comet-ml-17apr.mp3 Speaker1: [00:00:09] What's up, everybody, welcome. Welcome to the comet and our office hours, powered by the artists of Data Science. Super excited to have all you guys here. Thanks for joining me on this Sunday. Beautiful Sunday. Except it's like windy and snowy and cold where I am. Hopefully it's nicer where you guys are, but super excited to have you guys here. Claudio. Josh. Christof. To get the day started, Ayodele is unable to join us today. She got her second dose of the covid vaccine, and that's got her feeling a bit under the weather. So it's going to be just me, hopefully, as an opportunity to check this out. Last week at the Discovery Channel conference, had a great time emceeing that event. It was a lot of fun, a great opportunity to chat with a lot of people and and networking stuff like that. So hopefully you guys got a chance to check that out. Speaker2: [00:01:02] I was really hoping to meet you during networking session. Speaker1: [00:01:06] Oh, yeah. Yeah, I was I was able to do a little bit networking. I got a yeah. It was interesting how they, um. I like that set up on Harp, but it was funny because I had like four people in a row I had connected with that were like, you know, people already knew one of them was Jiah. That's always here in our one of our community members here and then a couple other people that I already knew really well. So it it's funny that they had randomized it in in that interesting way. I would have been nice, would have been nice to to connect with the on their mind but yeah. Man I'm excited to uh to get into questions. So Cristoff or Klaudia or Josh or tarping you guys got questions. I will do my best to help answer it. Speaker2: [00:01:45] Um OK. So maybe I start. Yeah. I just want to say I was really happy to see you and seeing and Data signs virtual I think at the first week you were quite stressed. Was this right. I had like this feeling that the very first time you spoke, you were I was I was like telling myself, this is this isn't Harpreet that I know. This is someone that is better than me with Speaker1: [00:02:16] The idea of the opening, the opening minute or so. Yeah, it was. It's interesting because like you guys don't hear behind the scenes, but like there's people talking to me in my ear that you guys can't hear telling me to. Oh, wait, if I say this or you guys say this and whatever. So that takes a little bit of getting used to. But after that, it was just smooth sailing. OK, yeah. Yeah, I did. I did enjoy it. I don't know if that can be up on YouTube or not. Hopefully it is on YouTube. Yes. And check that out. But what else is on YouTube is also the most recent episode I released of my podcast. I started releasing more video episodes. I know that on YouTube channel there's my podcast episodes are up there, but their audio only. But I'm going to start just releasing the videos now. I'll just trim them up a little bit and post the videos up there, but release an episode with John Kate Thompson. He wrote the book How to Build Analytics Teams. He's definitely a legend in the industry. He's been around for quite some time. And I've also got the interview up there with Robert Green, which if you guys have now gotten a chance to check out, you definitely should. Robert Green, author of 40 Acres of Power, All the Harp our stuff is up there as well, had a really interesting discussion during Happy Hour, um, on Friday. Uh, I know Tor was there, Cristoff. Were you there as well? No, um, Speaker2: [00:03:33] I just I watched it today actually. Speaker1: [00:03:36] Uh, I have two session. Yeah. Yeah. It's definitely a very, very good discussion. I urge you guys to check it out. I think it's an important discussion. Um, I'm glad that we had it. And I think the, uh, I think just letting the discussion play out like that was the best thing to do. I don't think it would have been responsible on my end just to cut the discussion short or have it be a trite answer and and move on. I'm I'm glad I let it play out the way it did. So hopefully I get an opportunity to listen to that, um, that Jiah in the building. Good to see you again, as usual. Hey, well, I'm open for questions, man. I was hoping to see, uh, hoping to see a bunch of new friends in here. Not that I, I mean, I love you guys. Yes. I love seeing old friends here, but I was thinking that there might be an influx after the Data times go, uh, virtual conference. It might be more people coming in. Um, and, you know, there there's a couple here and they're coming in, but mostly the, uh, the regulars. But I'm and I'm excited. Let's let's, uh, let's get some questions going. Speaker3: [00:04:35] Can I ask a question now? I understand on your back channel, I read just a question yesterday, which basically is a model to estimate the carbon footprint. But I'm having some challenges in how presenting the data it's not very clear when you look at The Matrix is so great. So I'm just wondering if I could get some kind of input on how I can present it or graphically display. It's all made in exile and I can share it on the screen, the table and maybe explain if you're interested, if we can. Yeah, definitely. OK, let me just. Where's that screen? That's what I share. Speaker1: [00:05:24] Should be on the bottom. There's a green button that points up Speaker3: [00:05:26] Where we go and couldn't be more green that you have to enable me I Speaker1: [00:05:32] Guess. OK, let me see how to do this. I don't know how we're supposed to do this. Speaker3: [00:05:37] Uh, we learn something new every day. Speaker2: [00:05:40] Yeah. Speaker1: [00:05:41] So this isn't this isn't my zoom room that I'm the owner of. So my share screen is uh oh. So I have set up that. See I'm trying to figure it out here. Uh, it's um. Yeah well I have it almost like and it's like I could pull it up Speaker3: [00:06:01] And I just upload it. It's only just like so I pull in the punters group. Speaker1: [00:06:08] Yeah. We go ahead and put this out there. Speaker3: [00:06:10] There's the actual carbon footprint estimation. Technically what I'm planning to do is to get that's Speaker1: [00:06:20] I'll have to download the actual Excel file. It's if I do that. Speaker3: [00:06:24] And so the idea here is that in my business and my job, you know, we travel to different audits and travel and being on site. You need local transportation, taxi your car. But if you coming from long distance, you normally would be flying in. And then, of course, you also have the same issue of of local transportation. So this is only for transportation purposes. So the top left or sell C3, this is where you plug in the maximum number of sites or so if you can put in five there just so that we get less numbers to look at and less confusing. And then basically what you have is that the number of weeks that the audits will be performed. So when you're on site for many weeks now, for each of the auditors, there's a calculation based on kilometers per day, which is an average times the grams per kilometers, which is the 50 times one grams per kilometer. And for flights you would then have it's a five hour flight. It's 192 kilos per hour. So these are the parameters that you can then to to just look at everything that's like yellow based on those parameters. The model now will then say in Colombia, you have zero remote or this is an issue. Also what people want from all. If you work from you do not have any transportation footprint, carbon footprint. So what the model is doing now is that it's saying that when you have zero, the first road zero remote operators, five of them are on site. Now, if none of them are from our nonlocal meaning everybody's local, then they will calculate your footprint for that audit. So it's eight hundred and fifty seven kilos, OK? And if you have one person, which is a nonlocal whatever coming in one flight, then the carbon footprint increases to one eight one seven up to five six five seven if the entire team is coming from abroad or a flight. Speaker3: [00:08:34] And as you start putting people into remote, meaning that working from home, your carbon footprint total will then be reduced. So this is highlighted now with the bold and the black. Now, the other numbers have had to include because I need to calculate the savings for the various options. So technically, what I'm doing next is that in their footprint savings, when you look at sell the twenty three, which is about 40000 for the 800 kilo savings, basically what that is, is that that savings compared to the total if and the all the orders were coming from international versus everybody would be local. And that means you have a saving up for the 800 kilos. And then if one of the remote orders are away, your savings of course, will increase because you're sitting at home. So so I've calculated the actual savings based on those parameters. Visually, I'd like to present that somehow. That makes sense. So what I would like to