HH63-2022-01-07_mixdown.mp3-from OneDrive

Harpreet: [00:00:09] What's up, everybody, welcome, welcome to the artistic Data science. Happy hour. It is Friday, January seven, 2020, to another year, man brand new year. Hopefully AIs had an amazing holiday break. Happy New Year to you all. Thanks again for hanging out with me on the the the end of the season. Then we had going on there. But yeah, man, it's good to get to get to get off camera man for a couple of weeks. It's odd when every single conversation you have is recorded and shared with the world for all to see, and it feels good just being able to not be on camera for a couple of weeks. So that was great. Recharging, refreshing. Hopefully, you guys had an amazing,

Speaker2: [00:00:52] Amazing

Harpreet: [00:00:53] Time off as well. Shout out to wrestle Ken. Vin, what's going on? Al Bell Museum in the House. Naresh, Alessa and Mr. H. Super excited for for you all to be here. So hopefully got a chance to tune into the comet office hours that was hosted on Wednesday. We had our good friend of the show, Christian

Speaker2: [00:01:14] Captive L, was in

Harpreet: [00:01:15] The building. We talked

Speaker2: [00:01:16] About pretty much

Harpreet: [00:01:18] Kind of how to translate a business problem to machine learning data science problem as part of an eight week series that we're doing at Comet. And it's all about standardizing the experiment. Hopefully AIs kid could join. Let me just read off a few of the events that we're having and putting in a lot of work for this. I'm super excited for for what we got planned. But yeah, we're, you know, first session kicked off to finding business impact with machine learning. Projects were coming in on Wednesday talking about how to define scope and success criteria for your machine learning

Speaker2: [00:01:49] Projects and then how

Harpreet: [00:01:51] To tell if

Speaker2: [00:01:51] You've built a good machine learning model.

Harpreet: [00:01:53] Learn now we've got an interview with the community member and then on January twenty six, understanding, validating versioning, [00:02:00] engineering and Data. So we're talking to Jimmy from pachyderm. We're talking to

Speaker2: [00:02:04] Abe from Super

Harpreet: [00:02:06] Conductive, also known as Great Expectations and community member Matt Plaza. That is going to be fun. Then we're doing another roundtable discussion on February 2nd with a good friend of the show something to live with. Also Susan Shou Chang, if you guys don't know Susan, but she's awesome. That's somebody who I really, really look up to and respect in the field. She's just she's a proper artist of the design season. I'm excited to have you on the show. And then also W Rodney Huang, who's that Google's research scientist at Google will be a great conversation. We've also got a another panel discussion happening on February 9th. A couple of awesome folks that will be will be joining us. I'm excited for that. So we've got Tiffany and Eddie. They're both principal senior level team lead data scientists. We're going to be talking about experiment management and

Speaker2: [00:02:53] How it can

Speaker3: [00:02:54] Make your

Harpreet: [00:02:55] Life easier. And then finally, we're going to close off by bringing on some in-house experts talk about the last mile of machine learning and beyond. So it's going to be great. I'm super pumped for this series. Every Wednesday, 10:30 a.m. central time right here on on LinkedIn, you can catch on YouTube as well. Everything is recorded and will be shared for you. Yeah, I'm excited, man. It's been been a lot of work going into this. I'm pumped for it. We're also doing a series starting in March. That's all going to be about deep learning for structured data.

Speaker2: [00:03:25] So I think a lot

Harpreet: [00:03:25] Of times people might just think deep learning is only for unstructured data like text and images and sound and things like that. But we're going to see applications of deep learning for just regular tabular data that you get from a relational database. That's going to be a lot of fun. I'm excited to put together the curriculum for that. As as always, everything is completely open, completely free. All the blog posts, all right ups,

Speaker2: [00:03:49] Everything unmetered on

Harpreet: [00:03:50] Medium. You guys

Speaker2: [00:03:51] Can go ahead and

Harpreet: [00:03:52] And follow along. So do do join your hands and be fun. So that's enough self-promotion. [00:04:00] Enough paying the bills, how you got, how you all doing, man? Twenty twenty two man shadow to me. Kiko Michiko is in the building, so let's how do we want to kick this conversation off? You guys got questions on on LinkedIn or on YouTube. Please do let me know I'll be monitoring the chat and comment section on both of those while we when we start off by what we're looking forward to this this year. Michael, what are you looking forward to in twenty twenty two?

Speaker2: [00:04:23] So rather than making the same

Speaker4: [00:04:26] Mistakes every year of doing 10 goals, I decided to do four. There were just the most impactful like would be, I don't know, the last couple of years I've been realizing that all my efforts have been kind of 80:20 only in reverse, where it's like 80 percent of my efforts were kind of like. Only driving 20 percent of whatever. So I think my four sort of focus areas or for intense are health money continued to develop into technical leadership is a big one for me. And fourth one is thanks to Ken Gee,

Speaker2: [00:05:08] I finally got the

Speaker4: [00:05:08] Courage to decide. I want to try to try to launch my brand. But first build up. A YouTube channel and like a content audience, so and I think actually that will be really good. I'm hoping to do. I'm going to start with visible things like jackets and all that going to move to sneakers. I love sneakers. I love making sneakers. I love designing them. We're kind of figuring out some of the equipment we need. And then ideally try to do the bridge between digital and physical fashion. So that's why I'm looking forward to this year. I still love engineering. I want to continue developing it into that and actually had a question for this group about technical leadership and all that. So I'd love to hear it. But those are my four things.

Harpreet: [00:05:56] That said, I love it. I love the way you're breaking [00:06:00] down the big rocks for four goals. I definitely can relate to you on that. Last year I was just I tried to do too much and spread too thin could not do anything. This year, I'm doing something similar to you just for big things, but I'm going to take it one quarter at a time. So quarter number one? Got something going on? That's the goal. Then we'll figure out what quarter two, three and beyond will be as we progressed the year. So look, do you guys want to just jump right into Mexico's question like, I do actually want to hear what you guys you know what you guys got planned for for the new year. So if we can keep it real

Speaker2: [00:06:34] Quick and then we'll get to Mexico's question,

Harpreet: [00:06:36] Let's go to Eric. Eric Sims, what are you? What are you looking forward to this year?

Speaker3: [00:06:40] Inadvertently came up with kind of like a three to one thing. So I was like, I have three books I want to read because I think I can manage three books in a year. Three books, two specific projects. And then I want to have a music, one song like a music project I want to do that's not directly Data related. And so I'm just excited to do all those things because some of them will help me on a career level, and others are just things I just want to do. And I I like music and creativity and stuff, and so I've been feeling like the itch to kind of get get more into that. And so that'll be a fun way I'm trying to bring together, you know, I think, you know, acquire the Don. He's really inspiring and I'm like, I want to do something like that. And like, I think I think I can, I have the tools to do it. And so I want to do something like that.

Harpreet: [00:07:28] I love it. Match that. I take care of the don. We will be recording soon. I just keep an eye out for that. I'll keep you guys posted on that. I mean, but you could make. This this music thing, somewhat Data driven ish like, you know, there's there's deep learning models that can help with generative music. If you're interested, I'll put you in touch with with my colleague Michael at Comet. He's actually a generative artist. He's a generative musician. So, you know, I think you guys can have a great conversation. If you'd like. I'll put you guys in touch. But let's let. Let's hear from Russell [00:08:00] Russell. What are you looking forward to? The Dr. Russell will go to Vin and Ken, and then after that, we'll get into the Chico's question about just genuinely curious what everybody is up to.

Speaker5: [00:08:09] So I've got kind of a twofold thing, think one, I want continued growth in the Data sector, both in my knowledge, my network and the things that I turn around. But given the last two years that we've had, I want to also concentrate on something outside of that and increase the whimsy and the gentle irreverence that I employ in my day to day lives. I try not to be too staid, too serious, and in that vein, I'd like to congratulate you for starting out saying do do in

Speaker2: [00:08:40] Your opening

Speaker3: [00:08:41] Monologue there Harpreet Sahota.

Speaker5: [00:08:44] You may want to listen back to that. You did say and, you know, had a little schoolboy giggle. So, yeah, I just want to find it fun in stuff and just, you know, keep doing the good work, but don't look past those fun elements and try and take as much positivity in all angles from everything you do.

Harpreet: [00:09:02] Absolutely love that man. You know, I'm going to do more this year by doing more every day, let me tell you what more means more ism and move on to observe, I'm going to reflect, I'm going to engage. Keep an eye out for a blog post releasing sometime early next week. About that, I could be more by doing more every day. Then what are you looking forward to this year? Then after then we'll go to Ken and then Michael's question. Yes, I did say, do you do in the, you know, I tripped up. So do you guys got questions on LinkedIn or here in the chat? Let me know what is the queue, but then let's hear from you.

Speaker6: [00:09:34] Well, first, turning my all IJI account into a kickstand when she comes out with those those kicks, it's just going to be an entire stream of me wearing her shoes. That's just that's the goal now. Sorry. All the other girls seem to be kind of secondary, but that's a winner. This year, I want to be more accessible. I realized last year that I was doing a lot, but a lot of what I was doing wasn't accessible. And so [00:10:00] this year, I want to be more accessible. I want everything that I'm working on to be more available. I want people to be able to interact with me a little bit better. And I want to, you know, same way to have more access to people that follow me, people that engage with me,

Speaker2: [00:10:16] People that I work

Speaker6: [00:10:17] With, clients, you know, just across the board. So that's been one centralized theme is accessibility. And the other one is I want to go back to having. I mean, this is kind of like the Russell said. I want to go back to having fun again. There's been, you know, I've kind of had to stiffen up a little bit as I make them move into strategy as I do

Speaker2: [00:10:35] More work with C-suite

Speaker6: [00:10:37] And board of directors. But I kind of want to let go a little bit at every once in a while because I was having a whole lot more fun when I was able to be more informal. So that's it's kind of a dual goal and our already knows this one throws apps. This year I'm going to get Thors apps

Speaker2: [00:10:54] That's

Speaker6: [00:10:55] Happening by May. I will be at a pool in Vegas, just surprising people with my age and apps.

Harpreet: [00:11:05] Love it, yeah, I'm also getting towards the abs, but it'll be a T-shirt, can't go for it.

Speaker6: [00:11:12] A really, really beautiful goal. I like that. I think we should all maybe aspire for that. So I had to drop some stuff down because I had to consolidate my thoughts. The first thing that I'm really excited about and I want to pursue more of this year is actually seeing people. I mean, I've never met any of you in person. And hopefully later in the year, that'll become a lot more possible. I'm trying to travel a lot more and trying to get out and explore. You know, it's great to do stuff from behind the computer screen. There's a lot of conveniences to it, but I think there's something inherently human about real life social interactions. I could go on this giant

Speaker2: [00:11:57] Tangent about the

Speaker6: [00:11:58] Metaverse and and [00:12:00] those types of things and why I think, you know, human interaction is so important, but I'll leave that for another time. Another thing I'm really trying to do more of this year is is measure myself like, keep track

Speaker2: [00:12:12] Of my goals

Speaker6: [00:12:13] And my progress and the things that I'm doing. I already keep track of my sleep with my ring. I was telling Harpreet I just bought a blood glucose monitor. I'm trying

Speaker2: [00:12:23] To track what my blood

Speaker6: [00:12:24] Sugar does after I eat every time. And I think that, you know, for me, just having more data on myself, learning about myself in a quantitative way is a very powerful way to understand where I can improve or how I can optimize.

Speaker2: [00:12:37] Or or maybe I don't mean after I

Speaker6: [00:12:40] Think of these things if I track them in some way, in some other weird, weird reality that I've created. The last thing runs directly contra to Harpreet, and that is I would like to do less this year. I think

Speaker2: [00:12:56] That I've gotten

Speaker6: [00:12:57] Really into meditation. I've really gotten

Speaker2: [00:12:59] Into this idea

Speaker6: [00:13:01] That my thoughts kind of come together well when I've given myself some time off and I don't, I obviously don't think this goes directly against what Harp saying, but like having more intention about what I do and I think I can get more intention by sort of limiting my scope and and narrowing down on what I believe to be the most important. So I'm also really trying to focus on like that pure meditation aspect. That, to me, is something I've read a couple of books recently and I'm like, Hey, I can tangibly improve my life and my philosophy and my focus and my resilience associated with that. So that's going to be a big priority. I'd like to have fun too, but I think that'll inevitably come. I would hope that probably the other people I work with wish I had less fun at work and just more professional.

