find opportunities to use Data science Speaker2: [00:20:42] Methodology in your current role, I highly, highly recommend that when I was trying to learn Python. Speaker1: [00:20:47] It's like four years ago. So at that time I was a biostatistician. I was working Speaker2: [00:20:51] As a essentially clinical trial statistician at a pharmaceutical company. All of our work was done in SACE, S.A.S., because that's what we would have to Speaker1: [00:21:01] Submit code along with the Data Speaker2: [00:21:03] To the federal government, to the FDA. And I wanted Speaker1: [00:21:07] To learn how to use Python. So what did I do? I recreated everything that I didn't Speaker2: [00:21:12] Sace in Python Speaker1: [00:21:13] Because I had like a basis of Speaker2: [00:21:14] Comparison. Right. Like I knew that my Sasko did this and Speaker1: [00:21:19] Manipulated Data this way and had Speaker2: [00:21:20] This particular output. Cool. Now let me see if I can do that in Python. And I did that Speaker1: [00:21:25] And just gradually started Speaker2: [00:21:26] Just building the skills that way. And it was it was a bit of a learning curve. Took me two, three months to Speaker1: [00:21:34] Get decent at it. Speaker2: [00:21:35] I'm still not like I'm Speaker1: [00:21:36] Not a software engineer by any Speaker2: [00:21:37] Means, but I can get stuff done. I get a lot of stuff done. But that's one good way to learn, is just Speaker1: [00:21:42] Find opportunities in your current role to leverage and use this stuff. All right. Speaker2: [00:21:46] Sacia Renata or anybody else, go questions. Go for it. Jill is Speaker1: [00:21:51] Heading out. All right. So what else is going on, guys? We got we got some we have some time here, man. Speaker2: [00:21:56] I'm happy to happy to chat about Speaker1: [00:21:58] Anything not even related to Data seems to be Speaker2: [00:22:00] Completely honest. Speaker1: [00:22:01] Denseness is cool and all, but it's not like the only thing I like talking about. Speaker2: [00:22:05] So if he has any questions on on anything at all, Speaker3: [00:22:09] You want to help me. You want to help me write. My dissertation Speaker2: [00:22:13] Was a dissertation about Speaker3: [00:22:15] Social mobile social media marketing for small businesses in Charlotte. Speaker2: [00:22:19] That's pretty interesting. Tell me more about that mobile Speaker1: [00:22:22] Social media marketing. Speaker3: [00:22:24] Yeah, you know, first of all, Web advertising has only been around since ninety four, so that's generally pretty early thing. And mobiles only really been around since around twenty seventeen. The mobile searches on Google have increased over desktop searches, so for years mobiles have been kind of king on search. But the problem is that most small businesses lose. That's my opinion. I'll find out when I actually did the research is they don't know how to do it. They're not equipped to do it or trained to do it. And even though they may want to do it for the business. So I found somebody who all he did was put this business on Google Maps, happens to be in Texas, but it's still a thing. And according to him, their income increased threefold by only going on to Google Maps only for small business. So that's the type stuff is to see, you know, if a small business takes the steps to go ahead and go on Google Maps or advertise on Facebook or Instagram, I know there's millions of places they can advertise trying to limit it so I can get my dissertation done, you know, what's out there, what can they do and try to increase their sales or their customer base or, you know, one is just how are you? Like, give an example. We went out to a restaurant and it was the food was the and the service stunk. And I put a nasty Yelp comment on for that restaurant because that restaurant bothered to even contact me or respond. Nope. So they've got this really bad review sitting out there and they haven't bothered to respond, which I don't think is proper. I think they should have at least responded to it anyway. Speaker1: [00:24:15] Yeah, yeah. Mobile mobile marketing is definitely interesting. Like marketing in general is just super interesting. I to be fascinated not just the not just like the advertising Speaker2: [00:24:24] Part of it, like doing the Speaker1: [00:24:26] Analytics on the ads and stuff like that. That's interesting. But just marketing Speaker2: [00:24:29] It, you know, as a field I find really, really interesting. A huge fan of Seth Godin. Speaker1: [00:24:35] He's written a few books on marketing. This is marketing. Speaker2: [00:24:37] All marketers are liars, but he's he's awesome. Yeah. Read