see is somehow a graph that shows y axis is the number of nonlocal items, for example, and the X axis would be the local outfitters, but the graphical presentation would be aligned for each other, no remote one, remote control and so forth. And that's my challenge. And I am not very good at making graphs. I can make simple. But not this kind of a makes sense what I'm saying, yeah, Speaker1: [00:10:12] So you want to X-axis just the number of auditors and Y-axis is going to be these values, whatever the Speaker3: [00:10:18] Local the local wants. But the problem is, is that the values, the actual sabic, I'm looking at the bottom Caples the savings based on the combination of three parameters, you're looking at how many remote and how many of those auditors are nonlocal. And that's what I'm trying to present demographical way based on this data are say nonlocal. Speaker4: [00:10:42] You mean they are not. Speaker3: [00:10:45] They're not they're not in the location where the audit will be performed. So, for example, I live in France, but most of my orders are done in Norway. So when I am considered nonlocal local auditor, since I've come in from France going to Norway. So in addition to the daily transportation I would have in Norway, I would have to add my flight going back and forth. So this is the Data. Speaker1: [00:11:14] So with whatever graph it is that you're trying to communicate, what is it that you want to communicate like? Speaker3: [00:11:20] What I'd like to show is the impact of having remote auditors. So the more people are staying at all, the less footprint you make. So how much savings do you actually make compared to. So that's my graph. So when I'm looking at the graph in my head, I'm seeing one line for zero auditors working from remotely, one line for one not working remotely, and so on and so forth. Now for a at the bottom of the X axis, I would then like to see some sort of a number of auditors that are local and number of auditors going on the y axis would then be the non-local operators and then I need to have the data plot it if possible. Speaker4: [00:12:09] So it's so and I'm still your expert. This could be the carbon footprint, right? The one the the table below its carbon footprint savings. And then your y axis shouldn't be the remote auditors that would show the correlation between remote auditors and the carbon footprint. Would that be the case? Speaker1: [00:12:33] So let me just try to wrap my head around it. So essentially, what you're trying to say is you're trying to show the variable of interest that you want to communicate is the savings. So said actually this difference, Speaker3: [00:12:46] Bottom table, the bottom table. Speaker1: [00:12:48] And you're trying to just say that, OK, if we look at a number of auditors, if we just look at the number of auditors, so you want to say local versus non-local and then communicate the Speaker3: [00:13:00] Savings or savings based on whether they are how many people are remote. Speaker1: [00:13:06] Ok, so I think maybe everybody has any ideas. Definitely. But I think it might be a good idea to do a hold on one second. We pull it up here. Speaker3: [00:13:18] And for me, this is really you know, I since I made the table, I can read it. OK. So when I'm explaining something that will help us out, the first thing you need to do is decide, OK, I'm going to be political and ideological. Now, when you look at the savings, you don't have to read the the number of auditors that will be on site physically. Speaker1: [00:13:41] So maybe something like a bubble chart might be what you're looking for. I think that might be because you're essentially you're trying to show three things, three exactly things of information on a two dimensional plane, right? Speaker3: [00:13:52] Exactly. Speaker1: [00:13:53] So I think a bubble chart might be a good option. Just look into that. Maybe that might be helpful. That's the only thing that's coming to mind right now because Speaker3: [00:14:03] You are right down. I'm trying to show three things and there are two dimensions. Yeah. Speaker1: [00:14:07] Yeah. So I think this might be a good way to look into it. So if we do here, we can do Speaker3: [00:14:14] About, unfortunately, working in Excel on that. So that's my skill level. So yes, I Speaker1: [00:14:20] Definitely read the first read through this documentation just about what a bubble chart is and if it's going to be suiting your needs. But then looks like you can do bubble charts and Excel. Looks like there's quite a few resources out there. So it's taking bubble plots and Excel. It looks like you might be able to to very easily do that. I know Microsoft has the documentation. Speaker3: [00:14:39] I'm sure it's a simple thing. It's just that, you know, only when I'm creating these things, I make it for myself. So this tool that I'm quoting now, it's for basically my company are going to be offering any offsetting the carbon footprint. That's part of my offer. So, you know, going into the environmentally friendly and obvious. And then, of course, I would like I at my table now to say to them, OK, based on how many people this is the. Offset that, we will provide you with the savings and, of course, negotiate with them that, you know, to me doing what it sounds like, there's no reason why you need to be there politically. If we have to do it remotely, everybody benefits. Right? So so this is part of the strategy. But in the long term, I'm also planning on having this model included in my website so that people can put it in the private version, then gradually get a nice display on where, what to which, etc.. Speaker1: [00:15:37] Yeah, yeah. So I just feel like bubble chart might be the best way to go or something similar to that. So I'm sure once you start doing more research into bubble charts, like they'll link it to other charts that are similar. There's there's this website that I linked to here from Chartered. They've got this document How to Choose the Right Data Visualization. Actually, there's somebody who did a session on Davison's called Virtual, and she had a chart to pick her up. Look, pick that one up. It was like a chart chooser. Uh, yeah. I got to find I don't know if Cristoff, you remember who that was, but it was like just a chart. Yes. Depict Data studio and her Shaddai interactive chart, Schuessler. And if you use a code like I think it was Desco virtual, I would get get the free chart user. So this will probably be helpful as well. So I put the link for that as well, Speaker3: [00:16:30] That fancy charging tool Speaker1: [00:16:32] That I did use. Did you tune into the Disco Virtual. Speaker3: [00:16:36] Yeah, was part of it, but I can't remember exactly what. But there was some fancy stuff there that was just mind boggling. Speaker1: [00:16:44] Yeah she was, yeah. She was doing a lot of really cool stuff on Excel and her website just has like a good glossary of all these different plots. So definitely, definitely picked that up. Speaker2: [00:16:54] Are we talking about any memory Speaker1: [00:16:57] That was her name. So that was depict depict Data studio. I think that was the er the website that I linked it right there. But her name was Annamarie. That's right. Speaker3: [00:17:05] A simple black and white cool. Speaker1: [00:17:07] Yeah. Well hopefully, hopefully that is helpful. Speaker3: [00:17:09] So but I'll start with those and I'll see what I can figure out because like I said, it's, I've never been to me spending hours and hours on creating some simple graphs and I've never been able to get my game, but I see now that I have to. OK, so that's why I'm just asking about. But I'll show you mentioned parental chart preto charts. Speaker1: [00:17:33] Uh, shows that which you mentioned Azariah. Speaker4: [00:17:36] Yes, that's exactly what I meant. I'm seeing that it could work. Have you Speaker3: [00:17:39] Tried it. I don't even know what it is. Speaker4: [00:17:42] I you I don't have to that I could look at it like and make a real fighter jet. You or I might be wrong. Anyone feel free to correct. Speaker1: [00:17:50] Yeah, definitely. Like if you have some resources go ahead and link to that. But unfortunately I can't give control to people to show the screen for some reason. But if you have a good resource, go ahead, dump it in the chat for talk and he can definitely check that out. So hopefully that getting set up and on the right path, it Speaker3: [00:18:06] Started Speaker1: [00:18:07] Awesome. Yeah. Cool. So open up, see if anybody else has questions at this point. What's up? We have some new people joining in here. I soon man as going after anybody has a question. Go for it. The floor is wide open man. How are you man. Speaker5: [00:18:22] This is my first time. Speaker1: [00:18:23] Awesome. Well, welcome. Happy to happy to have you here. So yeah, it's pretty much just whatever questions topics doesn't even need to be Data science related. Could me by anything