Speaker2: [00:13:50] But hey,

Speaker6: [00:13:52] Get what you.

Harpreet: [00:13:54] Thank you, can't yeah, I highly recommend checking out this. It's a song on [00:14:00] Spotify, just brief mindfulness, breathing, and that's kind of like the guided meditation that I listen to. You know, there's an eight minute version in like an 18 minute version and just kind of walk through it. It's quite nice. So definitely check that out right on some Akiko lists. Let's go to your question and you know you got a question on LinkedIn. If you got a question on YouTube or right here in the chat, let me know and I'll go ahead and I will add you to the queue. After Mikita, we got a question from KDKA coming in from YouTube. So just sit tight and we'll get to your question. But Makiko, go for it.

Speaker4: [00:14:36] Yeah, so I guess the question I have in my head is the ladder into technical leadership. I have bookmark Vision's blog

Speaker2: [00:14:44] Posts on,

Speaker4: [00:14:46] I have that, I do have it and I start reading it. And I also have like, well, Larson's like staff in here

Speaker2: [00:14:53] Part the staff book.

Speaker4: [00:14:54] But I guess I'm just trying to understand. What are what are the main transition points between going from someone who's like junior slash engineering slash sort of senior, maybe a senior engineer to like staff to technical leadership as opposed to management? I have a lot of people. I consider mentors in my life who are trying to push me a little bit more towards the people management business management side. And perversely, because I love making life harder for myself, I obviously don't want to go there because I'm OK at it. I'm decent at it, and it's not interesting. So the technical leadership side, it's it feels very elusive because I see examples of people who like they have the great technical chops, but they're not really good at interacting with people and they don't really have a mind for strategy. And yet they're they're not. Not my current company. I've seen this other companies write down my current [00:16:00] company just in case anyone's watching now, but in various like contexts, right? Like, I've seen a different mix of skills or whatever. And I guess I'm just trying to determine for myself like, how do I bridge that gap knowing that it's like a probably a two or three year gap? Like, I don't think it's something I can just kind of clench in the year. I think it's probably going to be a little bit of a longer term goal. So we'd love to hear, you know, various thoughts, opinions. You know what people, what skills people think are important experiences? What do those conversations look like and even just the general of should I continue doing stuff that I'm really bad at and try and get better at it? Or should I be leveraging or doubling down more of my strengths? Because that's a very common advice we tell people. For some reason, I seem not interested in my strengths. So, yeah, I would love to hear opinions

Harpreet: [00:16:54] And hear some opinions, too. Want to go to a dove in for this one, but I could definitely relate to Mexico. Like if you would have asked me, maybe just a year ago what I wanted to do, I would be like, Yeah, I want to be like a director of Data Science, like I wanted to manage people. And then I got in a position where I started managing people. I'm like, This is

Speaker2: [00:17:10] Not like this.

Harpreet: [00:17:11] This is like, this is not it's not fun because I don't feel like I get it. It's like this romanticized notion for me. And when I actually got to do it and, you know, reading all these books and like thinking, I know how to do it and I'm like, God, I just don't like doing it. But anyways, let's go to Vin to hear from him. Mikey, I'm going to find a link for you. I was listening to a podcast earlier this week is called Junior to

Speaker2: [00:17:36] Senior Software

Harpreet: [00:17:37] Engineer, Junior Senior Software Developer, something. I'll post the link here, but let's hear from you.

Speaker6: [00:17:44] I think, you know, just to your point, leading great teams and leading teams of people that are iffy. It's different kinds of leadership. And if your first leadership gig is an awesome team like leadership, [00:18:00] all of a sudden seems kind of cooler because

Speaker2: [00:18:03] A lot of the things that

Speaker6: [00:18:04] Are not obvious that you need to be doing with low performing teams, they're obvious with high performing teams like it almost leads itself, and you kind of learn with a high performing team how to lead because they know how to lead themselves. And they in a lot of ways, just by being amazing and being high performing,

Speaker2: [00:18:24] They almost teach

Speaker6: [00:18:25] You how to lead. And so, you know, don't necessarily say I hated it because my first team didn't work out. It may just be that you are not that kind of leader. And that's the that's really important. If you were going to do. Thank you. If you're going to do like a how do you lead, you know, or how do you figure out if you want to lead? Step one is figure out what kind of leader you want to be and what kind of teams you want to lead and then move on to what type of scenarios. What type of business scenarios do you want to lead? Do you want to be a transformational leader?

Speaker2: [00:19:05] You want to go into

Speaker6: [00:19:06] Teams that are in trouble struggling

Speaker2: [00:19:09] And make them better.

Speaker6: [00:19:10] Do you like that process of improvement? And really, the first year is horrific and then year two and year three, there's kind of this rewarding Wow, we made it everybody.

Speaker2: [00:19:21] We did this.

Speaker6: [00:19:21] We grew that,

Speaker2: [00:19:22] You know, so you

Speaker6: [00:19:23] Could be a transformational leader who's cleaning up messes. You could be a transformational leader. So you kind of get what where I'm going at is. There isn't really a transformation to make. First, it's a decision to make.

Speaker2: [00:19:35] And as soon as you

Speaker6: [00:19:36] Figure out what it is specifically that you want to lead and what situations who, then you can pick the scenarios where you're set up for success because you have a capability set in the leadership direction. Already everybody does. And that's what people don't realize is everyone's a leader. Not everyone is a leader in every team scenario, business [00:20:00] transformation because every leader transforms in some way shape or form. But we're not all the same type of leader, and few of us can lead in a succeed leading and be happy leading in many, many different types of diverse settings. And so when you look at top leaders, you know, if you look at parachute teams, they get put together to come in and rescue a business. Everybody's got a role. Everybody's got a place. They know what they're doing. They know exactly what it is that they can be successful at. They've got a playbook

Speaker2: [00:20:35] And it

Speaker6: [00:20:36] Becomes this thing. That's enjoyable because you know that this is a situation you're going to drop into it, you're going to be successful. And that's a big part of your own confidence. And I would say the second part of it that's huge is figure out your sources of authority. Everyone derives authority from different places. And this is the first thing to learn about yourself is to figure out where you derive authority and where those are strong sources of authority. And what scenarios are those weak sources of authority. When you talk about working on things that you're not good at, start learning how to build authority using sources that you're kind of weak at. You know, because right now you're going to lean on your technical chops. You're going to lean on your ability to communicate your ability to bring ideas

Speaker2: [00:21:24] To people and persuade them

Speaker6: [00:21:26] You're going to lean on that. But in some cases,

Speaker2: [00:21:29] That won't work. What are those

Speaker6: [00:21:31] Situations that you would be set up to fail using your current sources of authority?

Speaker2: [00:21:35] And how would you learn new ones?

Speaker6: [00:21:36] What new ones would succeed? Because everyone's framework for leadership is different, but the source of authority is kind of the universal. And you can begin to narrow down what scenarios, what teams, what transitions, what you're going to be successful in by looking at what you're good, what your good sources of authority are.

Speaker4: [00:21:58] Yeah, that that is true, [00:22:00] and it's interesting. I didn't think about it that way. I know when I was reading one of the blog posts, I'm like, Well, ours is like staff entering post. He does have like this these different archetypes of like staff. Plus one is like he calls it the right hand of the executive. Another one is like the architect, you know, like the technical, you know, not actually like in the weeds. That's like a separate problem solver that can just ground hole into something. And then I think the third or fourth one was like. More structure process. It's something more like architecture firm like, I think I'm good in those. I think I have operated successfully in those and it's the fourth one that's a little bit the architect architects. I'm like, Oh, that one's a little bit a little bit dicey because the minute

Speaker6: [00:22:54] The danger of archetypes is that you begin to try to fit yourself into an archetype rather than figuring out what hybrid you are. Because no one's pure one. You know, there's no pure this type of leader that I've ever met, at least. But I have met a whole lot of people who have made the mistake of trying to fit themselves into a bucket. And in many cases, you marginalize strengths and try to overemphasize weaknesses. And so the archetypes dangerous. I like letting, like letting your sources of authority speak for you because that allows you to really discover who you can lead, who will listen and exploring new sources of authority doesn't necessarily put you into a bucket. Those sources of authority are just wonderful to apply across scenarios. You know, they're really helpful and can. Happy birthday. Thank you. And to Ken and then to

Harpreet: [00:23:53] Both celebrating birthdays this week, then it's actually your birthday today. Well, happy birthday, man. Thank you very for [00:24:00] spending. That's that's freaking awesome. I know cancers earlier this week. I just wonder how you guys figure each other's birthdays out. I need to. I need to get this on the news on Twitter.

Speaker6: [00:24:09] When I when I lift it out like balloons popped up all over the place.

Harpreet: [00:24:14] Any other insight or advice for from Akiko here can or anyone?

Speaker5: [00:24:22] Yeah, I've got a comment just to elaborate on something that I posted in the chat there. Yeah. So one of the big things I would say

Speaker2: [00:24:30] Is

Speaker5: [00:24:31] That the change shift in thinking from going to a junior to a senior position is widening the field of observation and appreciation. So if you're a junior on your own and you're looking at a single task, you have the luxury of being able to concentrate on that and not have a lot of noise outside of that when you're in management.

Speaker2: [00:24:52] You don't have

Speaker5: [00:24:53] That because you've got to concentrate on the management as well as something else or to the exclusion of everything else. So try to widen that field of observation and appreciation. And I think there are some general generic skills for management that aren't widely appreciated by a lot of people. So I think you need to be able to coach the the staff beneath you to get the best from them. But that's not the biggest task, and I think that's what most people concentrate on. For me, the biggest element of management is facilitation by enabling everyone who works for you to get the best from themselves and work with their colleagues to get the best from the team.

Speaker2: [00:25:40] So really, management, in my opinion,

Speaker5: [00:25:42] At least, is more of our facilitation than it is about coaching. Coaching certainly is there, but that tends to be the headline that I think few then forget about the facilitation part about. So just as as a generic management observation, [00:26:00] I would say that. And then when you're talking about very strictly specific take leadership or other elements, yes, there's going to be bespoke things that you need to do in those in those situations. So, yeah, you know, I won't talk about all of those because there's probably 100 different things that you need to do. And I think Ben has summed up very, very well. The more specific stuff, I just wanted to sound out some of those generic elements.

Speaker6: [00:26:25] Yeah, I do have a quick comment. Harp, and I

Speaker2: [00:26:29] Don't think I'm one that can

Speaker6: [00:26:31] Really talk

Speaker2: [00:26:31] About advancing

Speaker6: [00:26:33] And tech leadership. I've kind of tried to avoid that whole equation together, but you did mention, you know, should you be indexing on your strengths versus what you currently do, which is focusing on areas of weakness and trying to improve those? And the way I've always viewed that, viewed that, at least

Speaker2: [00:26:53] Personally, is I try to

Speaker6: [00:26:55] Understand where my feelings are rather than like where I currently stand. So if there's an area that I'm weaker at. But I think the ceiling is really high. Like, there's limitless potential associated with that domain. I don't mind spending a lot of time to improve that skill set because I no longer term that can take me really far. Whereas if there's an area that I'm good at, if the ceiling is lower than the ceiling of this other area that I'm that I'm worse at currently, like the longer term play, actually, it's better for me in theory to pursue the thing that I'm worse at because,

Speaker2: [00:27:30] You know, there's that

Speaker6: [00:27:32] Again, like that longer term benefit associated with where you could go. So if what you're working on now, you're like, Oh, these skill sets

Speaker2: [00:27:39] Together, although I'm

Speaker6: [00:27:40] Weaker longer term, this is really

Speaker2: [00:27:42] Going to pay off

Speaker6: [00:27:43] For me. Like, absolutely focus on those domains. I think there's probably some hybrid

Speaker2: [00:27:48] Where you can combine

Speaker6: [00:27:50] Some skills that you're good at now

Speaker2: [00:27:53] With

Speaker6: [00:27:54] Skills that you're not good at currently, but have tremendous upside in the future. And that's like the sweet spot. [00:28:00] So I don't currently know how you're like, what framework you're using to think about it now, but that's at least how I evaluate these things in my own life, and I would hope that it's been, you know, at least relatively effective for me.

