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  • So

  • You want to get into data science, right?

  • well, you're in luck because I work as a data scientist at

  • Untitled company wait I work as a data science at untitled company

  • Hey guys welcome hey guys welcome to another episode of

  • Hey guys welcome to another episode of Joma Tech.

  • Now you might be wondering why am I in my room?

  • Well, that's just how things are gonna be from here on out so quick little update

  • ummm....I had to remove my day in a life video

  • You know the one that got me most of my views. I had to take it down because I violated some policies so

  • That's why it's gone, but today. We're gonna be talking about something different

  • We're gonna be talking about how to get a data science job at a you know a large company like

  • Friendster or MySpace something like that first of all what is data science because I have a feeling a lot of people have you know?

  • misconceptions about data science so if you do I would suggest you to check this video I made with

  • Engineer truth so yeah check that out

  • And hopefully it will give you an understanding of what data science actually is or what my role is alright

  • So let's get to it to the five tips on how to plan a data science job number one tip

  • do an internship and have a technical degree how I got my first hands-on experience on data science was my

  • internship, and I did my internship in a large untitled company and

  • How I got that internship was was I just had a few software engineering background, which you can

  • See you can see my other internships on the previous videos, but yeah, I had a few internships

  • That's software engineering and my degree is in CS

  • I never touch the data science, but or so that's the thing so the easiest way is to get an internship

  • And then usually it will give you a full time offer right after so here are some examples of good degrees to have CS

  • software engineering economics statistics

  • math

  • Just engineering in general like system engineering

  • Maybe even environmental engineering just anything with the word engineering in it

  • It would you know demoness technical and they will give you an interview all right second tip?

  • Learn sequel or SQL. I'm not sure so when you're gonna start your work as a data scientist

  • No matter where it is you're always gonna have to write sequel because you got to get the data somehow no matter

  • How fancy your you know Python is and stuff like that in the end

  • Usually most companies set up their infrastructure so that you query data using sequel why because it's an easy language and even like

  • Analysts, or like business intelligence people they use sequel

  • So you know you'll probably have to use sequel, too

  • because it's just easier for them and then because they set up the infrastructure like that you have to use that too and

  • You also should learn sequel because all of the interviews. I got that was related to data science. I had a sequel question

  • Let me show you an example of question, so I found this take home exam

  • And I will change it a little bit so that the large company which is not the original

  • Untitled large company, but another large company that might or might not be related to transportation

  • Gave me this take home exam, so I'm gonna whip it out so for example what they would do is

  • They would give you a schema

  • and

  • They would give you a schema for example you see ID

  • Client ID driver ID city ID client rating driver rating and stuff like that and then the question would be something like

  • for each of the cities, San Francisco and

  • Los Angeles calculate 90 percentile difference between actual and predicted ETA

  • For all completed trip within the last 30 days, and then what you do is you have to write queries for that

  • Yeah, I'm not gonna go through the answer

  • But you should that would give you an idea of what kind of query you should run tip number three

  • Learn about success metrics and tracking metrics that is very very important

  • And it's kind of hard because it's more about product sense, so why is it important?

  • It's important because when you become a data scientist you will be the extrovert on your team on data

  • Right so imagine if your team is like at McDonald's, and you know your domain data scientists

  • then you're the one that will know where everything comes from like all the data sources and

  • Also, what to measure for success because you're gonna be the one that the PM's asks you like okay?

  • How do we know we're doing a good job?

  • How do we know that the introduction of these new hamburgers?

  • That they're actually successful and making mcdonald a better place for people you know sometimes. It's easy sometimes

  • It's a lot harder to define these success metrics. I'll give you an example taking from the same take home exam

  • so

  • Imagine if the YouTube creator app in case you don't know what the cue tube creator app

  • It's a it's an app for YouTube creators like myself and when you click on it

  • You could see like how many views are getting you know and how many

  • Watch time and like what video is performing a lot, what comments. Are yeah, it's basically a tool for creators like myself

  • So that's basically what the YouTube creator app is so imagine the YouTube creator app is being redesigned

  • You know you're adding CTR metrics are adding better interfaces

  • And they had like a I don't just better user interface like a whole facelift

  • How do you know that the redesign was successful and what are just success metrics in the tracking metrics alright?

  • Let me talk to you about the difference between success metrics and tracking metrics

  • Success metrics is usually the one that the whole team cares about you know they're always seeing growth usually at a start-up

  • It would be like how many active users you have for example not Viacom bonded a startup cannot find com or you can share

  • 6.1

  • Second videos so imagine a Viacom I would care about how many active users like on a daily basis

  • How many unique people?

  • Actually open the app you know I would care about that would be my success metrics because that's the main thing I care about

  • For now and yeah, and then tracking metrics

  • I want to make sure you know that they're actually you know one real people or two having a good time on my app

  • So what are the tracking metrics? So that would be for example this think about it um?

  • I want to know if they're actually watching a lot of you know videos so tracking metrics can be watch time per active user

  • then or maybe just watch time in general and

  • Then another thing can be like how many videos are they watching you know

  • I want to make sure that the inter not just opening the app and doing nothing and then also

  • Let's see another tracking metrics would be you know how many

  • how many like force quit do they have why do I care about that because I want to make sure I want to track the

  • Negative stuff to like or my like or my apps being buggy like if they are and they keep force quitting

  • Then maybe the reason why they spend so much time

  • On the app is because it's really buggy right so I have to track these things to make sure that it's not buggy

  • Basically you want your metrics to be non treatable and represent success on your company, and then the next question

  • After asking like how would you you know define your success metrics? It would be how do you test it okay?