so happy to, to to help help you man. Any questions on, on anything Speaker5: [00:18:38] And not right now at the top of my head. But they will definitely do that Speaker1: [00:18:44] For the first time here. But I'm glad to have you here. How'd you hear about the comet. Emelle. OpenOffice ours. Speaker5: [00:18:50] Yeah I found it in LinkedIn actually. I think, I think I have you on my contact. Greenlights LinkedIn. Speaker1: [00:19:00] All right. Speaker5: [00:19:00] Ommen so yeah, probably too that I got this notification last week or two weeks ago and since that I'm not a Data full time data scientist, I used to be a geoscientist and I am looking forward to transitioning into data science, engineering and stuff. So it's over then. Yeah, I thought it might be something interesting to Speaker1: [00:19:28] Get definitely just an opportunity for people to come in and ask questions and not to say that I got all the freaking answers to everything, but I can at least point you in a right direction, hopefully I hope, or or at least learn something new from your questions. Thanks for coming back for any point guard questions. Go for it, Christopher. So you had your you had your hand up there, man. So happy to. Happy to. Speaker2: [00:19:47] I think I've got a question about it's a tough one. I, um. I think I'm about to quit my job. OK, OK. And I just don't know if it's the right decision and it's probably I should explain it much better. Speaker1: [00:20:07] Yeah, definitely give us some context. OK, so you quit your job, but why what is what's the Speaker2: [00:20:13] I'm also in transition. I'm trying to get into Data science field, and I really, really enjoy NLP, OK? And I know this is something I can be really good at. So I made this goal that within six months I get an NLP job. So I've set some goals like things that I'm about I'm going to do in order to find a job. And I just realized because it's like a month and a half since I started and I realized this is this isn't happening because I don't have enough time. I've got a full time job. I've got a little daughter, and I really don't want to sacrifice my time with the family in order to do that. And I'm extremely unhappy at my work right now because I feel that my job is meaningless. I mean, it's it's not meaningless at all. I'm a software developer, so so I'm still coding. But the prior project I'm working on right now is meaningless. I'm not learning anything and it's taking my eight hours a day, but it's sucking my energy and my positivity because I've worked hard to to be positive in life. And like, my job is the only source of negativity right now. So it was Monday after my holiday. I've had like two weeks holiday. And Monday was like a really tough day for me, realizing I have to work again. And that's the last thing I want to do. And since Monday, I'm just like, should I do that? Should I do it to do it or not? How can it I mean, I know I don't want to stay, that I won't stay there anyway. I mean, it's only a matter of the time. But the question is, how does it make me less valuable for the future employers? I mean, because I'm I'm going to be unemployed, so I'm going to have a gap in my CV. And I can I can explain it very well because I know what's important in my life and I know that this job isn't important at all. And how can it hurt my future recruitment? Speaker1: [00:22:34] Yeah. Yeah. So there's a couple of different ends we could start out with this problem. First is that if you want to get philosophical with it, we could we could start there or we can just move right into the the the practical repercussions of it. So depending on where you want to start, I'll tailor my answer in that direction Speaker2: [00:22:50] And let's go philosophically. Speaker1: [00:22:53] Ok, ultimately meaning is it's we all do our own meaning. Right. So meaning meaningfulness in itself is just a meaningless concept because it changes for any anybody. Right. So if you see yourself as a person are not deriving any meaning from the work that you're doing, then. Yeah, I'm just it's not a good use of your time. You can you only have so many years left ahead of you, so much time left ahead of you. And at that it is not even guaranteed that you have that time. You can leave life right now. Right. You could drop that right now. It's a very small probability that could happen. But you can. So knowing this, do you want to spend your time doing a job that you really, really do not like? Right. Because in order for you to do great things in this world, you need two things. You need free time and a free mind and clear mind, rather free time and a clear mind. Right. Sometimes you can get a job that provides you with free time, which is ample time to think and reflect. But you're doing so towards something that is very constructive for you. You are defining or deriving meaning from it. So that's great. Or you can be in a situation like yours or your time at work is completely tied up. Speaker1: [00:23:57] And not only that, your mind is not at ease. You just like stress the hell out, liturgist inner turmoil in your mind so that even when you do have free time to work on stuff, you're dreading the time that you have to spend sitting in front of the computer doing work that you don't like. So that's that's not a recipe to to do anything, I think, productive or great in life. Right. So there's that aspect of it. Your feelings are completely validated, like it's meaningless to you. It is meaningless to you then. And that's that's it. And so so there's kind of a philosophical thing. I'll stop there for a touch unrelated. We can't. But practically speaking, if you quit your job to pursue some NLP projects on the side to reeducate yourself, I think that is completely OK. As a matter of fact, I think we as a society should encourage people to do that. We should encourage people to take a year off to reassess, redirect and find a new direction if they feel like the current one they're in is not helpful. Right. So now the question is, if you come to a if you come to a hiring manager, right. And the hiring manager, see this gap in your resume, there's a difference between having a gap on your resume where all you did was quit work because you hated your boss and sat on your couch and watch Netflix. Speaker1: [00:25:07] A year and I decided to come back to work, that doesn't look that good, but if you take a year off to work on upskilling yourself in a certain direction, because this is where you are deriving meaning and finding real interest and and passion in, then you could easily make that a positive for a hiring manager. Right. I got to say is in hiring managers and they. Well, look, I got a gap in your resume. Why is that? Like, you know what? I quit my last job because I wanted to upscale any particular domain in this case, NLP. And I use that time to create two or three different projects here. Here's what I did with my year off, I was able to not only learn the fundamentals and basics of NLP, but I was able to excel in it, do well. And as a result of the time and effort I spent away from earning income, I was able to create these projects, which I think do a great job of showcasing my ability to deliver value for you in this NLP role. So if you have the financial means to to quit your job too, I don't pursue this upskilling, then by all means go for it, because at least then you can leave work on good terms. Speaker1: [00:26:15] Or it could be that when you go leave work and you tell your boss why you new day. Look, I don't want to pursue NLP. I don't I don't have time to do it at this job. Maybe they have something that some other subsidiary at the company where you can have this opportunity or maybe they'll give you opportunities for you. If that's not the case, that's not the case. But at least you're ending this work engagement kind of on your own terms and in a positive way. Right. It's better than getting let go because you're just completely disengaged and just not doing work right. Even though even though that would probably if you if you get laid off, you probably can collect unemployment benefits or something. I don't know how it works in Germany, but that's my perspective. I think you are like if you're at work and it just completely sucks and you have you're not able to to pursue what you want to because of this giant time commitment. I definitely go for it. Alternatively, you can try to wake up two or three hours earlier every morning. Speaker2: [00:27:08] I think it is already. Speaker1: [00:27:09] You do this already and it's still not enough time right now. Speaker2: [00:27:14] So so I've got time to work on side projects. But like I said, I've had this idea what else I could do, like sharing my ideas, like writing articles, like everything that could help me that I also really enjoy. I just don't find time for it. And that really sucks. And right now I'm like, I'm waking up at five a.m. every day to work