Speaker4: [00:28:19] Yeah, and that's that's good to hear, because I think. Yeah, I mean, that's good to hear, and I think I I am doing that. It's a little bit painful because it's like it's like I could work on this stuff, right? Like people ask for help and I'm like, I couldn't do this because I am good at it, but it takes away time from the things that are weaknesses I need to be working on. And so there's always that sort of, I think FOMO

Speaker2: [00:28:45] Like I could be doing this like

Speaker4: [00:28:48] This is really good or like, you know, stuff outside, like side hustle stuff like that. I'm like, Oh, I could be doing this. But then it's like, Oh, it takes away time from this thing that is like harder and it's special, beneficial long term. And I'm probably just procrastinating on it because it's painful like now. So, yeah, so but that's that's all good to hear because hopefully, yeah, I have a plan for the series. I'm trying to execute on it. So hopefully that will get some place in two years. So, yeah.

Harpreet: [00:29:21] I'm looking forward to the sneakers. Great question mark, great great tips and advice, definitely give me a lot to think about as well. We actually got a follow up question from

Speaker2: [00:29:29] Kdka on this

Harpreet: [00:29:30] Topic. It's it's not the original question. Ahead, we'll get to that question after this, but it's a great kind of Segway follow up question. Khadijah, if you'd like to meet yourself and ask, if not, I could, I could read out the question.

Speaker3: [00:29:50] Sure, how you doing? Hey, how's it going? So I'm currently doing an internship and I am going from [00:30:00] just creating the dashboard and just handing it off to managers to now creating that dashboard and also now, you know, making meetings and presenting them to other senior managers and other managers and also coordinating a lot of tasks involved. So I'm just wondering if you all have any advice on being comfortable going from just like analyzing and kind of just just just all technical to now being kind of like a project manager as well?

Harpreet: [00:30:37] A that you end up going to respond to this.

Speaker7: [00:30:42] I was listening on this bit more around exploring this exploitation, so I don't think it directly answers what you're saying.

Harpreet: [00:30:50] We will circle back to you. Russell, any tips here for for Kadiza?

Speaker5: [00:31:00] Let me think on that if if anyone else has anything springs to mind circled back to me. Yeah.

Harpreet: [00:31:08] Eric, go for it.

Speaker3: [00:31:10] Ok. Yeah, so that's I mean, that's a cool spot to be in, I think because you're getting beyond just making something and then but actually then being able to share it, which is cool because then you get to share it in your own voice and your interpretation of it. For me, like the you know, I'm sure there are lots of different things that you can do, but the first thing that comes to mind for me is just. It's like finding that balance between staying humble because you aren't necessarily the smartest

Speaker2: [00:31:40] Person in the room, but also

Speaker3: [00:31:42] Taking pride in what you've done because with respect to the thing you're doing like, you're the dashboard that you've made, you may actually be the smartest person in the room because not everybody necessarily understands all the different components and things like that that maybe you had to put together the nuances of the Data and things like that. [00:32:00] And so I would just say, you know, stay humble knowing that you have a small place in a big machine, but also take pride in what you've done because you, you, you know, it probably better than many people do.

Harpreet: [00:32:15] Mexico, go for it.

Speaker4: [00:32:18] Yeah, I think the advice that I was given recently for my manager, I really like is she's like when you're doing projects or initiatives or whatever. Create like a poster board sort of thing and it's just like a little cheat sheet or like the index card of like your main points below pointed that is the like. What is the best problem that you're trying to solve? How are your how are you solving it? Three. Supporting sort of analyzes or reports or what have you to support that solution and then list the assumptions too. So if you can kind of do it there because I have it, I used to have was that I used to just give like too little detail. This is like early early on my career and then my manager at that time was just like, Oh, you just need to write more. But she never really gave me insight into what was this more? And the more is the like making sure that you're answering the right questions, like answering your questions. While still kind of listing the sort of assumptions that you're making in your sort of answer and then leaving room open for feedback because I think when you see this and frankly, a lot of shows and movies, and I really kind of hate it like the person is super confident they present this killer presentation and everyone's just flawed and they have no questions. And that has literally never happened in my life, because even then, when they're really excited by the presentation, they're questions. There will be questions around like, Oh, how can we ask, can we implement it? Oh, can we tweak this x y z? Oh, [00:34:00] can we add this other feature that will create this new like 20 hour pipeline lift or something like that? So even when you're doing well in the presentation, it's killer. People always have questions and that's like a really good thing, or they'll offer feedback that will then change the analysis. The one thing that I always really enjoyed about working with my business partners was, in many ways, I don't think they were very simple. That's not really quite nice,

Speaker2: [00:34:27] But they're

Speaker4: [00:34:28] What they want out of it. What they want out of it is usually pretty straight forward. So that's like the nice thing is you can start with just kind of like the bullet points of it over the presentation, make sure that there's kind of room for discussion, even, for example, creating a slack thread so that if people have questions kind of real time, they can post to it. And then it doesn't disrupt the flow of the presentation. And like things like that, also, the second part is there is no such thing as too much preparation. Once again, a lot of noise on LinkedIn about how I got crush this presentation after only an hour of prep. Yeah, that also just never happens unless you're like the domain expert semi, the subject matter expert of your area and you have lived and breathed that project. Then people can maybe kind of crush it. But that's usually because they're just doing like a status update on the project. So how much time you feel like you need for your presentation to prepare for it? Definitely take it. Block it out for yourself. Deal the bullet points and also Minto Pyramid. Very cool thing to look up. It's a great framework for presenting. I think they live and breathe it at McKinsey or something. It's either BCG or McKinsey. And that's because they're costly, giving presentations to really smart, difficult people.

Speaker2: [00:35:53] Well, maybe even

Speaker4: [00:35:54] Also knows a little bit of that too, right? Yeah, he's I could say it. He's not considered cool. But [00:36:00] yeah.

Speaker2: [00:36:03] Yeah, I found like time wise for presentation, I've

Harpreet: [00:36:06] Noticed, at least for

Speaker2: [00:36:07] Me, like when I

Harpreet: [00:36:08] First create the presentation, it's usually about 30 minutes to forty five minutes of Prep for every one minute of

Speaker2: [00:36:17] Presentation. Like, it's not

Harpreet: [00:36:19] Easy making presentations like it's a it's a job and it's being the ass Russell and then Ken and then Kadish also had like another follow up question, which I think fits in nicely here. Then, after a judicious question, we will go to. There's a question coming in from LinkedIn from Paul. Great question. And then we'll get to Jennifer Nadine's question. Russell, than Ken.

Speaker5: [00:36:43] Thank you. Yeah, and I just like to second both Eric and Mickey Go's. Those are great points. One additional thing I'd say is know your audience and optimize the presentation for your audience and optimize the tools you will present for your audience. I've been in several situations where I've worked on some great stuff with a great team, and we've turned around something that kind of blows our minds, but the audience doesn't get it. It's kind of it's beyond their Data literacy point. At that stage, it may be something that you can save them to in six months, but don't throw it at them straight away. You need to build them up to it, so optimize it for the audience. Don't push them too hard. And even if you think it's the best thing, you know, you've got to swallow that pride element and understand that regardless of what you know to be the case, it's to serve the purposes of the audience. That's a primary objective of the work that you're

Speaker2: [00:37:40] Doing, so

Speaker5: [00:37:41] Optimize it for them and then create a plan to get them from that low point to the point that you already have in mind in a period of time, say, two, three months, six months, whatever it is, depending on the scope that you put in hand. But it's got to be optimized for the audience. That's the biggest thing for any presentation.

Harpreet: [00:38:02] Thank [00:38:00] you very much. Let's go to Ken, then then

Speaker2: [00:38:05] Eric, and then

Harpreet: [00:38:06] Coast up, we'll hear from you on this and just a preview, some of the questions that are coming up. Kadish wanted to know what you know.

Speaker2: [00:38:13] How far can you

Harpreet: [00:38:13] Advanced in analytics if you don't leave people in the organization? Paul has a question about why the hell we have to go through these crazy ass coding interviews for machine learning, engineering and, you know, that kind of stuff. And then Jennifer has a question on. I just wrote it down, but I lost it

Speaker2: [00:38:30] About

Harpreet: [00:38:31] Business analytics and things of that nature. So a lot of cool questions coming up. Make sure you guys stick around. Ken, then Eric, then coast up here. Your advice for if she were, I don't even know she's still

Speaker2: [00:38:43] Here or not.

Harpreet: [00:38:45] Go for it, Ken.

Speaker6: [00:38:46] Yeah, this will be really quick. So this is just kind of follow up following up on what Nikki said about the pyramid principle. I was actually a management consultant for quite a bit before I moved into data science, and I think it's interesting to look at how a consultant is trained to give information or give a presentation.

Speaker2: [00:39:07] You always start with the bottom line up front.

Speaker6: [00:39:09] It's a concept of a bluff, right? So you you tell the story backwards when you're presenting it, and I don't think that's necessarily a logical

Speaker2: [00:39:17] Or normal

Speaker6: [00:39:18] Way that you would present a technical concept. So the idea here is that like you have to speak their language. And often that's why starting with the findings and also like giving them a reason

Speaker2: [00:39:30] Why the

Speaker6: [00:39:32] Like proximate steps are relevant for what you're showing

Speaker2: [00:39:36] Them.

Speaker6: [00:39:36] So my thing would be like, Hey, start with the findings. Make sure they they know what they're getting and why it's important and why it's relevant to them. And then you have to explain the caveats and the and like, you know, the assumptions and a lot of these things that could make it not work as planned, you know, according to your your research and your analysis. So there's this like weird conflict

Speaker2: [00:39:57] Between the way

Speaker6: [00:39:58] Business users and business stakeholders [00:40:00] tell stories and how technical people tell stories. I haven't seen it resolved very effectively, but at the very least, you under you are standing. That they're going to listen for certain things in a certain order can help you convey your message a lot more clearly.

Harpreet: [00:40:17] Very much, can, Eric.

Speaker3: [00:40:20] So could you show one of the other things you said was partially presentation, but then also like the project management side and working with different stakeholders? So I wanted to say something quick about that and that is like before you have a big presentation with a group or anything like that, I usually like, I'll grab somebody that I know in product, somebody I know well, somebody I trust to give me feedback to like, say, like, here's what I have. Does this make sense to you? And if it makes sense, or maybe they give feedback, they give me some instructions or something like that, some ideas, then I can incorporate that after talking to one person and then I can go talk to maybe one other person and get a little bit of feedback from them before you know it. I've talked to half the group individually and then when it comes time to present to like the big wig person who you know, has to give that final sign off. I already have half a room who's on my team and they all have already seen it. They're agreed with it and it's cool and we're going to go for it. One of my old managers called it counting your votes before you call for them, and it's really helpful.

Speaker7: [00:41:26] Go, step, go for it. Mate, that's spot on, Eric, like I was going to say one of the things that I used to do, especially when I'm presenting to a larger group of people with a variety of things, is present before you present, right? Like, I would literally make that presentation three or four times, get individual stakeholders who I know that I need to get across the line. And along the way, I'll just find all these problems that they would point out and that one on one, and they're comfortable pointing it out. And I'm much more comfortable, especially when I was like in my first year working and second year working kind of thing, you know, still quite early to the professional world. [00:42:00] I was like, Okay, I'm more comfortable in that one on one situation telling them, Hey, I don't know the answer to that, and then I can find out before the actual presentation itself, right? Or at least I can tailor it to to that need. But the second side of it is

Speaker2: [00:42:13] That when you

Speaker7: [00:42:14] Don't do

Speaker2: [00:42:14] That and this is

Speaker7: [00:42:16] Something that I kind of learned on the fly by making all the mistakes, worst that you could do in that situation is by putting myself in a room with 20 or 30 people that were subject matter experts in their own areas and are only in that room to figure out how my presentation is going to help out solve the problems that they're trying to solve. That's on the top of their list. Let's remember that that's why they're there. They're not there to glorify your findings, for example.