Ryan Holiday's book called Perennial Seller, Speaker1: [00:24:47] Which is all about marketing as well. Speaker2: [00:24:48] So pretty interesting stuff. Speaker3: [00:24:50] So anyway, that's what I'm doing. That's taking up a lot of my time, as well as trying to look for a job, trying to write dissertations. Speaker2: [00:24:58] Yeah, man, it's it's hard to look for a job, man. I mean, I could I could identify with that struggle. I mean, I don't Speaker1: [00:25:05] Know, man. I feel like some people. You like people in interviews, Speaker2: [00:25:09] They don't ask good questions, right, like I've been on, you know, I've been on tons of Speaker1: [00:25:14] Interviews, right. Like I've been on tons of Speaker2: [00:25:15] Interviews and people just Speaker1: [00:25:19] The interviewers just asking stupid questions that somebody asked me how many models Speaker2: [00:25:23] I've built or Speaker1: [00:25:24] How many models have I trained. And I didn't know if it's a trick question, because if you include models trained and Speaker2: [00:25:28] Cross-validation probably millions. But I mean, like, what's the point of asking Speaker1: [00:25:34] Me that question? Speaker2: [00:25:35] Like, that's just really not a very useful Speaker1: [00:25:39] Question, but also questions like like I had somebody ask, Speaker2: [00:25:43] Like, you know, fairly recently the interview question was something along the lines of Speaker1: [00:25:48] This asking me a lot of different things, a lot different people. What type of data scientist are you? And it was a vague Speaker2: [00:25:53] Question looking for a specific answer, because this position was it is not Speaker1: [00:25:59] A position where you'd be deploying machine models into production. Speaker2: [00:26:02] You'd be doing a lot of statistics and analysis and that type of respect. So what he's Speaker1: [00:26:09] Trying to discern is, are you the type of data scientist that just wants to deploy models into Speaker2: [00:26:13] Production or do you prefer doing statistical and and probabilistic inference and stuff like that, which he did not mention Speaker1: [00:26:20] About in the interview, that this what the job was, was going to be until way later? Speaker2: [00:26:24] They just kind of clicked to me like, oh, OK. Speaker1: [00:26:26] Well, you're just asking a really weird question. Speaker2: [00:26:28] Why not just clearly state at the Speaker1: [00:26:30] Forefront of the interview that this is what the job will entail? Is this the type of work that you enjoy doing as a data scientist? Speaker2: [00:26:36] Right. So vague questions like that are really, really stupid. I give a shit ton of bad questions, Martin. Go for it. Speaker4: [00:26:45] How important do you think is it to have a knowledge about field and in that you are doing data science in, you know, can do and in civil engineering are Speaker2: [00:27:01] Very, very, very important? I think that's that's really, really important. Understanding domain knowledge gives you more Speaker1: [00:27:07] Context, makes you more knowledgeable and understanding what the implications of what this model is that you're building what it Speaker2: [00:27:14] Does. Right. If you can understand Speaker1: [00:27:15] The context to which Speaker2: [00:27:16] It will be used. So domain knowledge is important. Is it like a barrier to getting a job? Probably not, unless you're going Speaker1: [00:27:23] To super Speaker2: [00:27:24] Specialized, right. If you're going for, like, I don't know, computer vision or or self-driving Speaker1: [00:27:30] Cars type of machine learning stuff, then, you know, you better really, really understand that domain because I think it'd be hard to break into that. Speaker2: [00:27:36] It's like the first job. But if you're doing a product analytics and and stuff like that, it's, you know, domain knowledge. Speaker4: [00:27:43] So I live in an area where a lot of companies are located and they are looking for Data science very often. But I don't think that is my field. Speaker1: [00:27:58] Yeah, so they're probably rebranding biostatisticians as Data scientist or something like that. I'm not sure, but I feel like that you'd have to learn a lot of Speaker2: [00:28:06] Regulatory rules, especially pharmaceuticals. When I was in pharmaceuticals, there's so much like Speaker1: [00:28:12] Regulatory stuff that I Speaker2: [00:28:13] Needed to to know, mostly with respect to the work that I'd be authoring like protocol sections, statistical Speaker1: [00:28:20] Sections of protocols or statistical analysis Speaker2: [00:28:23] Plans and things like that. Um, it's helpful. Um, I