on the project. But it's a side project. It's a passion project. So I don't have anyone who can, like, guide me through and I'm stuck. And because it's difficult, so I'm stuck. And when I'm stuck, it's like I don't produce outcome and this sucks also. Speaker1: [00:27:54] Yeah, well, you don't necessarily even need to produce outcome on any project. You just gain understanding and gain knowledge. Right. So if you get stuck but you're stuck in a sense that maybe you're not moving past a hurdle, but you're still chipping away at your lack of knowledge and gaining more knowledge. That's still kind of a positive outcome, I would say. But I mean, it is a tough situation as he tries his hands up toward says, don't quit. Let's hear why. Speaker3: [00:28:16] Well, technically, I never it's like you mentioned that, you know, when you quit and quit on good terms, because technically this will also be your future reference and whatever position you will be going into. So making sure you keep the motivation, etc.. But I'm more curious that the company that you work for now is a large or small company. Any opportunity that you might be able to go and talk to them and say, listen, why don't you put me on a 50 percent job and then the other time I can do studies and maybe even get them to fund it. But you need to have some kind of justification that they will also benefit from that. How long are they going to benefit? Different story, but that might be an alternative to look into. There's an opportunity in your company. Speaker2: [00:29:01] Ok, so it's it's like I get maybe 50 employees and it's all relative, relative, small. But I mean, when I leave, I want to leave on good terms. Actually, already on Tuesday, I talked to my supervisor exactly about that. So I told him everything, how I feel. And and so he already knows that. But I don't think we can come up with a plan to, I'd say, to make me happy. And I like I said, I'm in transition. And I I've already learned a lot of machine learning and deep learning, and I even tried to apply it at work. So I organized this workshop to talk with sales at my company where I explained actually what machine learning and artificial intelligence is. And so we actually already do something, but it's going to take months and it's not going to be NLP is going to be more like computer vision. So it's also only temporary, I'd say, to I could theoretically stay, but it wouldn't change anything. And I've been already struggling with this problem for months, so it's not like it happened on Monday. Yeah, it really is. Speaker3: [00:30:25] What I refer to as the quick switch. There's this little switch up here in your head is called it quits, which was that's turned on. There's no turning back. Doesn't matter what you do, it doesn't matter what comes up. It doesn't matter anything at the end of the day that quits, which sounds like you turned it on. Once you've done that, now it's time to move on focused. And given that you are in that financial freedom decision, then, you know, just do it is better to quit now while you still it sounds to me like you have a very good dialog with your supervisor and the team. So the only thing you want to make sure is to say, OK, I'd like to make a proper plan for that quitting so that whoever is coming on board to replace you or the testing team will have a good transition. OK, because once you have that transition, then you go and then you do all the other things that you want to learn but do your job does the last thing I worry about, because when you come to the next interview, you're not going to be talking about, you will have one quick question, but you will already have that answer. And depending on your situation at that point in time, it may not even come up. So I wouldn't worry too much about the gap. It's more important now that you have a very good relationship, a good leaving this company. And that way when you apply for the next one, you can even use them as a good reference. You will have a good reference that I guarantee you if you do it properly. Speaker1: [00:31:53] And then I mean just that that the signal you're sending to any potential employer saying that I'm so vested in this field that I accept I quit my last job just so I can focus on NLP stuff. And here's what I produced in this time. I think that is huge. That's a huge plus. It's a good signal. So resume gout's. I wouldn't even I wouldn't worry too much about that. Let's see what I said. Speaker4: [00:32:19] Yeah. So, Chris, I'm kind of similar to your situation as well. But what I'm doing, I work for a small company, biomedical company. Right. And what I've done is because I am passionate about Data and stuff. So what I do is I've told my company that this is where I'm passionate about. I'm passionate about Data and I like my stuff and so forth. And they're fully aware of that right now. And I've already told them in my my self evaluation reviews that I have at my company, this is where I want to hit. And I'm already paving my way along that way. But what I'm doing right now is the same thing. Like I want to focus more on Data and stuff. So what I've done is I've I don't work full eight hours at my company. I cut down my hours and I work part time and I had the other hours. I fully dedicate myself and doing doing my projects and all that stuff. So I don't have the financial means of quitting my job because that's my situation here. So I still work part time and I still do my fashion stuff. Speaker4: [00:33:20] But the other thing is I'm also trying to create a project like in the past, actually, I'm trying to use the sales data that they have been trying to create. So I want to create something for them and then I want to leave. And if they see value in the project that I provide, perhaps they might create a position for me as a data scientist or whatever scientist or whatever. So I'm hoping I'm giving them options right now, you know, and this is what I'm going to do for you. And, you know, and you are fully aware that I I want to be a Data person. And I've given all the heads up quite a while ago so they know where I'm coming from and where I'm headed. And I will do that project for you. And then you tell me you want me to stay or go. So so that's what I'm doing. So I just reduced my hours down by half. Yeah. Again, because of financial me, I still need to pay my bills and all that stuff. So that's that's the route I took I'm currently taking right now. Speaker2: [00:34:15] I see. Thank you. And at my company company they know that I do it since January twenty twenty. So it's like it's because of the my company. Was it a difficult situation two years ago, that difficult financial situation. So right now we don't actually choose projects, we do projects that provide money. And I'm with them. I've been there for four and a half years now, but it's it's taking too long. So like I said, there was already this switch. Quick, quick switch. It is on. And I'm really trying to do this the right way. And but like I said, I just wonder if it can help me in the future. But I'm. What I heard Speaker1: [00:35:07] Here, yeah, I don't think I heard you in the future, but for the reasons they said, because they're signaling that you have such a great interest in this and they're using that time productively and you're making improvements and you're learning, understanding. But now the question is, can you find it NLP job right now? Like, can you start applying for jobs already? Do you have anything in your portfolio project that you can showcase to potential employers to get right into NLP without having to worry about quitting? Right. Because I think you're a software engineer. You've already got more than half the skills for Data science and plus NLP. It's mostly software engineering. It's not. There's some statistics, obviously, and machine learning, obviously, but I feel like it's more heavily influenced by software engineering. You already have more than halfway there, like 70 percent, barely. That's enough to get you a job, a first job. I thought about Speaker2: [00:35:55] That. I mean, I want to start applying right now just to see what's happening. But at the same time, it's like I said, I just can't work on what I love because, yeah, it's it's really like it was two months ago when I discovered NLP more deeply than before when I realized this is that this is something that I really enjoy doing. And what you say that this is software engineering and mostly. But NLP is also about language. And yeah, I love this language stuff. Yeah. And I believe that this makes me much more valuable because I really enjoy it and I enjoy it. I'm curious about it. And I ask more questions and I go deeper and I just want and this made also my job more difficult because when I know that there is something that I love, something that people get paid for and they get paid well, and then I have to go to work for eight hours. Yeah. And doing this stuff. Yes. This