Speaker2: [00:42:42] So what I what I

Speaker7: [00:42:43] Found the trap was was. It's very easy to fall into that situation where I'm presenting, so I know the answers, so when they ask a question that kind of actually detracts from what you're trying to present, they will they will constantly come back to say, OK, how does this solve my problem in operations? How does this solve our problem in the manufacturing floor? If you don't have the answer to that, the thing that I found was being comfortable to say this isn't what that's about, right? Just straight up calling it out and

Speaker2: [00:43:11] Saying, that's not

Speaker7: [00:43:12] Why I'm presenting. We're presenting specifically with this error. They're going to want to do that. But being able to deflect that in a in a professional manner and actually being able to say, you know what? I see the value in what you're trying to apply this to, maybe let's take this chat offline and being able to deflect that and keep the focus on what you're trying to

Speaker2: [00:43:29] Present is really

Speaker7: [00:43:30] Important because it's so easy and it's happened to me at least twice where it's completely derailed and I'm stuck in a point where I'm mentally trying to solve that problem on the fly. But it just all goes to shambles, so that's a trap that it's it's very easy to prepare for by a doing counting your votes, as

Speaker2: [00:43:50] Derek said, and also

Speaker7: [00:43:52] Very easy to account for by just being comfortable saying, I don't know, let's find out, you know? So, yeah, that's

Speaker2: [00:43:59] What I learned from [00:44:00] screwing it

Speaker7: [00:44:00] Up multiple times.

Harpreet: [00:44:03] I love that.

Speaker2: [00:44:04] If I've learned one

Harpreet: [00:44:06] Thing and that's I know a lot less than than than I actually know, it's a good feeling. Mikey, go go for it and then we'll go to Paul's.

Speaker2: [00:44:17] Yeah, we'll go to Paul's

Harpreet: [00:44:18] Question and then circle back to each other question. Michael, go for it.

Speaker4: [00:44:22] Something that to me was very, very cool when I switched over to the engineering world from the data science analytics side of the house was tech specs. Not necessarily that writing a tech spec is really cool, but the idea that these are living, breathing, async, async documents where you can collect feedback and you can specify the like what they call the some people call it the racing matrix, or they call the DC Matrix. Not like DC

Speaker2: [00:44:54] As in supporting

Speaker4: [00:44:56] The motherland right of India. Like not like that, but like like DCI. But racing seems like responsible, accountable, contributing, informed. But the idea of like a textbook being like this living document that encourages feedback encourages input. To me, was just such a cool concept because a lot of times when you I used to be when I was early in my career, I had this habit of like kind of getting feedback from people like right before the presentation or like during or after. And some people kind of, you know, everyone has different learning and processing styles. So this is also another way to be really inclusive to people who maybe just need a little bit extra time is if you have like the what they call the prairie dog. So you kind of have what you want to say and some supporting stuff and you kind of like, send it out beforehand. It could be like how many days or week or whatever. First off, people love being included on those even when they shouldn't be in the kitchen. They love it. And [00:46:00] it also gives some time to, like, really think about it and to like, do all this questions and chat. And then if you even have the chance to address some of those before the presentation that already it builds your credibility in terms of like the relationships and as a this is a partner who cares about what we think, but also it can get some of

Speaker2: [00:46:19] Those people like, you know, squared away before they

Speaker4: [00:46:23] Derail your presentation, even the people who don't mean to. Sometimes there's there's always those one or two people. So, you know, that's something that to me was like, very, very powerful. And it's really kind of cool. And I think, yeah, I feel like I'm mostly seeing injuring side. I feel like I haven't seen it a lot on the day since machine design, saying I like side and I wish more people kind of did that.

Harpreet: [00:46:44] Great tips. That's something I actually do, because I present a lot for work like mostly at conferences and webinars and things like

Speaker2: [00:46:51] That,

Harpreet: [00:46:52] But I'll have because, you know, I'm doing presentations that are technical, that are somewhat marketing, they're somewhat advocacy. So having a little one off, just like run throughs of the presentation or just overviews and getting people's feedback, that's been very helpful. So I love that tip. Thank you very much. Makiko, let's go to let's go to Paul's question. He's got a great question question that I'd love to know the answer to as well. Go for it.

Speaker3: [00:47:20] Hello. Yeah, I'm really happy to be here, it's cool to see you guys. And can I been like listening to your videos for so long? So this is cool. Yeah, I was wondering why weekly code questions are the barrier to entry for machine learning rules. I'm looking for a junior position or an internship, and I've been sent assessments which seem like code problems. And then when I've built projects in class or during my current internships, I haven't really had to apply to something like sort of a linked list. So I'm just wondering why [00:48:00] we're asked these questions like kind of as a way to assess our machine learning skills and how to get better at answering these questions because I feel

Speaker2: [00:48:10] Like they just don't come

Speaker3: [00:48:12] Naturally to me. So, yeah, that's my question.

Harpreet: [00:48:17] It definitely relate to you on that. Trust me, and I would not even just entry level, it's all levels you got to go through this. Let's hear from Mexico and then Canada doesn't have his hand up and I'm bringing him into this kind of live to hear from him as well. Mexico go for it

Speaker4: [00:48:38] Because people are bad at hiring and they're lazy and cheap. Yeah, no. Ok, so it depends, right? Ultimately, it comes down to, OK, so for example, big phone companies, they get like hundreds of applications, much like college with the first year. Is these really ridiculous? Like, why would you throw C++ at an incoming college student in their first year? I mean, there are just easier languages, like if you're if you really care about the STEM pipeline, why would you do that? Right. And it's just to weed out people because they get so many feel like go through the program, right? Because if they actually make computer science and programing fun, then that would be a lot more people that are engineers. So it's similar with like big tech companies because and but the nice thing is that all their interviews are very standardized. You know, when you go interview for Apple, Google, Facebook, Twitter, you know exactly what you're getting. I mean, you could even look up the data structure and algorithms questions only code. And you know what you have like almost a 10 percent, 20 percent chance and picking the right question, which if you spend enough time studying for it, that's actually pretty good. Like just. And some people have done that. They've literally just

Speaker2: [00:49:58] Worked through

Speaker4: [00:49:59] Cracking the [00:50:00] coding interview and they will just copy paste. So the answer, right? So big tech companies, they can kind of afford to do that, though, because like, who wouldn't want to work at a company where like, you're getting what, one hundred and twenty to 250 K in base with equity? So they do that intentionally. Other companies, I don't think they do that intentionally. I don't think they do it with that. We want to kill as many people, all of our pipeline as possible. It's I think they do it because they honestly don't know a better way to hire and or they're not willing to make the investment because it depends if you're going in as a data scientist or a machine learning engineer or a data engineer or whatever. If you're an engineer and you're working on fundamentals sort of application layers, you do kind of need. You actually do need data structures and algorithms knowledge to a certain degree. Like if you're a Google engineer that is working on like the next iteration to spanner your data structure and algorithms. It probably should be really good saying if you're working in a Ph.D. in robotics, a lot of times you're writing stuff that's very close to firmware, so it does need to be that level. But for a lot of companies and roles, it actually doesn't. And so the reality, I think, is that for a lot of times, it's because they don't know how to hire an interview.

Speaker4: [00:51:22] You know, it's unfortunate. And I've been there like a science to an engineer, to like platform engineer, like it's it's a pain. Now the brilliant part is when you get more senior, a lot of times they won't even put you through that. Like literally once you get past senior level right now, like they will not. So it's a pain point that juniors feel. Once you get to the senior level, you have work experience and maybe personal projects. And at that point, they usually just care about like how you think about problems at a system level. So that's like the secret guys. It's they don't do this to everyone. They don't. And a lot of companies don't. When I was getting [00:52:00] my first email engineering job. I tried doing the ones that had destruction algorithms, interviews, and then at that point, I just said no. And it end up being like 70 percent of companies I interviewed for. They did not do a Data structures, not everyone's interview, because I had some work experience at startups. So that's that's the other secret is not every company does it. You can kind of find the ones who don't, but it gets easier as you get more senior.

Harpreet: [00:52:27] Can go for it, then after can win and then cost up.

Speaker6: [00:52:33] So I agree with what she was saying about why we have these.

Speaker2: [00:52:37] I think that,

Speaker6: [00:52:38] You know, for

Speaker2: [00:52:39] For worse, for the

Speaker6: [00:52:41] Most part, there's

Speaker2: [00:52:42] Just a lot of companies

Speaker6: [00:52:44] Don't don't think that there's a better way to evaluate the talent and a lot of the companies that aren't at the at the top, they just see what Google and Amazon and Metta are doing and they

Speaker2: [00:52:55] Just follow suit. Right?

Speaker6: [00:52:57] Which does not necessarily make a good ecosystem. On the other hand, it does show that you can sit there and learn a skill and dedicate a lot of time and effort to practicing it, which isn't necessarily the worst thing to signal, right? If you can get through those things, they say, Wow, this

Speaker2: [00:53:12] Person can clearly

Speaker6: [00:53:14] Dedicate X amount of hours to picking up the skill and to learning this infrastructure. And maybe they would do that same thing when they started at our company. What I can help with, hopefully, is how to get a lot better at these, and there's two steps. So one is just reps to like leap code others. Algo expert. If you're looking at data science specifically, there's interview query and there a lot of times just taking real questions from these companies and just cycling them back through to do something that I would always do before an interview and

Speaker2: [00:53:47] Not necessarily

Speaker6: [00:53:48] Sure how, like super ethical it is, but I would always just go on Glassdoor and look at what they asked other candidates. I mean, it's out there, it's public. It's it's it's information that and I

Speaker2: [00:53:58] At least

Speaker6: [00:54:00] Four [00:54:00] or five interviews got the exact same questions that people were posting on Glassdoor, like verbatim the same thing. And so you can go in and they're out there. If you're just looking for like, hey, a list of Facebook interview questions, a list of XYZ interview questions.

Speaker2: [00:54:15] So at larger

Speaker6: [00:54:16] Companies, they

Speaker2: [00:54:17] Cycle through pretty similar

Speaker6: [00:54:18] Questions. Most of the time, it's smaller companies. They often don't really change the interview questions that they have very frequently. So in both cases, you're probably going to be able to see similar questions to what you're actually going to be getting. So again, my advice would be practice and just do as much homework as you

Speaker2: [00:54:36] Can ask your friends

Speaker6: [00:54:37] If they went through the interview process, whether. It'll be probably a pretty, pretty effective way to get ahead there.

Harpreet: [00:54:49] And thank you so much. Also, just shout out to Austin Loveless in the building, good to see you again, man. It's been quite some time. I'm curious, like, you know, to to the other half of of. Paul's question is how do you prepare for these questions? How do you get better? Like, there's got to be a better way than just brute force memorizing these solutions, right? Because what? Who does that serve like? Is there an overarching

Speaker2: [00:55:12] Kind of way to?

Harpreet: [00:55:16] I guess all these questions or think through them or something like this, you know what I mean, then let's let's hear from you.

Speaker3: [00:55:24] Yeah, I think so. I'm old.