mean, like, why not just apply for and see what happens is there's never any harm with that. Speaker4: [00:28:31] Do you know do you know any data science project in the field of mobility laboratory. Speaker2: [00:28:40] A little bit more. What you mean by mobility. Speaker1: [00:28:42] Um, I just like people moving around the mobility. I would have mobility. Speaker4: [00:28:47] No, not. And new mobility like like how to predict traffic or OK to give people information about if there is any trouble ahead when they are driving in the car or public public transport being. Speaker2: [00:29:14] Yes. I mean Speaker1: [00:29:15] The first thing I do is just start researching Speaker2: [00:29:17] The, um, the domain. Speaker1: [00:29:20] Right, because there's I mean, I get Speaker2: [00:29:22] A lot of knowledge from just reading white Speaker1: [00:29:25] Papers and research papers because you go to the reference section and there's some great references that that kind of help you, Speaker2: [00:29:30] You know, build a chain of good resources, but just start Speaker1: [00:29:35] Googling. That's the easiest thing. Right. So we're talking about here like, Speaker2: [00:29:37] I know, traffic prediction. Right. So we do traffic prediction and machine learning. Speaker1: [00:29:43] And let's just see if somebody has actually done this in a Speaker2: [00:29:46] Jupiter notebook and Speaker1: [00:29:48] See what comes up. Right. So in this case, I'm doing traffic prediction machine learning Speaker2: [00:29:52] Jupiter notebook and we've got traffic sign detection for Tensorflow is probably not what you're talking about. Um, yeah. So let's remove this, uh, file type part or even look for white papers. Um, so you Speaker1: [00:30:04] Had to start looking for research papers and see what happens. So we got deep learning on traffic prediction, I call comparison of machine learning methods Speaker2: [00:30:11] For the air traffic prediction architecture based on machine learning, traffic flow, prediction of big Data machine learning approaches for traffic flow forecasting. All right, cool. So there you go. And it looks like there's a ton Speaker1: [00:30:23] Of research in that area. I would just dig into it by getting one of Speaker2: [00:30:27] The papers Speaker1: [00:30:28] That seems most approachable, kind of read through Speaker2: [00:30:30] It, understand it, and then, you know, dig in a little bit deeper. So, for example, this one that we're looking at, a PDF just opened up because I accidentally downloaded a PDF, I guess I don't know where it went. Here it is. Speaker1: [00:30:44] And it's a comparison of machine learning methods for the prediction of traffic speeds in urban places. Speaker2: [00:30:51] So let me just go ahead and pull this up here. Speaker1: [00:30:53] And most white papers will Speaker2: [00:30:54] Have, um, I don't know where the, uh, where my screen went. All right. Sorry about this. So I'm pulling up the PDF here just to show you how to go through it. So there are a bunch of people here, right. So I would I would if I Speaker1: [00:31:10] Were any time I'm trying to learn how to figure out how to do something new, I go to a research paper, I take note of all these people, and I'll add them Speaker2: [00:31:16] On LinkedIn just to see if Speaker1: [00:31:18] They're active on LinkedIn or see if they have, like university profile, because maybe I want to connect with them and ask them Speaker2: [00:31:22] Questions. And, you know, like, hey, your paper is interesting. Here's what I have a question. Right. That's completely OK. But most white papers have the reference section, which are probably the best section. And I mean, it's pretty, really thorough. It looks like this is a really shows you the architecture and everything, which is really cool. Um, but what we're Speaker1: [00:31:42] Looking for is the references section as well. Right. Speaker2: [00:31:44] Because you might find more interesting papers that are more specific to what Speaker1: [00:31:50] It is that you Speaker2: [00:31:51] Are looking for. Speaker1: [00:31:52] Another thing you could do is let's say Speaker2: [00:31:54] You've you've pulled up like two or three or four white papers in the domain. Speaker1: [00:32:00] Just print out the reference sheet from all these or the references from all these white papers and see what's common among those. And that's a good indication that, Speaker2: [00:32:07] Ok, that's probably a source of truth that I should look into. OK, but that's that would be my my approach to that. Speaker1: [00:32:14] So question here from a.. You ask who gets on the podcast, whether