is more difficult than it was before. Speaker1: [00:37:05] So I mean, let's try to pivot from that mindset. Right. Let's just you know, right now you got your job. You got to do the job. And you know that that's let's let's try to pivot from there and start looking more at any opportunistic mindset. I would say right now, if I was in your shoes, this is what I would do maybe from now until the next, you know, until the end of May, I would just apply to every single NLP related job that I could potentially find. Why? Because I want to get a signal from the market. Right. So if I'm applying for jobs right now and I've got maybe one or two small ish projects that I could showcase as part of my portfolio. Right. I can start sending the signal to the market and see what comes back. Really. OK, do people view me as this NLP role yet? Right. And maybe you get a few callbacks for interviews and you go through the interview process and, you know, maybe when you crush maybe one, you don't crash, but you're learning throughout the process that that would be the first step is right. Let's see what see how the market views me right now without me having to leave my job for a month. And then, OK, if you get to the point where it's winding down towards the end of me, like, all right, well, I'm not hearing back from any jobs. Why aren't why am I not coming back for many jobs, do some reconnaissance? What's the feedback you're getting? Right. And if the feedback is not enough cocoa NLP experience, then you just make sure you develop like a really cool NLP project. Right. So then maybe you you do end up leaving your job at at the end of May, maybe put in your two weeks notice towards the middle of May. Speaker1: [00:38:31] Right now, come summer, June, all the way through August. I have a plan where you got to have the plan in place. I what is it that you want to create. Right. Because you need I can't go into this unemployment thing completely blind. I start thinking about the project you want to create right now. That's just going to be like your calling card of sorts. I started thinking about the actual project that it is that you want to create. I maybe maybe the project could be taking all of the @ArtistsOfData science podcast transcripts and creating a something from it. I don't know. Right. I'm giving you just ideas. Right. Maybe it could be like I don't know. I don't know much about NLP what can be done with NLP. But just have an idea in mind that this is where I'm going to try to build and just focus on that, because I don't want to go into this unemployment thing with nothing planned out. Right. Oh yeah. Let's say use now until the, you know, one month from now to apply for jobs, get feedback from the market, get signals from the market, start ideating on what it is that you are actually going to work on. You know, if you have to go into unemployment, meaning quit your job without any jobs lined up. So then you know, you have a purpose to carry you through the next few months to work on this project. That's a bit of tips I give. But yes. Are you going to say something now? Speaker2: [00:39:49] What I meant is like I already had a plan, but it's not working because I don't have a time. Speaker1: [00:39:55] Yeah, ok. OK, yeah. Speaker2: [00:39:57] So I've got plenty ideas because NLP is like endless list of ideas and I want to. Like thousands projects to work on. Yeah, it's Speaker1: [00:40:09] Not a problem. OK, so you got some good ideas and stuff that you want to work on that. That's a huge, huge step. Yeah. So, I mean, that's probably the best way to kind of go about this is, is to send some applications out, get some feedback from the market. Do people see me as a NLP practitioner yet based on the current projects that I have and do that for like the next month or so and then start planning your Cordarrelle transition out of your current role and have concrete steps in place for you know, I know you said you got a bunch of ideas, but just focus on just one thing that you want to do, go all in on that and then probably towards, you know, I'd say end of July, beginning of August, start going super aggressive with, you know, the job applications. And this is like contacting people, reaching out, sending good messages out to people on LinkedIn. Um, yeah. I mean, I'd even started trying to make connections with NLP people and, you know, in Germany, like now to start connecting with. Speaker2: [00:41:05] I've already done it. I already started. So, yeah, Speaker1: [00:41:09] Sort of meet up groups and everything as well. Speaker2: [00:41:11] Meetup groups are on hold. Speaker1: [00:41:14] Well, there's there's virtual ones as well. I know we have a bunch of Meetup groups here in Canada that are that are virtual. I see. Yeah. Speaker2: [00:41:22] So, OK, thank you for everything you just said. I mean, all of you. That's great. I'm happy to come here. And what you just said about one goal, I have got this book. It's called Four Disciplines of Execution. Speaker1: [00:41:37] Oh yeah. For the X. I've got that somewhere here. Speaker2: [00:41:40] Yeah, exactly. So I know exactly what you mean. And I also read this book. It's called How How Will You Measure Your Life? And this is also where I write about it. I mean, why people lose motivation and joy at their job and what makes people love their job. And I think it's I mean, I think I just was looking for confirmation confirmations that it and I think I found it like everywhere. I mean, like the quitting the job is right. And it's all over the place, actually, when you when you look for it, do what you love, stop wasting your time. We've got one life. And like I said, I don't want to sacrifice that time with my family because it's way more important than everything else. And I've got this, like long term plan, who I want to be in three years from now. And it doesn't mention my current job at all. Speaker1: [00:42:43] Yeah, I do. Ultimately, I do things that that make you happy. But it's not always about about making money or having steady PDF. I mean, what's Nassim Taleb say? The three most harmful addictions in life are heroin, carbohydrates and a monthly salary. So you know that I think what he's trying to communicate with that is that taking the safe route of always having that monthly salary, yeah, we need money to survive. But if you become complacent and unhappy just so you can have that monthly salary, then are you really living, I mean, to say you had your hand up or sorry and. Sure. If you have a question. Speaker3: [00:43:19] A question. Speaker1: [00:43:20] Yeah, sure. If you have a question, go for it and Speaker3: [00:43:22] Go Speaker4: [00:43:23] To our go for it. First, let me just finish up. Give me just a minute. Speaker3: [00:43:25] You know, I was just going to say that work isn't everything learned the hard way, 14 hour days, seven days a week, four years in a row, that kind of does something to you and, you know, having fun and fun at work. But it's always been what's most important to me. I don't really care as much about what I do, but being able to have that joy of getting up in the morning and being excited to go in and deal with everyday whatever problems, whatever challenge is thrown away and do what you love. That's how you get success. However, the way you look at it, because it gives you that unconditional motivation to to succeed, you will succeed because you will always have that smile on your face, which means you track differently with other people, etc.. I chose the consulting lifestyle after my last corporate job. I've been doing it now for seven years. I don't miss the corporate life at all of the paycheck. You know, the security of having that monthly paycheck is nice. It's a little bit more uncertainty and especially the at the future. But somehow you still survive, OK? And you will always find that way. It's a balance. So stick when you're gone and value the family because you do not know what's going to happen tomorrow. And the same thing goes with work. You have no idea what you're going to do tomorrow. And at the end of the day, the employee does not, quote unquote care about it. Speaker1: [00:44:59] Yeah, yeah. Man, there's another Nassim Taleb quote I remember hearing I think it was in the bed of. It was something along the lines of he said that the best way to get to control a slave is to convince him that he's an employee, something along something along those lines. Right. I mean, obviously, work is important. We obviously must do something with our lives. But I mean, you're not going to get rich renting out your time. Right. And he'd think of it that way. Like, it puts it in perspective. Like, I mean, I don't know why I'm rambling on about this, but I hope to one day be free of this, having to work for anything I like. I work in the sense that I have to show up at somewhere at a certain time. After a certain way, I have to speak a certain way. I have to have certain