  • so there's one thing you need to know you need to know about a/b tests you need to know about control groups and

  • experimental groups and

  • Like how many people do you need in them enough so that enough so that statistically significant that you could see the differences

  • but also to reduce risk of

  • Having a shitty product that you launched right because imagine if you're the the launch of the new product or like a feature that you

  • Built on not fine calm is incredibly shitty. You don't want everyone

  • You don't want the whole 50 percent of your users to be exposed to that experiment right so you have to think about ways to

  • To make sure you don't risk that but also to get enough information that statistically significant all right tip number four

  • Learn ipython our tableau or excel or whatever to manipulate visualize and interpret data

  • So why do you need to know all this to be honest in my personal opinion a lot of people like to use ipython are

  • because they actually look really you know a

  • Technical and coding intensive, but you know honestly. I see a lot of good data

  • Science only use Excel right they just grab the data using sequel

  • They manipulated using sequel, and then you should put in Excel to visualize things because the impact is not how?

  • Technical you are but how well you communicate those findings, and how well translate into product recommendations now

  • I'll talk about communication later

  • Why do you need to learn all of these because you know you have to manipulate data

  • Time to sequel can't cut it and you need to visualize the data

  • Because you need to communicate them later, so so imagine you have a lot of data and your company

  • Let's see let's go back to McDonald's, so what do you do you do an exploratory analysis?

  • So imagine your McDonald's and you have a bunch of data?

  • About your sandwiches or your hamburgers and stuff like that so what the first thing you can do you could you know use it?

  • To download all the data

  • manipulate them group them by like you know hamburgers or

  • Day of the week and stuff like that and you could discover a lot of things using that and how would you do it you?

  • Would use either tableau Excel R or?

  • Python to manipulate things right because you want to answer questions like all right which day of the week

  • We have the most sales and now which hamburgers

  • Have you know which hamburgers are the most popular?

  • But then you could also say like which hamburgers actually have the highest profit margin plus

  • You know sales like so which one gives you more revenue

  • You know and stuff like that and then which ones are actually losing money because apparently when I work there

  • I scream every time

  • They sell ice creams are actually losing money because employees didn't know how to you know stop putting ice cream

  • So they filled it up too much

  • so they try to teach us how to

  • Put ice cream, but only put around it and leave a big void in the middle so that they save more ice cream

  • I never did that because I cared about my customers, so I want them to have the best ice cream ever even if

  • McDonald's losing money just saying so this is why you have to learn stuff like ipython are and stuff like that

  • Yeah, honestly it doesn't matter which one just as long as you're fast and you're correct at you know creating these

  • Visualizations, so yeah so Indiana use any of these technologies to show your results to interpret them and yeah

  • So that's why you need them alright number five

  • You have to learn to communicate really really well because the whole point of a data scientist is to communicate their findings

  • Right because if you don't communicate your findings. No one will know and if no one will know it's not gonna

  • It's not gonna flourish into anything. It's not gonna Forge into any products learn how to do public speaking

  • You know because you're gonna be doing a lot of presentations

  • You're gonna be talking to a lot of people so a lot of cross-functional partners a lot of people like PM's

  • Engineers and all that you're gonna be talking them all the time and you can be presenting to them learn how to write better

  • Because you can't be presidenting to everyone in the company

  • But you know whatever you found is valuable for maybe for another team, so how do you do that you write about it?

  • You know you write power points you are you write an article within your company you write a post or an email?

  • That's pretty important too because like writing is a very good medium to reach as many people as possible

  • especially at work learn how to make interesting powerpoints because

  • When you're gonna be presenting to people or when you're gonna be presenting you know

  • Your article you want to have graphs and stuff like that

  • And then you won't you don't want it to make it boring you want it to make it obvious what they're looking at I mean

  • That's all I see a lot of data scientists

  • They have like grass with like a shit ton of like fancy crap

  • But I can't interpret it like I look at it, and there's shit ton of bars and like colors and stuff like that

  • But what am I supposed to look at dude? I don't have attention span you know just like a video creator you need to

  • You need to get their attention within 2 5 second. That's the same thing with work if people don't want to read your shit

  • You're not gonna have any value added to them because they didn't read your post so make sure that when you present a graph

  • That it tells exactly what you're supposed to tell so enough information and it's obvious what you're trying to say and improves your thesis

  • So we have the same actually I just say that I don't think it's actually saying

  • But here's the thing if you made an interesting analysis, but no one is there to read your analysis. Did you really make an analysis?

  • Food for thought

  • But yeah, and then bonus last one learn predictive modeling, so this is not super required

  • because well

  • It's funny because most people these

  • Optimized on this learn predictive modeling whenever do you think data signs because they think data signs are really related to like

  • forecasting or like finance to finance

  • It would be pretty important because the whole goal of your of your job is to make more money

  • And how do you make more money you predict the future so that you can capitalize on?

  • But for us as like a product data scientists

  • Predictive modeling isn't that important, but it's good to know and it makes you a little bit cooler, so yeah

  • So it might be useful when you're like forecasting goals and stuff like that or when you want to build

  • recommendation systems on your product, but it's not super important, but it adds credibility and

  • also, you know

  • So that you can tell your friends. I you do modeling whoo all right, so yeah, so that's about it

  • That's five tips on how to land a data science shop at an

  • Untitled large company, and yeah, hope. I hope that was useful. I hope that was great

  • This is pretty short because I don't want to go through the details of it

  • But if you have any question you know comment down below

  • And I'll try to answer it or I'll try and make another video later on so yeah

  • so don't forget to like this video if it you know added value to your life and also subscribe if you like my videos and

  • Yeah peace out. Thanks for watching

So

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獲得數據科學工作的5個技巧 (5 Tips For Getting A Data Science Job)

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    林宜悉 發佈於 2021 年 01 月 14 日
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