Speaker6: [00:55:27] I don't really know if computer science is still taught the same way as it was when we were first inventing fire, but we were taught like some really basic types of patterns

Speaker2: [00:55:38] And you had

Speaker6: [00:55:39] Patterns, practices, fundamentals of architecture and design. And most of these leaked code things, that's what they're trying to test. But the people that built them aren't really entirely sure of what they're going after, and they're trying to be nice in some ways where it's like they want to test this really complicated thing, [00:56:00] but they want to also not make a really impossible test. What they end up with is something that

Speaker2: [00:56:05] Doesn't test anything

Speaker6: [00:56:06] And is

Speaker2: [00:56:07] Already more complicated than

Speaker6: [00:56:08] It needs to be. And so you have to look at a lot of these and realize the intent

Speaker2: [00:56:13] Was to

Speaker6: [00:56:14] Test your foundational knowledge of software engineering at some point, you know, you may be testing architectural concepts

Speaker2: [00:56:24] Very high level, but they're also trying

Speaker6: [00:56:25] To do that in what should take, you know, between two to three weeks to test. And they're going to give you this thing in eight hours that you're somehow going to cram just it. It's a construct that has decent intentions, especially for companies that are solving really complex problems. You make a mistake, then you get away with it four times, put the fifth time you know it crashes. And that's that is literally what goes on is, you know, the hashtag AIs down trends because, you know, you made one small mistake. And that's that's what they're testing for is to make sure that that mistake happens as few times as possible, that you have the foundational knowledge necessary to do really complex work. But no one ever gives the intern that work anyway. I've no idea why we test Junior. You're like, Why would you do you give them a mission critical check in and no one reviews it. Really, no one's testing this stuff. Come on. So it's kind of unrealistic. But the point is, and if you want a mechanism to study, it really is that you've got to go through the foundational knowledge of software engineering, not coding, but unfortunately it gets convoluted. And so if you understand those foundational concepts, the patterns, practices that

Speaker2: [00:57:49] You know, then it gets

Speaker6: [00:57:50] Easier. But because they're all variations on a theme, really, maybe like 20

Speaker2: [00:57:55] Themes, but

Speaker6: [00:57:57] Past that, the implementation like the thing that [00:58:00] you're actually going to have to code, that's where stuff gets convoluted.

Speaker2: [00:58:05] And, you know,

Speaker6: [00:58:07] Everyone could potentially fail any given one of these at any given time. And I think that's what a lot

Speaker2: [00:58:15] Of us call out on a regular

Speaker6: [00:58:16] Basis is any given Monday. You give me any one of these and I'll pass three out of five, you know, and that's the truth of it, is that the implementation that's given to you, maybe you forgot that's a legitimate reason why

Speaker2: [00:58:34] And exactly what Kiko

Speaker6: [00:58:36] Said. You get to a certain point and people are afraid to interview you. Because they're scared, you know, more, and you're going to make them look stupid in the interview. And so there is this fear in interviews that you know someone smarter than you is going to show up in the interview. And this is also a fear that's pervasive when you're interviewing very educated junior level or entry level, especially if you have some, you know, one or two impressive projects behind you. You can actually intimidate your interviewers. And so be wary that in a lot of cases, if you see one of these at a less tech, first less advanced company,

Speaker2: [00:59:17] They may be using

Speaker6: [00:59:18] This to kind of save themselves a little bit. And it allows them to ask fewer technical questions and potentially reveal some of their weaknesses as a team. So there are a number of different sort of inside games that are being played. And so I would just call those out and do the best you can with with understanding the foundational concepts and hope for the best implementations.

Speaker2: [00:59:42] And when you talk about like like

Harpreet: [00:59:44] Patterns and things like that, it's

Speaker2: [00:59:45] That stuff like sliding

Harpreet: [00:59:46] Windows

Speaker2: [00:59:47] Pointers, merge

Harpreet: [00:59:50] Sorts link list. Sure. Trees, it could be.

Speaker6: [00:59:54] You know, that's the thing is there's foundational concepts of software engineering, which

Speaker2: [00:59:59] Are basically [01:00:00]

Speaker6: [01:00:00] Your best practices so you don't build

Speaker2: [01:00:01] Garbage. And that can be

Speaker6: [01:00:03] Everything as granular as comment your code, for God's sake. Please comment your code, especially if you're a data scientist. Sorry, I went through something there.

Speaker2: [01:00:11] It can be

Speaker6: [01:00:11] As something as basic as right. I think that's a pattern. Come into your code, make it make sense. And then, you know, variable naming is so it can be really granular stuff that's being asked of you and you can have a solution that's totally wrong. But if you comment it and you do some best practices more times than not, a team is going to look at that and just go, You know what? I can teach the rest, like I can teach the larger architectural pattern that you completely missed, but I can read your code and you're the first person who submitted an answer. But I would actually enjoy reading. And so don't don't overestimate the really granular patterns and realize there are, like I said, there's about 20 variations of these leat coding types. You know, 20 themes that get very get variations on. So really try to find the patterns behind it. If I walk through all of them,

Speaker2: [01:01:02] It would be

Speaker6: [01:01:03] An hour and a half. So just say try to figure out the underlying patterns, figure out the most common architectural patterns in software engineering patterns, get those committed to memory and then figure out implementations from there.

Harpreet: [01:01:17] And thank you, Coast, to go for it.

Speaker7: [01:01:21] Yeah, this is this is a bit of a pet peeve area in general for me because I think we've got it grossly wrong. Then I followed a lot of what you said in this area. I mean, most of us have seen most of that. It's one of those things where I think at this point in my career, I'm kind of resigned to the fact that, yeah, people are going to ask me to do that, but I'm going to care about it just as much as they care about it. You know what I mean? Like, I have in the past literally dropped an interview process, voluntarily saying, I'm not interested in any stage out of fear that I'm not going to do well enough in that test. And then, like now, I look back at that and see the big flaw in that thinking [01:02:00] is I was at that time wondering, Oh, I've got to impress people. I'm not going to impress. I'm going to make a bad name for myself for this. But if they're literally just using it as a filter and they're going to spend 10 seconds checking the final score on what you got. Don't worry yourself so much about trying to impress them because it's just going to waste your energy. Now, if that's the barrier to getting you the jobs that you want, go ahead and put those hours into

Speaker2: [01:02:25] Just getting well enough to get

Speaker7: [01:02:27] Through that barrier, right? It's like getting your your résumé writing skills up to a point where people are actually going to bother to

Speaker2: [01:02:34] Read it right?

Speaker7: [01:02:35] It's like put in as much

Speaker2: [01:02:37] Effort as they're going to put

Speaker7: [01:02:38] In in return right now. If they're going to take you through like a project and actually give you proper feedback, then yeah, you can learn a lot from that. So I do see interviews as being this two way street, right? But they're going to give me a project. I'm going to expect a proper review on that code and actually sit through and walk through it because

Speaker2: [01:02:55] That gives both sides something one. It lets them

Speaker7: [01:02:58] See the

Speaker2: [01:02:59] Intent behind what I'm

Speaker7: [01:03:00] Coding and to. I get to actually see that, Hey, this is a team that's going to bother to review my code. That's going to bother to, you know, understand what I'm trying to get to if they're not going to bother doing that for me, are they going to do that when I'm working for them, you know? Is that do they have the expertize to review my code? These are the questions that I answer to by actually doing these coding tests as much as I hate doing them. That's the value that I seek for it. So if they're going to fire

Speaker2: [01:03:25] Off like a fire and forget,

Speaker7: [01:03:28] You know, code quiz at me, then I'm going to fire and forget to smash it out in an hour. Do my best if I fail. Whatever. Go on to the next job interview.

Speaker2: [01:03:36] Let's be honest, it's a

Speaker7: [01:03:37] Bit of a bias. Sorry, a seller's market in a sense where the sellers of skills. There's dozens of interviews out there. Don't stress this off and take each

Speaker2: [01:03:46] One is practice.

Speaker7: [01:03:47] Don't worry about all I need to have done 100 practice quizzes before I go and attempt this one. Consider that a practice quiz and just keep going like the end of the day. It's like passing your high school math exams, right? You just got to do hundreds of them. And [01:04:00] yeah, sure, you'll fail a couple along the way.

Speaker2: [01:04:03] The bigger problem

Speaker7: [01:04:04] Here is how do we actually come up with a way of understanding a person's technical expertize and depth of technical expertize in a very diverse area that often we're not familiar with? And that's not really a problem that an interviewee

Speaker2: [01:04:21] Solves, right?

Speaker7: [01:04:22] That's something that we as an industry need to really rethink.

Speaker2: [01:04:26] There isn't going to be a one

Speaker7: [01:04:28] Size fits all for any company in this measure, so you're going to see a variety of that yourself in practice questions for a bit. But at the end of the day, don't beat yourself up about it because frankly, like Vince said,

Speaker2: [01:04:39] I can't guarantee

Speaker7: [01:04:40] That I would remember a specific line of code in the pandas toolkit when I need to. We're going to Google that stuff all day.

Speaker2: [01:04:46] So even if we do not know it,

Harpreet: [01:04:50] We'll stack overflow data scientist. That's what I am. Kiko, go go for it.

Speaker4: [01:04:56] Something that like was mind changing was this was the last year or something like that when I was like struggling, I literally failed every single technical screen interview because I would just get so nervous and I'd stop thinking. And so at that point, I was taking the full stack deep learning workshop, and one of the teaser I remember I posted, I was like, It's just been terrible. And like, one of the teachers was like, Let me just work on your resume and let me let me hear what you're doing with your process. And he was like the first one to say like, Oh, by the way, you can look at your level and granted like, you know, I have five or six years now of working in various roles in tech, so I'm not a super junior candidate, right? So just to copy out that right? He's like, Yeah, you don't even need to do any of those are like, Wait, what? I have a choice. I can say no. And he's like, Yeah, he's like a certain level. You don't have to. He's like, if you want to do the tech, you know the big thing companies, right? Like, you can't really negotiate on some of those. But he's like, as long as that is [01:06:00] not your goal. He's like, Yeah, you can just say no to all of them. And you can even ask the interviewer, like,

Speaker2: [01:06:06] Hey, like, you know, based off my

Speaker4: [01:06:07] Experience and your needs, can you fast track me because guess play like I got? I got other recruiters to talk to you. I got other

Speaker2: [01:06:16] Opportunities. And there's one

Speaker4: [01:06:18] Girl on Twitter, I think, who did something really cool. So she she has she has just this generic vanilla copy paste message where she's like. So if you're a recruiter, I will not be doing any tech screens. It unfairly disadvantages women, people of color, LGBTQ. If you have if you have anxiety, if you are a career changer and you haven't had a chance to get a formal education. She's like, and I fit all these boxes. So there's a lot of reasons for why I will not be doing these sexual screens. So you can just skip right past me and I'm like, Yeah, that's baller. I didn't do that. But yeah, like like a certain point. When you build up the leverage you, you have choices and you can make some of those decisions to either like voice or AIs. It's just a matter of making sure you stay in the game long enough and you build up that cachet in that leverage to eventually be able to do that.

Harpreet: [01:07:17] Akiko, thank you very much, Paul. A lot of great advice there. Hopefully, you enjoy that. I might just cut that up and make that into a blog post sometime soon. Great, great tips. You can expect that everybody get tagged on that one. Let's go to Jennifer's question that after Jennifer's question will go to Nicole's question. Jennifer, yes, yeah, there you go.

Speaker3: [01:07:40] I am. Yes. So I'm

Speaker4: [01:07:42] Working with my

Speaker2: [01:07:43] University to add

Speaker4: [01:07:45] A business analytics emphasis to their MBA program. So think about those that are not engineers, they're not technical, but need to use Data at the end of the pipeline for strategic decisions. [01:08:00] What do you think they need to know coming out of their MBA program? Hmm.

Harpreet: [01:08:09] Then I'll toss this

Speaker2: [01:08:11] With you, or I

Harpreet: [01:08:12] Can can then win.

Speaker6: [01:08:17] Yeah, so I've thought a lot about this, I mean, I came out of a business program and was trying to understand what Data was and how to use the tools and whatever it is. And, you know, initially like going into the program, I didn't even have an idea about what school was. And I think just being able to access data that

Speaker2: [01:08:35] Other

Speaker6: [01:08:36] Other people are using the engineers are using is a really powerful skill. I also think skill is not something that's overwhelmingly difficult to learn for someone with a business background. Right? You're used to

Speaker2: [01:08:47] Seeing

Speaker6: [01:08:49] Like excel tables. You know, SQL and theory is just a bunch of like a different excel tables that are linked together in an organized way.

Speaker2: [01:08:57] So I think that that

Speaker6: [01:08:59] Is a really fundamental thing that a lot of business people are going to be seeing at work and they'd want to be able to interface with and use. The next thing I would generally recommend is some sort of be a tool. I think a lot of the business people who I've worked with in the past, they really wish that or they get really excited when they see Power BI, when they see Tableau and they can start working with it. That to them is like, Wow, I wish I had learned this earlier because this is something I can differentiate myself with in my work. This is something that gives me the power to create insights or generate a lot of these findings. On top of that, though, I do think if you're doing descriptive statistics, if you're looking at central tendency, you probably should have at least one additional sort of statistics, fundamentals or a foundation course. So you're not making assumptions that aren't realistic for the data that you're using. So I think those are

Speaker2: [01:09:55] Sort of the three things

Speaker6: [01:09:56] That I think are relevant.