meetings with certain individuals. I have like this stuff. To me, it's like not like I would rather I'd much rather read and research and reflect and then teach that back to people which I kind of get an opportunity to do through Data and extreme job. That gives me an opportunity to do that. Like I'll explore a topic for a month and then I'll teach about it, or I'll just do calls like this and try to inspire, motivate people. But I'd much rather spend my time just studying and learning and reflecting and sharing that back with other people. Let's go to Cristoff and then Sue. Man, I Speaker2: [00:46:13] Just want to say that I mean, I guess all of us know this and find the job you love and you'll never work a day in your life. And I also found in this how will you measure your life? And I truly believe that I'm already successful because I found that job I will love I know this and it's and I've been all over the place. I'm like machine learning, artificial intelligence, data science. And I've found it. But I was working hard to find it. And I just don't want to waste any more time. Speaker1: [00:46:50] And that's a good realization to have. Let me be completely honest with you. Yeah, I work as a data scientist. I don't know if I necessarily love data science. I mean, I love studying randomness. I love studying probability. Those things I genuinely love. I mean, I don't even love statistics. I love probability and randomness for sure. These are things that I thoroughly enjoy studying and learning and reading about. I mean, apart from being an options trader, like what do I do? Those skills. Right. Sort of become a data scientist and build models. Right. But then even then, I'm not sure that this is something that I truly love. They'll probably be a pivot for me at some point in the future. And at least Data science is giving me some way to get there. So give me a platform to jump off of. Um, and I mean, I like to think that I'm good at it and I'm OK, you know, I enjoy doing it, but is that what I really want to do forever. I don't know. I really don't know. Um, but I mean to man if you have a comment let me know. But if you have a question, I'm going to have to ask you to go after ushe. So, um. OK, cool. I should go for it. I see that you're back Speaker4: [00:47:52] So I intend to keep drinking. Speaker3: [00:47:54] No problem Speaker4: [00:47:56] Sir. I like to take the topic away from the account. So if you if you want to. Speaker1: [00:48:01] I'm sorry. No, no. Go for it. If you have a question, go for it. Speaker4: [00:48:04] Definitely go for it. I'm rooting for you. There you go. So I had to I have two questions actually. The first question is in regards to I know it was discussed on the well, sorry to just go on Sunday, but I still have a question on it in regards to the projects you have when your GitHub breaks. What how what number is too much, first of all? And secondly, what areas do you focus on? I know there was a mention of trying to solve a business problem and trying to showcase your skills. Yeah, but how many do you have? Speaker1: [00:48:35] Not too many. Not too many. Right. You don't want to confuse anybody. There's also the paradox of choice to present anybody with so many things on GitHub. What if I mistakenly by not even mistakenly, just randomly, because that's how we behave, just click on a project that just happens to be the least, uh, favorable project for you. Right. So just have projects, two, three projects at the most that really, truly showcase your ability to do the job. You don't want to have half finished projects or ten different projects where it's just kind of all over the place, just two or three projects at the most that are just absolute indicators that you can do the job in terms of content, what they what they have. Content doesn't necessarily matter as much as you are able to showcase your thought process. Right. Essentially, what what what are you doing a project for? Hopefully you're doing a project for two key reasons. Number one key reason is that you are trying to emulate real world Data science, are trying to understand the practice. You're trying to flesh out your set of principles through a project. Right. That yes, I can go from a abstract idea with Data to a fleshed out solution with a conclusion. Right. And that the chain that takes me from A to B is logical and coherent, and it's constructed in a clear, concise manner so that whoever is reviewing the project can easily follow my train of thought. And I'm shedding insight into why it is I'm making every decision that I am along the way. Speaker1: [00:50:04] Right. So. What I look for in a project I've said this countless times is a clean repository structure. So one project is structured very cleanly, meaning you're following a logical template, maybe such as Caddo or, um, cookie cutter Data science. Both of those have really nice repository structures. You're writing good, clean, well-documented code that clearly has making good use of NLP principles and fundamentals. Right. So making sure your code is clean, it's modular. You've got comments. You're telling me exactly what the function is doing, right. That you're not just including a bunch of random bits of exploratory data analysis just to show me that you could do info had that describe, you know, making sure that whatever it is that you're showing me in your notebook fits this part of the narrative. Right. So that brings me to the second point is that you're trying to demonstrate your ability to think clearly during the project so as to fold kind of thing. That one is your ability to come up with a question and find a path to a solution while showcasing your technical ability to write clean code and follow a certain set of principles and to show that you're able to clearly think through a Data problem. Contents of that doesn't really matter as long as you're showing those things because you abstract enough away write everything kind of looks the same, right? Like everything, you know kind of will look the same, if that makes sense. Yes. Speaker4: [00:51:32] Yes, it does. And that brings me to my second question. Yeah. When you're starting a new role and you're dealing with a lot of data, right. You're coming into very rough work done for lack of a better word. There's no set, clear structure that's there. And a lot of the data is multilingual. It's dealing between countries that speak different languages. So my question is, when you're coming into a new role and you have a provisional one, you get your you have the expected things they expect you to achieve in the first two months, second, three months second. What's your approach to getting into a new year begins studying everything what you knew, what you were trying to get into a new role in a company? Yeah. Speaker1: [00:52:09] So pick up the book the first 90 days. I mean, I can probably give you a link to to my article file for that. But the first 90 days, it's an excellent book about what to do when you first start a job. So definitely check that out. But the specifics to your question is you talk to your manager who hired you like I am sitting here. What what do you expect to see from me within the first three, six, nine months? Right. So I can't give you any more specific advice or information from that because I don't I don't know the company. I don't know the projects. I don't know what the state of the world is for them or what's important to them. Right. At the end of the day, you're hired to help them solve the problem. You're hired to help them do something. So it's on you to go figure out what that something is that they're trying to achieve and how you're a certain set of skills can help get them there, because ultimately, whilst they were also paying you, if not to to help them, so have that conversation with your boss, I guess Speaker4: [00:53:03] When you answer the question is when I realized I might have missed it. Frankly, what I actually mean is when you come into a role, sometimes people expect the expectations are too high to the extent of things you can do. How do you handle the expectations, especially when you're new and you handle expectations? And you said, no, this cannot be done now Speaker1: [00:53:20] By asking them questions and just drill down the questions like, oh, so you want to do this, this and this and then spell it out clearly and just say, OK, well, in order for and just give them time estimates and say, OK, if you want this to get done, well, I need to do these ten different things, each one of these ten different things. Me alone will probably take me this much, this much, this much time. Right. And you're looking to accomplish just one thing. It's not going to happen overnight. Here's all the different pieces that have to go into it. Here's how long each piece is going to take and how much it'll cost me to to do that, you know, and just be upfront with those. Right. Like, if if there's something