they see Data science where they decide to go to the next two to five years. What's my take on it? Speaker2: [00:32:23] Flipping, flipping the script on me here. So where do Speaker1: [00:32:26] I think Data says is going in the next two to five years? Definitely don't think it's going Speaker2: [00:32:30] Away, that's for sure. It's here to stay. Speaker1: [00:32:33] And I think the next three to five years I don't see anything like Speaker2: [00:32:37] Really, really earth shattering happening. We might make progress on some interesting things like self-driving cars and things like that. But let me just stick to to the Speaker1: [00:32:48] Profession of Data science as a self. Speaker2: [00:32:50] I just see it becoming, you know, more and more discrete kind of roles. Right. I feel like it'll move more towards specialization. Opera operationalization as well. So then talks about this a lot. And I agree with his perspective on this. You'll see more about Ops II Ops type of roles, pop up more architecture type of roles, pop up, that is for sure. Speaker1: [00:33:13] But I think even beyond five years, Speaker2: [00:33:15] I think every job is going to Speaker1: [00:33:17] Become a little bit of Speaker2: [00:33:18] Data science at some level for Speaker1: [00:33:21] Many professions, especially professions that do a lot of Speaker2: [00:33:24] Computation, that do a lot of damn replication work with a lot of Data you'll see Speaker1: [00:33:31] Them doing more and more Data type of stuff, more and more Speaker2: [00:33:33] Analytics. I mean, my come to mind, there might be a day where, you know, accountants Speaker1: [00:33:39] Start using Speaker2: [00:33:39] Python scripts in their in their Excel formats, in their Excel files, which is possible. Right. Because you can you can integrate Python into Excel. So I see that trend happening more. I think Speaker1: [00:33:52] People are just there just to be more and more data scientist at work doing data Speaker2: [00:33:56] Science type of work, but maybe don't have the data science type title, if that makes sense to go for it. Speaker3: [00:34:00] And I a good comment that you have. I don't disagree with you on that. And I also think that I think the biggest challenge right now for Sassan streamlining Data science into more jobs is the tools. I mean, Python and ah and all these tools. It's not something you just get into and do you have to sit down and then you have to use a regular basis. Now, Excel has had extreme success because it's a tool that everyone uses. Starting off with two plus two equals four and that kind of build gradually. Personally, I think that when they integrate Python etc, I think it will be an indirect arrangement where it basically will still kind of use the Excel. But you will then have more standards that are going to be running scripts for you like macros, etc.. And over time, yeah, doing better answers right now, preparing for an audit. I'm going to be starting at one that week and I'm looking now at 800. Thousand transaction lines. The original Data received that sixty three columns. Now I'm up to about one hundred and forty because I have to add all the extra columns to extract, streamline, adjust the Data. Now, that part, the part that the title could have done something about or are could have done for me, but that's I can't program it myself that it's just going to have to wait. So I'm still doing it manually. But now Data science is definitely here. We're living on Data Data. That's going to be an hour every day for the rest of our lives. Speaker2: [00:35:45] Yeah. Yeah. It's not like, Speaker1: [00:35:47] You know, there's going to be any decrease or slow down to the amount of data being generated around the world. But we got more and more Speaker2: [00:35:53] Devices that collect data that, you know, companies are hopefully using ethically. That's something Speaker1: [00:35:59] I'd like to see happen in the next two to five years, Speaker2: [00:36:02] Maybe more courses on ethics, in data science, how to how to be a data scientist who doesn't cause harm or just some awareness and ethics, um, I think would be very, very useful. But I hope that answered your question that a.. I'm happy to elaborate on that. But to towards point, you know, you Speaker1: [00:36:23] Just your people are going to have to start Speaker2: [00:36:26] Doing things involving Speaker1: [00:36:28] Coding because it's going to make their job easier. Right. They might come across something at work which are like, oh, my God, I've spent so much time doing this. Speaker2: [00:36:34] I wonder if I can write something, a script or something that