Speaker2: [01:09:57] So the ability

Speaker6: [01:09:58] To access data with [01:10:00] sequel, the ability to understand like the implications of the data with statistics and the ability to show that through some sort of powerful visualization tool. I think that those are, in my mind, the foundations.

Harpreet: [01:10:14] Go to a coast

Speaker2: [01:10:15] Up, then Eric, and then I

Harpreet: [01:10:17] Keep forgetting to go to, then we'll go to him as well, coast, then Eric and then also shout out Greg Coquille is in the building. Good to see you, man. Happy New Year, because we'll go for it.

Speaker7: [01:10:28] But one of the things that I see universities have a tendency to do because it's it's easy is take existing courses

Speaker2: [01:10:36] From or as existing

Speaker7: [01:10:38] Subjects from other courses and streams, particularly from like undergrad streams and chocolate into

Speaker2: [01:10:43] Like a

Speaker7: [01:10:44] Postgraduate course. And whether that's like an MBA kind of

Speaker2: [01:10:47] Course, or whether that's like a

Speaker7: [01:10:49] Masters to something, I see that a lot. And my problem with that is that it might be a total waste of time for them to do a full undergrad, you know, intro to statistics course. A lot of that might not be applicable to, you know, people going for an MBA. But I think being able to put together a specific course that will take

Speaker2: [01:11:10] Them through the statistics

Speaker7: [01:11:12] That they're going to be exposed to. Now as a business leader, you're exposed to, you know, you're going to be thrown a confusion matrix now and then you're going to be thrown, you know, some kind of statistical visualization. Is there a way that you can expose them to those kinds of situations where they need to understand

Speaker2: [01:11:29] That because plenty of

Speaker7: [01:11:30] Times on the technical side, I had to present to business leaders who don't really understand the statistics of a confusion matrix and having to explain that from scratch. Often that's a big hurdle in the first place, you know? So getting them to understand metrics of what they're going to see coming out of the Data might be just as important as getting them to understand the processes applied to the Data itself. In fact, I'd argue that if they can walk out of there understanding

Speaker2: [01:11:57] The metrics, then they can

Speaker7: [01:11:59] Glue that conversation [01:12:00] with the data science team that they're going to be working with. They're going to be able to glue that, you know, business level understanding to what the statistical technological understanding is. So, yeah, maybe avoiding that pitfall of like, Oh, we've got a statistics course over here. They need to know statistics, chart that in and tailoring something that maybe you've got a data science master's stream that's already running. Maybe those people want some experience in presenting their findings to a particular business use case. So maybe just setting up workshops like that that gives the Data science master's students the ability to be exposed to, Oh, we've got to present these findings. And likewise getting, you know, the MBA students to be able to have that exposure of having findings presented to them. That's a very real world way of learning it. And I don't know whether that fits into the

Speaker2: [01:12:50] Approach of here's a subject that

Speaker7: [01:12:52] We do at university, but it might fit in like an MBA, kind of course.

Harpreet: [01:12:57] Also, thanks so much, Eric, go for it.

Speaker3: [01:13:01] So I so my partner is actually doing her MBA right now, finishing up here soonish and has a data analytics component to it. And so this has been on our minds for a couple of years now.

Speaker2: [01:13:15] And so, you know, I was going

Speaker3: [01:13:16] To basically echo what Ken was saying about like SQL is huge because like some of the people I work with, you know, it would it would make a huge difference in their life if they could get to the data just and it's not even that hard for them to get to what they need. So having some SQL knowledge like a bi platform and then the other piece like this is I know it's a delicate balance because, you know, stats can be an undergrad or it can be a course and you know, you've got to draw the line somewhere. And some of the things that have kind of come to come to mind for me are, you know, understanding AB testing. It doesn't have to be a crazy high level and you don't have to get down into all the TS and Zs and Fatos. And I don't even know what else is, but like just like [01:14:00] understand a b testing because it's a thing and we can all relate to it. Clustering tableau can cluster. And so like if you understand, K means clustering and you have this vague

Speaker2: [01:14:09] Notion of that

Speaker3: [01:14:11] Distance, great, you know, and because market segmentation is an important thing in the context of an MBA student with a marketing emphasis. And then the other piece is, I would say with, you know, talking about something like regression, which is common is like, you know, making sure, I would say, going deep enough into the most common subjects and like understanding what those assumptions are so that when we're having a conversation, me and somebody who has an MBA that when we're talking about it, we're kind of talking on the same, talking about the same things. And we know kind of where the

Speaker2: [01:14:48] Fences are around the things

Speaker3: [01:14:49] That we're talking about. And that's really helpful. And I would say because I think that should be sometimes added. The thing that I would take

Speaker2: [01:14:57] Out is like, OK,

Speaker3: [01:14:59] Forget that piece of the class that where you're going to teach, jump or forget the piece of the class where you're going to teach SAS because oh my gosh, like with your MBA, you are never going to need that if you're doing not fast something. And I would say instead, you can then focus on getting those like a little bit of the little bit of depth in the really important stuff that we talk about most frequently. And then the

Speaker2: [01:15:25] Other thing just piggybacking

Speaker3: [01:15:27] Off the coast

Speaker2: [01:15:27] Upset in my program,

Speaker3: [01:15:29] We had to do like a dashboard presentation to the to the coach of the university tennis team. So he gave us like a bunch of stats that they use for tracking tennis players during their games. And then we were divided up into teams and we had to put all together and we all present it. It was great. It was hard. It was intimidating. We didn't know who the person was that we were going to be presenting to or anything, and it was in an area that I had almost no domain expertize in or anything. So I really liked the idea. It was a good experience [01:16:00] for me to.

Harpreet: [01:16:02] Now, let's go to Greg in Mexico, and then after that, we'll get to a Nick Hill's question shout out to mixing mixing is the building good to see, man? Check out the interview that was

Speaker2: [01:16:13] Released, I think it was this week

Harpreet: [01:16:14] Right on Ken's podcast with mixing stuff. Check that out. Greg, go for it. Yeah.

Speaker3: [01:16:22] So basically, it's graduate school. I feel like it was really oriented towards adding context, especially MBA programs. I think programs like this will really focus on use cases can win a lot, especially in learning the business environment. So nowadays you live in the time of open source. So there are a lot of use cases that we can pull out their repos where the data is already seeing there. And then you put it in a business context and you have these guys, you know, study or perform certain analysis. And I totally I can totally relate to to can about that, know having access to the data or knowing how to

Speaker2: [01:17:09] Access it with

Speaker3: [01:17:11] Sequel is is really huge. But also understanding the removing the context of, you know, tool, but the focus on the how and doing it with the minimum tool possible. So for example, again, Excel, for example, is the birthplace of the AI and its power query.

Speaker2: [01:17:35] So you could do a lot

Speaker3: [01:17:37] Of things in Excel that you can do in power bi.

Speaker2: [01:17:40] So all of these use

Speaker3: [01:17:42] Cases, you can create an environment for these guys to understand one. What are the business metrics that they need to analyze? Have sound analytical capabilities like know how to perform [01:18:00] trend analysis, know how to perform some high level statistical process control. Sometimes, you know, even knowing how to perform ANOVA know even some forecasting method, too, depending on what the what they do. Optimization is another one. I'm only thinking about the things that I've done

Speaker2: [01:18:19] In my graduate program.

Speaker3: [01:18:21] And, you know, at the end of the day, putting these

Speaker2: [01:18:25] Methods

Speaker3: [01:18:26] Inside of a context or a different context which which which which is

Speaker2: [01:18:31] What I call use

Speaker3: [01:18:33] Cases or business cases is really key to giving them a good leg forward when they enter the real world.

Harpreet: [01:18:42] Greg, thank you so much. Makiko, go for it. And then from Kiko, and then we'll go to Nic Hill's question.

Speaker4: [01:18:48] Yeah. So there was a period of time where I was kind of straddling the analytics to the data science world, and I was focused on strategic finance, supply chain revenue operations like kind of the meat of a business and using Data in a business. And I feel like the struggle in AI also being successful there, I feel like the struggle could be encompassed in. The annual planning exercises where we try to put together like a business scorecard. That gave a comprehensive overview specifically of like our sales forecasting and expected sales projections. It was almost like. Pulling in so many different skills because there's the one layer of there's the visual communication and the business communication, how how, how do you tell a story?

Speaker2: [01:19:54] Then there's a

Speaker4: [01:19:55] Second part of actually just like problem solving, which is using your context right [01:20:00] of the business domain. So it could be finance, it could be supply chain, it could be whatever. Do you understand some of the fundamental equations or theorems or what have you? Such that you can then frame and identify the problems and then come up with the relevant solutions, which is kind of hard. I don't really know how to teach on the undergrad, but then it's how do you then forecast in a manner that is both? That leverage is not just like low hanging fruit, machine learning techniques, but also the business partners like the business partner as an SME. So one of the big mentors which I had to change was I went from a forecast is whatever our like managers like the GM's or whatever would float up there, their individual excel sheets and whatnot. And that's like the running that's the running of like how things are going and what they think versus like how do we get in over like a company overview and then how do we adjust our forecast

Speaker2: [01:21:08] To the actuals?

Speaker4: [01:21:09] I'm sorry, I'm doing a really bad job explaining this, but it's funny because like, I feel like that series of projects to me summed up all the things are really, really hard about being a really good analyst that's working with business partners is you need to operate at these different levels. I do feel like one of the biggest gaps was the like utilizing low hanging fruit machine learning techniques, while still combining it with the human business input to then get a really good picture of of the business. I thought that was really kind of hard because we had to figure out the dashboard. We had to figure out how to frame the question, and then we had to somehow float it up into our snap like in our annual planning process. And a lot of people have different theories about it, but I've never seen one where it connects the like the technical like, how do you do it with the business problems [01:22:00] and the domain? And then how do you then create like a viable Data? Business product out of it. I'm doing a really terrible job explaining it, but I think that's so cool that you're doing that. So I just want to say it fits very well with what I've struggled with at Intel. So I think I understand what you were describing,

Harpreet: [01:22:20] Then go for it.

Speaker6: [01:22:21] 10 things I know is I was 100 percent sure Greg or I was going to say this. So for four classes that I would add or at least four areas of focus that I would add focus really on, what do you do with Data? And we don't teach anyone what to do, how to work with Data. And from what I've found, that's useful in helping people like learn how to make decisions with Data introduction to like an information sciences overview, introduction to decision science, introduction to actually not an introduction. You could probably go pretty, pretty deep into game theory, because if you're already going through an MBA track, you've probably got the background to run through game theory. And then finally, do a class of neuroeconomics, because that's really the the synthesis of those three

Speaker2: [01:23:15] Domains is

Speaker6: [01:23:16] Neuroeconomics. And so that's it's a really interesting progression from understanding Data with information sciences to understanding the basics of decision making with Data to understanding game theory, which really gives you frameworks for decision making and evaluating alternatives and understanding collaboration uncertainty. And then finally getting into neural economics, where we're beginning to really understand how we make

Speaker2: [01:23:47] Decisions and why we're such

Speaker6: [01:23:49] Trash decision makers and why we somehow survive even though we make terrible decisions, which is kind of one of those wonderful things where you realize it's okay to make bad decisions, you just have [01:24:00] to get better at it. And so there's this. I mean, that's kind of an evolution that I wish we had more of. I wish we did those four classes for anyone that was even thinking about becoming an upper level executive leader or C-suite. Because if you don't, how to make a decision. If you don't have rigorous frameworks for making a decision, not a best guess of it. You really often get promoted beyond your capabilities.

Harpreet: [01:24:25] Ben, thanks so much. Jennifer, a lot of great tips there as well.

Speaker2: [01:24:30] I'm sorry, I really I'm going to

Harpreet: [01:24:31] Start chopping all these officers up into a blog post is to disseminate this information. So much good stuff. Let's head to Nick Hill's question. Are you still here to kill?