that you cannot do, tell them, you know what I mean? This is nice to have what you're saying. Like, I can definitely understand why it is that you want to accomplish this, but it can't be done given the current state of things. I had this thing at work where it's it was a seemingly easy problem to solve. And it's in it definitely, in theory, very easy to solve. But the Data underlying data was so inconsistent and so dirty and messy that I was not able to get to a solution even though I had the right, even though in theory, even though I had the methodology in place, it's just the fundamental the fact that the Data was not in good quality means that I was not able to produce a solution of any value whatsoever. Speaker1: [00:54:33] And so I just communicate that. But yeah, once we started getting some stuff in place for Data quality and start collecting more data, then yeah, we can probably revisit this. But given the current state now, that's not something that that can be done. You just stated up front, that's the only thing you can do. And sometimes you might they might have a question on surface and you think like it can be done, but you shouldn't say, oh yeah, that's something I could do. You should always say I hold the seems in theory that it sounds like something that can be doable. But give me half a day to dig deeper and figure out more in. Focuses on what's going on to let you know how long it would take you to do something right. Does that make sense? Speaker4: [00:55:11] Yes, I think that's exactly the problem I've been having. Yes, I'll do it. Sure. Yes, sure. Especially with the ad hoc requests. I want this. I want that one day so that Speaker1: [00:55:20] You can't do that. No, no, you can't do that. Can you just tell them like I especially with ad hoc requests, like you can never say yes to anything right off the top. Your first response always should be. Well, it depends. Give me half a day to look into it, to figure out if this is doable and if I manage to do it in that half a day, I'll have the results for you. But until I look into this a bit more and give you an estimate of time, I can give you more than that right now. Right. That definitely don't take everything on. Thank you. Yeah. Um, people think machine learning is magic. They think Data scientists. This is magic. It's really not for sure. Canceled everything. Um, yeah. Hopefully that was helpful. So let's go to Simmons question. Speaker5: [00:56:02] Yeah. I might have a little longlong it's like a glitch. So I've been I've been in the oil industry for like eight years and the Data. So a lot of a lot of software color coding was not my was not required much. The Data science part is like in that process. Look, each of the algorithms that we use, it is like the heart of it. But I used as a I was I was looking at it like a scientist. Right. Rather than R&D. If I were not in the I would have thought of coding experience. So I spent a lot of time like analyzing, processing and basically creating the end product and selling to the client. So that was a experience I still didn't have to code. But it has a lot of technical stuff like the software platform that we have developed. It is still like a mini version of coding. It's a lot of parameters and testing the codes are so like three or four years ago, I followed that for a long time before that. Actually, statistical learning is one of the key to our process. Machine learning was very new to me. I didn't know what I thought was a robotics and all that kind of stuff. And then when I took a statistical learning at Stanford and Data, and then I started like delving more into it and went through a bunch of classes with Standring, the deep learning. And while I was at home, I managed to get the certification, those things. And last year, last year I was I was hoping that I was I was waiting for a one final promotion at work. And then after that, I was planning to quit and then and then look for the Data science outside the oil and gas. Speaker5: [00:57:48] And then the unfortunate accident that happened. I did get my promotion. And probably because of that, I was I was on the target list for the headcount reduction when the corona happened. So, I mean, I still don't believe that I was let go because I was I was really good or decent. So I'm not working with my company anymore. I know it's been almost like eight or ten months, but I didn't I didn't live in the bad times like they said that they might hire me if the business is back again. So but I don't want to go back again to the oil and gas anymore because this happens over and over. And I have already seen like five and ten rounds of layoffs in the past eight years. And that really freaked me out. And so. Well, I'm not sure that the most the the most negative thing of my current experience is that because I lack a lot of the software, the coding side, it's really hard for me to like present in the market. So I've been giving interviews last year and a lot of time I, I wouldn't be able to get the second interview like the first interview. I wouldn't make it make it to the second round. And I don't know why. And I've been coding for the last two years and on my own I'm taking a lot of data science classes like bootcamps and stuff like that. So what would you suggest in this in this circumstance? What's that I evaluate for myself and what I'm not getting attention, even though I think that I should be I should be a very good candidate for Data science. Speaker1: [00:59:19] You have a project that shows that you know how to do stuff, or are you just taking classes hoping that that's enough to convince somebody that you are qualified to get the job? Yeah, right Speaker5: [00:59:28] Now I have a couple of projects, two or three projects that came through the the class as well, but not a very big project yet. I didn't have I haven't done the capstone yet, but something up to the level of linear regression and of course in some deep learning about it, Speaker1: [00:59:46] I think first thing is just to do a project like a really well structured project, like I mentioned earlier, some qualities of projects that people should should take on. So definitely make sure yours are up to par with that good repository structure and everything else had mentioned to Toisa. There's also a bit like a fifteen minute. On a story by Data or Data Chedid YouTube channel about five tips for getting a project, making a project, let me get you hired. So definitely check that out for a lot more detail. But if you're saying like I mean, it sounds like you have identified that coding is a weak point for you. So keep on practicing coding, keep on getting better and better at that. Maybe start looking for roles that are not necessarily just Data scientist by title, but look for roles that are adjacent to that Data analyst operations research. Right. Those type of roles that are adjacent that can help as a stepping stone because coding is important. Obviously, as a data scientist, you don't have to be a software engineer, but you got to be able to write good code. So hopefully you're keeping track of the types of questions that you're getting wrong so it can go back and revisit those and get up to speed on those and make sure you understand why you got it wrong. That's that's key. Speaker5: [01:01:06] Most of the time what I get is that everything goes well. And then and then at the end they would ask me, do you have actual production? You have a coding like did you like code any into the production, like an actual corporate level production job? You know, every Speaker1: [01:01:22] Single time they say that, yeah. Speaker5: [01:01:24] A lot of times, like most Speaker1: [01:01:25] Of the and quantify that a lot of time. Most like out of what percentage of how many interviews because 80 Speaker5: [01:01:32] Percent, 80 percent. And even it is a screening part. Right. So it is not a coding test yet. They don't. So ask me about. Yeah. Speaker1: [01:01:41] Are you going for roles that that just by the description of the role, look like they're very, very heavily software engineering focused roles? Because I don't think you necessarily need to have done several models and production to be a data scientist, maybe for going from machine learning, engineering type of roles. That's possible. But I don't think that you need to have necessarily deployed models to production to have a job as data scientist. You need coding skills for sure. So make sure you got those down. If you're if you're if you're going to coding interviews and you're getting certain particular types of questions wrong, focus on making sure you get those right in the future. Speaker5: [01:02:16] Right. Yeah, and that's what I don't know what kind of coding question I would get. Speaker1: [01:02:21] Yeah. So you want to you want to check out websites like Hacker Inc is a good one. Leading cause is a good one. There's a particular Web site I like a lot for learning