can make my life a little bit easier. Speaker3: [00:36:41] That's that's the low code. No code development system development that I don't know if you've heard about it, but first time I heard about it has been around for like five to seven years or 15, but I've just heard about it where it allows people with no programing background to do that exact same thing. I see something that's too difficult. I want to make it easy so they can go on to Lokodo called platform and they can develop the application they need to do the job. But it's really cool. Speaker2: [00:37:11] Yeah, yeah. A lot of Speaker1: [00:37:12] Low code, no code platform. The thing that's great, it makes it easier for people to do stuff. But once you start getting into the edge, cases like these companies are back. Backdoor way out is that if you get to weird edge cases, eventually you have to Speaker2: [00:37:22] Go into the hood and code and it's usually python code that you're going to have to to rate yourself. So that's kind of like the other way around it. Speaker1: [00:37:29] Like you still, even if you're using low code, no code environment, like it's not Speaker2: [00:37:32] Always taking your Desai's jobs, if they're do low code Data science, it's just a different type of person working on different types of problems. But eventually Speaker1: [00:37:42] Even that Speaker2: [00:37:43] Person will have to learn how to code because something they're trying to do with their low code, no code environment is bugging out, not working. Um, they have to do write code. And he says this happens to him constantly, Speaker1: [00:37:56] Since learning are for just over a year, sees things that he used to spend so much time doing. Speaker2: [00:38:01] Now that is so much faster. Yeah, I mean, even like try to find ways to make your life easier somehow. Right. Like, I had an epiphany earlier this week and it was Speaker1: [00:38:11] The stupidest epiphany. I've been using computers for a very, Speaker2: [00:38:13] Very long time. Right. Like I've Speaker1: [00:38:15] Been using computers since I was, Speaker2: [00:38:17] I don't know, like twelve, thirteen, maybe thirty eight soon. So I've been using them for quite some time, but I never bothered to learn like shortcuts Speaker1: [00:38:26] For just the operating system Speaker2: [00:38:28] And stuff like that. Speaker1: [00:38:30] And I just recently made the transition to using Speaker2: [00:38:32] A Mac just like two years ago and never really properly learned the shortcuts for for the Mac OS. And I was spending Speaker1: [00:38:39] Time like, here's my setup. Speaker2: [00:38:40] Right? I've got I've got a keyboard and I got a trackpad right. Then it's my laptop right here on the on a stand. But I'll be typing and Speaker1: [00:38:49] I need to do something. I've spent a lot of time moving Speaker2: [00:38:51] My hand over to the trackpad moving stuff around. It will cause wrist pain and it'll just take time. And I was like, dude, this is so stupid. Why don't I just learn how to use the shortcuts on the keyboard so then I don't have to keep on going to the track pad. Speaker1: [00:39:06] And so now I only really use the track pad or try to only use the track Speaker2: [00:39:09] Pad for stuff that I cannot do with the keyboard, which tends, I think to be more like creative type of stuff. It's the way I'm thinking of it in my head because I like boring type of stuff that I can imagine. A computer being able to write a script for that stuff probably has keyboard shortcuts so bad. That's just just give me a hand. Speaker3: [00:39:30] So I got another question for you. Yeah, I've heard of descriptive analytics, predictive, prescriptive, and recently I read an article about collaborator's analytics. Are you familiar with it? You know what that entails? I haven't gotten into it in detail. I was just curious if you were aware of that. Speaker2: [00:39:47] I've never heard that term. But I try to infer maybe Speaker1: [00:39:51] What they mean by collaborative analytics because there's platforms that like Google Collaboratory Speaker2: [00:39:57] Or Data breaks where data scientists can work on the same notebook at the same time collaboratively. So if that's. What is meant by that and then I think that's that's awesome, but let Speaker1: [00:40:09] Me look this up. Speaker2: [00:40:10] Let me Google this for a clip real quick, collaborative and a little. So you have never heard of this thing before? Speaker3: [00:40:20] Yeah, it just I mean, within the last couple of weeks, I've read about it. Speaker2: [00:40:23] So I thought essentially it says Speaker1: [00:40:26] It combines business