Speaker3: [01:24:44] Yeah, invited. Yet. I just think of so much of job. I get it. And the transition question to me, just give you a quick background and everything that I have gathered and. Concerns with the FDA and the. Uh, so we guys must know that we need to unload everything and along with that research. And I just what I just read this article that I think it was about how power guy is being diverted. And I'm pretty impressed by that, and I think it was a couple of months ago and I've been spending a lot of us a couple of months, so I finally decided to do this thing and I've been trying to upgrade my skills and everything. [01:26:00] And I was just listening

Speaker2: [01:26:04] To a lot

Speaker3: [01:26:05] Of people and all this stuff, I'm thinking about getting my to to them. Uh, but people of the district need to do. Maybe because if we don't do it, we don't have anything to stand up to, and there's a very high chance that you weren't going to get the win.

Harpreet: [01:26:38] So just to recap real quick, because the audio was not coming as clear as I was hoping it would. Should you leave your PhD to get a job? You've heard people say that without a PhD, you'll be not perceived as qualified or whatever to get the role. If anybody hear the question better than me, please let me know. But let's go straight to mixing for for this question,

Speaker2: [01:26:59] And then

Harpreet: [01:27:01] I'll see some input

Speaker2: [01:27:02] From Mickey.

Harpreet: [01:27:03] So we'll go to Nick, then Kiko, and then anybody else wants to jump in. And also, if you'd like to you, if you just want to quickly type out the actual question right there in the chat to that way, we're all clear on that. That'd be helpful.

Speaker3: [01:27:17] Yeah. I mean, my just quick answer is from what I heard your question. Hey, if you don't have a PhD, people won't call you in for interviews. I don't think that's true. I think PhD can help but slogging through four or five years just to get an interview. There's way more high leverage things you can do.

Speaker2: [01:27:35] Wrote a whole book on it. It's a data science interview. But even if you didn't read that

Speaker3: [01:27:39] Book, it's just in general. Hey, the why of four years? I do it because you're interested in it or you want to dove deeper. But just that's a very practical thing. There's so many better ways you can get interviews, probably next month. So that's it, does he?

Harpreet: [01:27:56] He mentioned he's in India, does the advice change for somebody in India? [01:28:00] I mean, is it different like, OK, if you're in India trying to get a job in the USA, right? Like this?

Speaker3: [01:28:08] Yeah, I get that vibe for sure. There, I would say. Like, Dude, if you got to get a masters to immigrate, like, I totally get that. That's what my my dad did. You know, like, I understand that part. I guess my PhD, I'm thinking even pass masters, you know? You know that that's cool. But yeah, get a master's.

Harpreet: [01:28:29] Yeah, clarified here. He's a PhD student in Canada, so he's already already here. So awesome. Let's go to Mexico, then Coast, Deb.

Speaker4: [01:28:42] Ok, so. I let me touch on two points, and then I'm just going to end that answer because honestly, I didn't go the PhD route, I didn't go to the master's degree I graduated with like a two point four. In anthropology, so I am literally, probably one of the least educated people on this call, like right now. Actually, I think for sure, I am maybe one of the least educated people on this call.

Harpreet: [01:29:11] Definitely one of the smartest, though, if not the know.

Speaker4: [01:29:15] But like a couple of things. One. And it's funny. My partner, who's Indian and I Japanese, we talk about a lot. We hear a lot of pressure from our Asian families about you need to do x y z. I'm not saying that's where the pressure is coming for you, necessarily, but things, for example, like they were telling him, if you don't have a full time job, no one's going to want to look at you or marry you or things like that. You know those things, right? I would say, you know, as well-meaning as families can be and as families and friends and those communities, especially if your first or second generation immigrant, sometimes they they overindex on what has worked for them in the past or they see these like common [01:30:00] indicators of success. And the thing is like, there's probably a lot of students who have the assigned jobs who honestly probably could have still gotten those jobs without the Data science because they are curious, they're hardworking. They are good at leveraging resources to solve like

Speaker2: [01:30:18] It's one of

Speaker4: [01:30:18] Those things, like if someone gets into Harvard, does that make them automatically better than someone who goes to Cal Poly or whatever? Right? That's not really the case. Sometimes it's just they're selecting on the traits that people have who would have been successful anyway, right? So I would say, first of all, we just it's it's good to just sometimes take a step back and understand where this advice is coming from and also what are the assumptions that are driving that device?

Speaker2: [01:30:44] That advice,

Speaker4: [01:30:44] It's it's a super important skill like in science. Anyway, if you steer result, can you sort of break down what is driving that result? So if you see a difference in a campaign or in a model

Speaker2: [01:30:55] Prediction,

Speaker4: [01:30:57] Can you understand what's feeding that? And a lot of times we carry these these models of operation from our families and friends that are well-meaning, but they're ultimately kind of damaging to one's social and mental and economic and health and all that, you know? Yeah. So the second part is if you're being paid for the internship and if it has if there's a visa situation, I definitely wouldn't just quit it to like, go get a job. It's very, very competitive. The way I look at it is that they're paying you to study in Canada from what it seemed like, you could probably utilize that time to actually get yourself really prepared into interview around, because it's probably a lot more interesting to say. I'm currently in a PhD program and I'm thinking of ways to sort of apply that skill set. And all that versus I just left my PhD problem and I have 90 days or whatever before I have to leave the country. Please, please, please give me a job. So I'm going to leave it to. I'm not. Yeah, [01:32:00] so I think other people can speak to that. I would just say, don't ever

Speaker2: [01:32:03] Assume that

Speaker4: [01:32:05] A degree will get you a job that has been proven to be false the last 20 years, unless it's something very, very specific and research oriented, like a job at Google Brain for which it seems like, honestly, that is like the de facto sort of. Requirement, and that's because they're basically getting paid to do very cool research. So not different from what you do in a PhD program.

Harpreet: [01:32:30] The coast captain, Jennifer.

Speaker7: [01:32:34] So the thing that I see a lot happening and I've seen it happen to a lot of friends of mine, and it's basically this idea of and I reckon it's

Speaker2: [01:32:44] Confirmation bias, right? It's positive

Speaker7: [01:32:47] Attribution

Speaker2: [01:32:47] Bias in the sense that we see

Speaker7: [01:32:49] Successful people in the field with PhDs, but we don't see the number of people who have done PhDs that then don't actually convert that into a, you know, world beating career in that field, you know, so there is this positive attribution bias we need to really be careful

Speaker2: [01:33:05] About.

Speaker7: [01:33:07] So you've got to really question what's and the words that Nick used is actually spot on. Roy, what's the return on investment on any degree that you

Speaker2: [01:33:15] Do anything that

Speaker7: [01:33:16] Especially if you're paying for it now, I understand you're getting a stipend for this PhD so that that changes things a little bit. But let's remember that your investment in that is

Speaker2: [01:33:24] Your time right appears to be

Speaker7: [01:33:26] Is four

Speaker2: [01:33:26] Years now. A couple of years ago,

Speaker7: [01:33:28] I was assessing whether I wanted to go into a PhD or what I wanted to do a

Speaker2: [01:33:31] Master's, and I

Speaker7: [01:33:32] Actually found that really what I was looking for is that little bit of education that I struggle to teach myself because of the way that I learn, right? So I found that a one year master's was enough to push that through and get the amount of research that we did in that one year. Master's was enough to give me those blanks that I needed to fill my career

Speaker2: [01:33:52] To get to where I want it, to

Speaker7: [01:33:53] Be right to put me on the track that I wanted to go on. And it was to my mind, it [01:34:00] may not be the right master's degree for everybody, but one year won me a year and a half that I would have otherwise spent a couple of other universities right that year and a

Speaker2: [01:34:08] Half to me is

Speaker7: [01:34:10] I could work for a year and a half, save for a year and a half. But like

Speaker2: [01:34:13] Invest in a property sooner, I can

Speaker7: [01:34:15] Invest in other things sooner. So financially, it's a much more sound decision. So you've got to consider where in life you're at and whether you actually want to do a master's or a PhD. So that's that's one side of things. The other side

Speaker2: [01:34:26] Of things is, and I see

Speaker7: [01:34:27] This a lot with PhDs is a lot of them do graduate and end up overqualified and under experienced. And I see this a lot in Australia,

Speaker2: [01:34:37] Where we have a

Speaker7: [01:34:38] System where you can finish a bachelor's of engineering or for you any any four-year science or engineering degree that has an honest component to it. You can go straight into a PhD. I don't know what it's like in Canada and USA, but basically we end up with loads of people, particularly. I see this would be a physics. It'd be aeronautics where there's not a huge job market and they jump into doing a PhD. Then they come out the other side three or four

Speaker2: [01:35:03] Years later, lot what they were

Speaker7: [01:35:04] Doing. But the transition into industry is extremely difficult because companies can't justify paying them what you would expect a postdoc to receive because they've lacked that experience working in the field. So I kind of subscribe to an old school model of do a PhD. Once you've already gathered around an element of expertize in an area because you work out a lot of your problems in the field as opposed to, you know, necessarily while doing a PhD.

Speaker2: [01:35:32] But if your reason

Speaker7: [01:35:33] For doing a PhD is, Hey, I want to spend and dedicate time researching this field because this is amazing to me, that's worth the investment. So your return is different. It's a it's a personal satisfaction return. Right? So you really got to assess whether the course you're doing in the time

Speaker2: [01:35:49] You're spending is actually

Speaker7: [01:35:50] Getting you what you want to get. So in that sense, if leaving the PhD stream and walking away with the master's because it's going to get you

Speaker2: [01:35:56] To the field, do you want

Speaker7: [01:35:57] To work in sooner? Maybe that's the right call [01:36:00] to make, so it takes some time to really iron

Speaker2: [01:36:02] Out

Speaker7: [01:36:03] The investment that you're putting. Value, your time, value, your money. Value your effort. And we really try to understand it down to the bare bones, why you're doing what you're doing, right, like start with why? Like that's that's entirely what any major career advice

Speaker2: [01:36:19] Tells you to start with.

Speaker7: [01:36:20] Why are you doing what you're doing? And then you can figure out the pieces and to how you get there. And yeah, absolutely. I agree with everything that they can.

Speaker2: [01:36:29] Everyone before me

Speaker7: [01:36:29] Said, is we over fit on this idea that education is important, but also that quote that you see flying around. I don't know if it's Elon Musk or someone else that they've attributed to Musk now. But don't confuse schooling with education. I don't think, particularly in the data science field, particularly in the software field where these skills are accessible and people can learn

Speaker2: [01:36:50] Them through multiple

Speaker7: [01:36:51] Pathways. I don't think that educational qualifications is as much of a barrier to entry than, say, in the accounting field, where you need to be a chartered accountant or, you know, in other fields where obviously being a doctor, you can't go and learn that off. Udemy, right? But let's be real, you can do it some other way. So let's not confuse education and schooling. That's all I'd say.

Harpreet: [01:37:14] An excellent, excellent advice, thank you so much had as Mark Twain and never let schooling get in the way of my education. Jennifer, go for it.

Speaker4: [01:37:26] So I would actually agree with a

Speaker2: [01:37:28] Lot of what has

Speaker4: [01:37:30] Been said, and I'm going to give you the flip side because my husband got his PhD. He went straight through and got it.

Speaker2: [01:37:37] It opened a

Speaker4: [01:37:38] Lot of doors for both his initial career and a midlife transition

Speaker2: [01:37:43] Into teaching at

Speaker4: [01:37:45] A university. So you've got to know very clearly what you want.

Speaker2: [01:37:49] Is it necessary within his field? Yeah, that was

Speaker4: [01:37:53] Necessary for where he wanted to be at inside a company like Intel and teaching at a university. Those [01:38:00] are things that get your foot in the door and get you the interview. There's a lot of work out there where it's just not. And so you've got to know what you want to be doing with the degree. I'm actually a real proponent right now of Get the Degrees while you can. I started an MBA program shortly after college and I did not finish it.

Speaker2: [01:38:24] I'm going back now. I really

Speaker4: [01:38:26] Wish I had done that earlier because it's

Speaker3: [01:38:28] It's a certificate

Speaker2: [01:38:29] And and it's it's

Speaker4: [01:38:31] Something that I wish I didn't have to do at the same time as

Speaker2: [01:38:34] As a full time job.