Python called Python Principles and I'll link that right here. So Python principles will teach you just the fundamentals of Python from the ground up. But then it also has these types of like challenges that although they might not necessarily be the level of difficulty that you might get in a hacker and challenge, they're still really good questions to kind of get you thinking about how to solve problems with code. Speaker5: [01:02:51] Um, yeah, I think so. One question is that a lot of this coding website is geared towards software development. Right. Or the actual programmer. And so in a Data sense, the how important is to have that side of the coding, because from Data science coding perspective, I think I have met a lot of I mean, quite significant progress so far. I'm pretty comfortable with coding for the Data science perspective, not like software development, but. Speaker1: [01:03:19] Yeah, yeah. So maybe the types of roles that she's targeted are more research type of roles and production or each of type of roles, because there's definitely a Data science roles that are on product teams. Right. And if you're a data scientist on a product focused type of role, you're going to have to be able to write production quality code because you're deploying features into a product. Right. So if you find yourself going for like Netflix or Facebook or whatever in those type of roles where the result of your work is being implemented in some product, then you definitely you need to have that that software engineering down pat. But if you're going mostly for roles that are primarily focused on forecasting or predictions or research and development type roles, probably not so much. Right. Um, so I'd say just keep applying for jobs, but maybe make sure you're not applying for jobs that are highly focused on product. Um, I see Torres's hand. So if you want to try and go for it Cristoff. Thanks for hanging out Zarabozo. Speaker3: [01:04:20] When I was just curious because OK, from the Data point of view and all the skills and the projection is matching what you were referring to, the oil and gas industry. So I'm just curious what area, because technically I wouldn't give up on any field or specific industry. I'd gone through four of those headcount reductions in the oil and gas industry would always start late back and forth. But I'm just curious what background you have from the gas, because technically, if you were a business development and the skills that you gained from that should be very relevant for a lot of other industries, industries, even from a supplier point of. You and when you're bringing a lot to the table and then with the Data skills and their knowledge skills as well as procurement, I mean all of these there are plenty of projects going on in the oil and gas industry on improving and using Data. Right now, there's tons of them. And in Norway, which I'm familiar with in the U.S. and Canada, among others, they're always looking for people to assist in those projects with those skills. And that background is something you looked into. Speaker5: [01:05:37] I totally agree with you that unfortunately, I was on that segment of the whole industry that is pretty focused and narrowed in itself. So my background is I am a I'm a geoscientist, a geologist. And my my job was to go into anything. So basically I would go on the ocean or whatever land, what they are looking for, the oil. We will get the seismic data and we will create a subsurface image of that. And so and we we do process a huge amount of data like the last 10 terabytes of data. So that's my sights are totally isolated from that business. I mean, I wasn't I wasn't involved in network of sales, but it's still like really narrow in that geoscience community and and dealing with images and digital signal processing kind of stuff. Speaker3: [01:06:30] Right. I it is a very narrow field, but that's the I mean, it's the core of oil and gas that you've been working on. And you do for sure, based on your experience, work a bit and understand the processes that go through all the networks. I mean, analyzing these type of data, it's huge amounts of data. I'm familiar with it. But to me, that skill set that you bring should be very relevant for a lot of other industries. And also from a supplier point of view, you have been on the analysis side of it. There's plenty and by my organization that require your type of skills now because they're offering those services. What's also happening in the industry is that they're not consolidating and share in this type of Data among the companies. There's a more openness to sharing which allows the company, which also in my mind means that there should be more opportunities for this data mining data analysis, the data transaction and all of these things that come along with it. So I would just say, don't let it go, just kind of keep track of it. And and Data is DeKnight my mind, whether Web site, geological or statistical or customer base, the end of the day, you need to understand it and apply it. And if you do that, you should be OK. Yeah, yeah. Speaker1: [01:07:55] I'm looking at LinkedIn profile like there's no reason why you should not get a job and Data science. I think it's just the fit for the type of roles are applying for you. From the sounds of it, it looks like you're mostly applying to product based roles. You should probably look for more R&D type of roles because that's really heavily geared towards what the type of work that you're doing. So an industry to maybe look for if oil and gas is no longer your cup of tea, precision agriculture could probably use your skills for sure. So I'm not sure if you familiar with the precision agriculture industry, but machine learning, computer vision, like the type of work they're describing right now, the agriculture industry is it's picking up on, uh, you know, they're they're using geo satellite data to help figure out exactly the right combination of nutrients to apply at a particular point of land. So, for example, a company based out of Canada right here in Winnipeg, Farmers Edge, that's an example of a precision agriculture company. So take a look at what they're doing. Go to Farmers Head website, check them out and then see who their competitors are and maybe apply to some of those roles. But I'm willing to bet that it's probably made a difference in fit between the type roles that you're applying for, given your background. But now that begs the question, do you want to be more on the business facing side? Do you want to do you want to develop product features? Like what? Give me a sense of the types of companies you've applied to. Speaker5: [01:09:19] Yeah, I think a couple of the more product product oriented and a couple of them were, I think, research oriented and but it is also possible that I wouldn't know that it is a product oriented or is it just that they are. But my my personal interest would be the predictive side, like casting or more insight. Speaker1: [01:09:40] Yeah. Yeah. So definitely let me look into more research scientist type roles. That's probably going to be more suited to your current background. And when you see roles for data scientist, if you notice that this involves roles that you're developing, features for products, then you are going to have to have a strong coding background. But at a bare minimum, I you know, I don't know what the quality code looks like right now. I've got. Can look at that next week or something. Has got to wrap up here, but make sure you're following a good structure for your fair project, right? Make sure you use either quadrille or cookie cutter to help organize your project. You're writing. I mean, be working beyond just a notebook. You can have a pipeline to go from raw data to a decision using whatever postscripts that's automated, usually executable, things like that. So it looks like you probably do have experience doing that. It might just be just, you know, more practice interviewing because, I mean, you got your background for the work you've done. And given your education, I you shouldn't be too difficult. Probably a lot more practice interviewing. So do as many mock interviews as possible. So for that, I would recommend prep preamp dotcom, some good ways to prep through that. So I went ahead and posted a link there, but that's all the time we got for today here. So hopefully soon and we could see here again next week. And then maybe we can take a look at your portfolio next week to see what it looks like and give you some pointers. But in the meantime, just do as many practice mock interviews as possible. Preamp is a good resource for that. I guys will take care, have a good rest of the afternoon or evening, depending on where you are in the world. Have a good day. We'll see you guys next week. Take care. Remember, you got one life on this planet. Why not try to be absolutely sure. Speaker5: [01:11:25] Thank you, everybody. Tor and.