intelligence Speaker2: [00:40:28] Software with some type of Speaker1: [00:40:31] Increased analytics teams, knowledge. So, yeah, it just looks like it makes it easier to collaborate with maybe non Speaker2: [00:40:39] Data scientists or non super technical type of people on a project, which I think is great. We need more of that. I agree. We need to learn it right now, probably not just to learn it Speaker1: [00:40:49] Kind of on the job we need Speaker2: [00:40:50] To on demand. That's my theory for pretty much everything is just Speaker1: [00:40:54] Mastered the basics, master the fundamentals and learn Speaker2: [00:40:57] New things on demand when you need to. Because if you Speaker1: [00:41:00] Have a solid foundation, solid fundamentals, then you can learn anything you need to. I think I spend a lot of my time now, like reading books that are related to the field, but not textbooks. Speaker2: [00:41:10] So for example, this book right here that I'm going through the book of why I buy Judea Pearl, it's it's a book. It's a statistics book. But it's not like, you know, a textbook that you go through with, like an online course, a class like that. But these are the books that I feel give me a better intuition of the dry facts that I learned in school, because in school, after grad school, I learned a shit ton of formulas and I learned how to do things with formulas. But I never really understood the history of some of the stuff that were behind there. So these books are really interesting for that purpose. That's why I'm Speaker1: [00:41:45] Interviewing the author for this one Speaker2: [00:41:47] Sheep. Jordan Ellenberg also wrote How Not to Be Wrong Power of Mathematical Thinking, New York Times bestseller. I haven't read through this yet. Probably start that later this week. Speaker1: [00:41:57] But let's go to Joshua. Speaker2: [00:41:57] So, Joshua, you got a question or anything? Let me know that he was unmetered very briefly or Renato for not as a question. Joshua, go for it. Speaker1: [00:42:05] See your unmuted, but I'm not able to hear you. Speaker2: [00:42:08] All right, Speaker1: [00:42:09] Renata, how are things going with you know, you got a lot of advice and information last week. I'm wondering if Speaker2: [00:42:16] If you made any progress on those projects you're working on or trying to work on. All right. Does not seem like it looks like Renata's audio is failing, Speaker1: [00:42:26] But, yeah, let me know if anybody else has questions. Got just a few more minutes here. So I'm happy Speaker2: [00:42:30] To to stick around. I just want to Speaker3: [00:42:31] Quickly follow up on my mentioning about, you know, learning command. Similarly, I mean, that's technically what I do. And every day when you get requests, there's probably kind science as well as I always evaluate whether that is something that's going to be a one time thing or is that something that's going to be a regular type, whether it's a report or a summary or some sort of delivery? And given if it is something that I expect that's going to be coming again and again, then I will spend a lot of extra time to properly develop so I don't have to do it again in the future. So I assume it's a similar thing with that science. It could probably do some very quick things, get it resolved. But on the other hand, you can probably if you believe that this is something that's going to be used again, it will probably spend more time to prepare and properly designed something so that it could be used to get out Speaker2: [00:43:29] Like that approach. Speaker1: [00:43:30] That's an excellent, excellent approach. And it's all about the master, the basics, master fundamentals like I like. Speaker2: [00:43:36] And I hear a lot Speaker1: [00:43:37] From students that they has to him to others like very well. I studied this and I studied this. I studied machine learning. I've studied this algorithm, that algorithm. Speaker2: [00:43:46] Which algorithm should I learn next? And it's like, how about Speaker1: [00:43:49] Stop studying algorithms and just try to use one of them Speaker2: [00:43:52] For a change and compare them and see how that works? I think that's a much more fruitful effort. So looks like Josh Speaker1: [00:44:00] Is having dashboards, having network issues Speaker2: [00:44:03] Related to having my Speaker1: [00:44:04] Problems. But Antia, a question here. Do you have one great book for learning Speaker2: [00:44:07] Statistics, basics for a non mathematician? I've been reading the comics you mentioned, but looking for a good book to read next. Yeah, I love the comic series, the Cartoon Guides. Those are some of