Speaker4: [01:38:36] So I'll put in. I'll put in a score for that one,

Speaker3: [01:38:41] But when it comes down to it,

Speaker4: [01:38:43] What is the reason for the degree is going to be up to you?

Harpreet: [01:38:49] Excellent, excellent tips there. Jennifer, let's go to Greg.

Speaker3: [01:38:57] I was I was just about one Mexico City, this is real. The whole 90 day thing, it kind of struck a chord with me because I was there at some point too. I was an international student and actually got lucky. I think I spoke about that Harp when when I was invited to a podcast

Speaker2: [01:39:18] Of I

Speaker3: [01:39:18] Was looking through an unfortunate event in my home country, Haiti, which is the earthquake and because of the earthquake, the United States, because I was already inside of that 90 day, he'd find something and had a master's degree, find something or after 90 days, you get your out. And after the earthquake, the United States was like, Hey, these guys were here. They don't have a place to go home. That just gives them a way to stay with a work permit.

Speaker2: [01:39:48] So there's no clear

Speaker3: [01:39:51] So out of this strategy, you have this lucky thing that got me into the corporate world in America. So I was pretty lucky. And it's tough.

Speaker2: [01:39:59] There's no right [01:40:00]

Speaker3: [01:40:00] Way. You just have to push your luck and look for the next opportunity and understand, know what you want first and go for it and try different ways to get there. To Jennifer's point, it's really true. You have to know what you want. Sometimes is the right way. People use PhD to stay longer in the United States. I've seen people do that

Speaker2: [01:40:23] And then they feel

Speaker3: [01:40:24] Stuck because they're not interested in the research part that comes with most of the time. But you know, you have to really make a strategic decision and stick with it and don't think that there's only one way to get to that end point. You should try different things and stay ready and ready to jump to the next opportunity. So thanks for that reminder, me. Keep up. It's great.

Speaker4: [01:40:49] And I feel the U.S. would have been at a complete loss if you had left, so I'm glad that we got to keep you, Greg. Absolutely. But yeah, I mean, like, that's the thing, right? Like I think. So I feel like. So I feel like what's glorified is making like big decisions and like making the jump and also stuff like you see it in a lot of times in media,

Speaker2: [01:41:15] Like the entrepreneur

Speaker4: [01:41:16] Who. I quit his job and then was starving on the streets and then was working on the startup, I mean, that was actually the story of box kind of in a way. I remember when I was like hearing the Xbox CEO, he talked about living on,

Speaker2: [01:41:30] Well, his floor,

Speaker4: [01:41:31] Any top ramen in a one bedroom apartment, right? But I feel like what you see, what people who are really successful, especially navigating very uncertain situations, what they do is they just don't cut off their options. I feel like that's that's really, really critical is if you can keep your options open and you can kind of keep leverage. That's really the best sweet, sweet spot to be. So if you're not interested in your PhD program, like keep going with it, but you use that time [01:42:00] somehow to to network, to build up projects, to do do all that other stuff. You know, I would say just don't serve, burn the boat until you 100 percent know what you're doing, right? So last year or whatever, I quit my job at Teladoc in the middle of the pandemic. And my family was furious. Like, I, you know, it's funny, I switched my jobs this on Sunday night because I'm like, no one is going to know, Sue Karolyi told my parents. I was like, No one's going to know, no one's gonna see it. And that ended up being the most liked status of my LinkedIn profile ever. So everyone found out, including all my mom's friends, including some of the aunties. They all found out that I quit my job to go work on this real estate tech startup, which that's OK. Then I didn't work out, and that's OK. I was able to find another job.

Speaker2: [01:42:56] But the

Speaker4: [01:42:56] Reason I was able to make take that informed risk taking and decision

Speaker2: [01:43:01] Making. Yeah, I know the is right.

Speaker4: [01:43:03] Like, they're brutal. And the second and third cousins like, you think they're your homies? And then and then they squeal,

Speaker3: [01:43:10] And then they show them your Instagram posts and then it's all game over. Once they find it is grim or, you know, all the LinkedIn in our case, you know? Yeah.

Speaker2: [01:43:18] Yeah, that's that's pretty much what happened.

Speaker4: [01:43:21] So but the reason I was able to make such an what seemed on the outside like an extreme decision, especially when people are losing their jobs left and right from startups like, you know, like Twitter,

Speaker2: [01:43:31] I think had let go

Speaker4: [01:43:32] Of a bunch of people, some other companies, right? Was that when I the month I quit, I had I had five or six final round offers or final round interviews and offers for data scientist roles. So I'm like, OK, I know I can kind of get this lockdown. I had a bunch of money in the bank and I'm also an American citizen, so I didn't have to worry about being pulled out of the country. So when you see people making these like big [01:44:00] decisions, you don't realize how much like capital and privilege they have behind them to be able to make those decisions and take those risks. You know, so that's something that is like a really, really important in most of my jobs. I even when I hated them, I stayed with them. I went to school. I would go to school at night or whatever to boot camp. I keep bringing in money. It was hard, but I had to do that because realistically, I was not in that position of the entrepreneur that could like, go, quit their job, go to a startup. No, I could raise funding and stay in the country, right? So what I would say is that rather than thinking of decisions or options like as an A or B, try to see how you can really blend them and mitigate your risk as much as possible. It's not the sexy approach, but it's the real honest to God. You know, ground truth

Speaker2: [01:44:59] Is

Speaker4: [01:45:00] Really smart like really, really smart entrepreneurs. They risk mitigate as much as possible in various ways, you know? And the other part to right is for a data scientist gig. And like, you know, Nick, definitely talk about this. One of the ways to figure out if you really like is building projects and shopping it around, like shop that experience. If people are biting for it, then maybe that's a good indicator. But if they're not, then maybe that's not the right approach is quitting or PhD to go work on that. You know, you need to have more data. You need have more information to really be able to make these decisions. But the beautiful part is not cutting off your options gives you more time to make these decisions and to gather data so.

Harpreet: [01:45:43] Excellent tips, thank you very much. Let's go to coast up there, Nick.

Speaker7: [01:45:51] Yeah, something that like just kind of echoing what Mexico was saying, something that a lot of people don't experience until they experience it or just cannot [01:46:00] comprehend until they experience it, is the safety of being in a country where

Speaker2: [01:46:05] You permanently

Speaker7: [01:46:06] Have the right to work in the right to live right. Like, I went to the UK and as an Australian citizen in the UK, that's not really like theoretically, that's not such

Speaker2: [01:46:17] A big deal, right? Because it's quite easy for

Speaker7: [01:46:19] An Australian citizen to get a permission to stay in the UK. But I'll be honest, it was way like there was an emotional component to it that I'd never expected when I moved there.

Speaker2: [01:46:30] There was an emotional component to it.

Speaker7: [01:46:31] I don't have the right to be here, the same as a lot of other people and immigrants will face that people moving to other countries will face that. So you've got to be really, really clear on your on literally why you're doing what you're doing and what your goal is, right? Because there's going to be that factor as well unless you have

Speaker2: [01:46:50] The privileges of being, you

Speaker7: [01:46:52] Know, of having those opportunities in your home country. Right now, I'm lucky enough to have great opportunities at Australia, so I can do that quite safely. But I've just got to appreciate what a lot of a lot of people go through in this process. And there is that burden. Be really real about that burden on you as well, because that is another form of effort that you're investing, right? The emotional effort to committing to what you're doing and that takes energy and that takes time, right? So, yeah, absolutely what we keep saying factor in all of those things and really come up with

Speaker2: [01:47:22] A map that can keep your

Speaker7: [01:47:23] Doors as open as possible and yet out the decision as opposed to just jumping

Speaker2: [01:47:29] Into it.

Speaker7: [01:47:31] You're a Data guy. Clearly, it's not working on the Data, right?

Speaker3: [01:47:35] They go for it. Uh, what I

Speaker2: [01:47:38] Really liked and what

Speaker3: [01:47:40] Mickey said was two things. That whole founding story about the boxing, oh my God, half the founding stories are made up, right? Like even even that one, I'm not sure because I've seen from the little sample size I've seen and people are not being dishonest about it. It's just sort of like one guy told me, like crashing all these couches, right? And like he becomes, if someone [01:48:00] repeats that story too many times, it's almost like, wow, like, you didn't have to crash on that many couches, right? Like, you didn't have to eat ramen. And and guys, trust me, if I make it, I'ma tell them all in my mom's basement right now. But like, low key, I'm here just because I don't want to be an S.F. during COVID or whatever. But like, you know, I tell people like, Yo, I was, I was in my mom's basement writing the book for a year and like, you know, I don't know, man, I have just found these stories that are crazy and kind of not true. In the same way, at my last company, I liked my boss or the CEO. And he's a great entrepreneur, VC back all about high risk, high reward. But he got his money first from like doing smaller businesses that he he in the dot com. Ninety nine, he did something in his college dorm room. He was able to flip for maybe, let's say, a million dollars, which is enough to sustain a whole lifestyle. When you hit that kind of money at twenty two, twenty three and I think a lot of people in crypto can do it now, but they won't say like, Oh, I had a million dollars and I got to do it that way. So I don't know these founding stories, man. Like, they're so confusing to hear, and I'm more and more and more. I'm like, These are not true. And even my founding story is

Speaker2: [01:49:10] Going to be like, Hell,

Speaker3: [01:49:12] Bootstrap, and I had no money. But realistically, the money for my book and I live at my parents' house. But I wasn't by choice to save money because because actually what you said also, I just love what you said so much that I'm like. It rains on me. The other thing was entrepreneurs are like seen as risk takers, but actually the risk mitigators like I've seen that so much like it's not about taking wild risk, it's about taking like actually really intelligent risks, which includes a lot of risk mitigation which people don't talk about.

Harpreet: [01:49:40] So, yeah, like isn't that like the the story from Adam Grant's book originals? He's talking about that. He's about to invest in Warby Parker, but didn't

Speaker2: [01:49:50] Because the founders

Harpreet: [01:49:51] Weren't all in and given up everything to do this thing. But they're mitigating risk. They're being smart about it. All [01:50:00] right, any other comments or questions? Nicole, hopefully you are good with all that advice. There's a lot there. And that's one hell of a way to kick off the new year man now. It was amazing. Dope, dope session. Thank you guys so much. Absolutely love hosting these things. This is a lot of fun. Keep it going this year. Every single

Speaker2: [01:50:21] Friday, I'm going to

Harpreet: [01:50:23] Start doing a better job of trying to unlock the wisdom

Speaker2: [01:50:26] In here by just

Harpreet: [01:50:27] Writing more about what we got, you know, going on and the questions. So hold me accountable to it. You know, if you guys don't see a blog post from me a week

Speaker2: [01:50:36] Recapping what's going on in the

Harpreet: [01:50:38] Office hours, get on my ass. But we've got a busy, busy month this month, so I'll have to start in February. So thanks, guys, for hanging out.

Speaker2: [01:50:47] Be sure to tune into the

Harpreet: [01:50:48] Podcast or at least an episode with Johnathan Rice Intel talking all about blockchain in that episode, something that I've been really interested in, but, you know, not putting enough time into. But we had a great conversation, part one of a two part conversation that's going to get him back on to the show. If you don't know Johnathan Rice, you can tell he's he's done a bunch of courses on LinkedIn Learning all about

Speaker2: [01:51:10] Like blockchain, three

Harpreet: [01:51:12] Nfts and things like that. So he's really, really big, big piece, I don't know name of space or educated in the space, so check that out. Also this coming Wednesday, 2nd. An episode in the series for standardizing the experiment. Second of eight, for a series that I'm doing with Comet, so it's check it out, there's the session, one is up on YouTube as well as check all that stuff out, guys.

Speaker2: [01:51:43] That's it

Harpreet: [01:51:43] For today. Thanks so much for tuning in. Happy New Year, everyone. Thank you for spending

Speaker2: [01:51:47] All this

Harpreet: [01:51:48] Time with me. I appreciate having all you guys here. Great questions, great discussions, y'all. Take care, have a good rest of the week. And remember you got one life on this planet. Why not try to do something big? Cheers everyone.