my favorite books, but a good book for Speaker1: [00:44:24] Statistics, basically for a non Speaker2: [00:44:26] Mathematician. Naked Statistics by by Charles Wheelan. That's a great book, but I'm not sure if you want more or less like a textbook type of a book. Um, because, I mean, realistically, every textbook type of book will have a bunch of math in it. So it by design. Speaker3: [00:44:42] Mathematician Statistics for Dummies is an excellent starter statistics book, which is very high level, very easy to understand. And then you can go from there into deeper articles later. Speaker2: [00:44:54] Yeah, that's a great point. Speaker1: [00:44:55] Yeah. Those for Dummies books are awesome. Speaker2: [00:44:56] I love them. I've got a few of them sitting here. I got a few of those books there. Speaker1: [00:45:02] They break stuff down and make it easy to to understand. So definitely. That's a good recommendation if you're looking for more of a book that's Speaker2: [00:45:09] Just kind of a nonfiction book about statistics and how it's used Speaker1: [00:45:14] Widely used Speaker2: [00:45:15] In a bunch of different fields and domains, then I would recommend Speaker1: [00:45:19] That. Naked Statistics by Charles Wheelan. That's a good Speaker2: [00:45:22] One. Yeah, that that's I got a bunch of other ones here. But how not to be right now, how not to be wrong, Speaker1: [00:45:29] How to measure anything is actually a Speaker2: [00:45:31] Really good book by Douglas Hubbard. Speaker1: [00:45:34] A lot of good stats in that book not really designed for mathematicians. More just for for this thinking business people so highly recommend that one as well. But if you're looking for more of a Speaker2: [00:45:42] Textbook type of thing, then yeah, definitely go for that for Dummies book. Right on. So hopefully everybody is doing well today, which has a very happy Speaker1: [00:45:52] Mother's Day to all the mothers listening Speaker2: [00:45:55] To all of you guys, um, the mothers in your lives hanging out with, uh, with my wife and their baby very soon, right after this. Hopefully has had a chance to check out the episode I released from my podcast just a couple of days ago with my friend Prachi Tucker, and that those are the interview I released. Speaker1: [00:46:16] The officers will be released a little bit later. I didn't get a chance Speaker2: [00:46:19] To work on them yesterday due to my son's first birthday. So edit and upload those up a little bit later today. Speaker1: [00:46:26] So if you Speaker2: [00:46:26] Guys are waiting for that, they'll be available. Got some great interviews Speaker1: [00:46:32] Coming up for you guys on the podcast in the very near future. Next week is actually with Dennis Rodman. I believe Speaker2: [00:46:39] He is. Speaker1: [00:46:41] Interesting guy at the AIs will enjoy that conversation. I really enjoyed talking to him. Speaker2: [00:46:45] He's an author for Pact, so he's written a bunch of books for Packed, the Speaker1: [00:46:50] Most recent of which Speaker2: [00:46:51] Is all about Transformers, which which my friend Tom Ive's is is all about as well. But yeah, that will be released this Friday. Got a bunch of other interesting stuff coming up on the podcast, just to name a few episodes dropping in the coming weeks. We've got an episode releasing with Steve Nory in a few weeks. I've got one with Kingi coming up in a few weeks here. Um, an interview with Chase Caprio. Chase Caprio works for Impact Theory. Speaker1: [00:47:21] So that's Tom Baillieu's company. And I'm a huge Speaker2: [00:47:24] Tom Billings fan, so that's a good one. I really enjoyed speaking with him. Speaker1: [00:47:28] Interviewed Dave Gray, who wrote the book Liminal Thinking, and a bunch of other books. So that was really fun. Speaker2: [00:47:34] Fun interview as well. And then an interview with the philosopher Jamie Woodhouse. We're talking about this philosophy he developed called sentient ism, which should be Speaker1: [00:47:43] Interesting, does not look like there are any other questions, Speaker2: [00:47:46] Like I stalled for as long as possible. So, guys, thank you so much for spending part of your Sunday Speaker1: [00:47:52] Morning with me. You enjoyed having you here. If there's any last minute questions, now's the time. Speaker2: [00:47:57] Kevin, any last minute questions? No. Speaker3: [00:47:59] To say thanks, Speaker2: [00:48:00] Roland Martin Speaker1: [00:48:01] Or Tory or anybody else, I don't mind. We got to take care of the rest of the weekend. Remember, you've got Speaker2: [00:48:07] One life on this planet. Why not try to do some big cheers, everyone.