5 Tips For Getting A Data Science Job



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 dome attack 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 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

42 Comments

  1. TechLead said:

    I should have mentioned, the only reason I flip cans is to annoy my wife (she hates that move). I've been trying to make it into a trend. The one caveat is, if you're still single… this trick is likely to backfire 🤗

    June 26, 2019
    Reply
  2. Maneesh M M said:

    how install hadoop on windows

    June 26, 2019
    Reply
  3. Amy Nguyen said:

    You are hilarious 😂

    June 26, 2019
    Reply
  4. ram mom said:

    Hai, I am from non tech background so can I go for data scientist ????

    June 26, 2019
    Reply
  5. Sahaya Ajay said:

    How can we use SQL in data science?

    June 26, 2019
    Reply
  6. Nitish Thakur said:

    Hey Joma….loved your video…I was Interested in knowing how you feel about the difference in the jobs of a Machine Learning Engineer and Data Scientist and what are the different skill sets for them

    June 26, 2019
    Reply
  7. David Fihn said:

    I agree that the skills listed are pertinent, but frankly, all the items listed are requirements for a basic analyst position. A key differentiator for a data scientist is to be able to build primary prototypes/algorithms for products or prescriptive advice for a company. To be frank, if you do not have a good understanding of ML/supervised learning, model production, and statistical inferences, the role is more of an generic analyst role. Not saying it's not valuable, but you can not model and build reproducible knowledge from a million samples in a worksheet.

    June 26, 2019
    Reply
  8. Alessandro De Marchi said:

    “this how how things are gonna be from here on now” quoting The Techlead

    June 26, 2019
    Reply
  9. Courage and Peace said:

    i think he is working with youtube
    that is why he is talking so much about policies

    June 26, 2019
    Reply
  10. Courage and Peace said:

    i think he is working with youtube
    that is why he is talking so much about policies

    June 26, 2019
    Reply
  11. Courage and Peace said:

    i think he is working with youtube
    that is why he is talking so much about policies

    June 26, 2019
    Reply
  12. Courage and Peace said:

    i think he is working with youtube
    that is why he is talking so much about policies

    June 26, 2019
    Reply
  13. Daljit kaur said:

    Hi I am daljit, I had done PhD in biotechnology field and want to become data scientist in health care field. I am doing data scientist master course online through self study. Few weeks ago, I had just started it. Now I am learning SAS. I will learn r, python, excel, hadoop. Am I on right path? What else what I should have to do? I have no background in computer science. Please reply me…

    June 26, 2019
    Reply
  14. Shuvo Shams said:

    you work at pronhub man?

    June 26, 2019
    Reply
  15. Dinesh Verma said:

    I have degree in mechanical engineering but I am interested in data science. I also have basic knowledge of SQL and c++. Problem is companies don't prefer different branch candidates even if they learn that skill

    June 26, 2019
    Reply
  16. TheTheawe said:

    Economics?
    Bless my soul, i thought i was doomed when you said "Technical Degree"

    June 26, 2019
    Reply
  17. Hisham said:

    He works at Facebook y'all. 😉

    June 26, 2019
    Reply
  18. Anne Smith said:

    What a boring job.

    June 26, 2019
    Reply
  19. S_O said:

    what companies are good to start internships?

    June 26, 2019
    Reply
  20. Coach Fru Louis said:

    I really like this video. I actually wrote an entire book on this same topic. https://amzn.to/2YEdtjR
    My 5 points were a little different.
    1. Learn Excel
    2. Learn SQL
    3. Learn ETL & Data warehousing
    4. Learn some coding language (java, python, e.t.c)
    5. Be ready to adapt, change, learn and unlearn all that you know.

    Check out my book and sub to my channel if you like it for more details and resources on each of those topics. https://amzn.to/2YEdtjR

    June 26, 2019
    Reply
  21. Dirk B said:

    My 2 cents: This video should have been called "Get a Data Analytics Job". Zero mentions on inferences (The single biggest difference between Data Analysts and Scientists). Good vid nonetheless! Thanks 🙂

    June 26, 2019
    Reply
  22. Roderik Mogot said:

    6:33 Discord?!

    June 26, 2019
    Reply
  23. Jake Stidham said:

    Really appreciate this

    June 26, 2019
    Reply
  24. Balázs Horváth said:

    data science without actual predictive modelling is just data analysis, change my mind

    June 26, 2019
    Reply
  25. gustavo3322 said:

    Did you do a masters degree or took bootcamp courses?

    June 26, 2019
    Reply
  26. Bobbie Floerchinger said:

    Your a pretty groovy guy. Thanks for the videos you do.

    June 26, 2019
    Reply
  27. Rafat Munshi said:

    Thanks for shedding light to what depth SQL might be asked. Can you also let me know, similarly about excel , examples of data visualisation requirements to know level of expertise in excel?

    June 26, 2019
    Reply
  28. atothezeezee said:

    Watched intro, immediately scrolled down to search for the TechLead. 😀 great vid

    June 26, 2019
    Reply
  29. Erin Moore said:

    You're the only person who said that you need a technical degree. No one else I know even has one.

    June 26, 2019
    Reply
  30. Paul Ntalo said:

    Have enjoyed the video, its precise to the point that have learnt alot and gained alot of confidence and insight into my new data science career path. thanx

    June 26, 2019
    Reply
  31. Harsh Reddy said:

    So you work at Facebook. Sorry Untitled company. Can you get me a referral bro. and also the material to learn DS.

    June 26, 2019
    Reply
  32. Steven Oneill said:

    Love your videos and channel!

    June 26, 2019
    Reply
  33. Apple Watch said:

    stop fucking following what is "hot", "popular", "trendy"

    June 26, 2019
    Reply
  34. aliasghar907 said:

    physics degree also work to get in..?

    June 26, 2019
    Reply
  35. Edward Kim said:

    Excel for the win!

    June 26, 2019
    Reply
  36. Viet Nam History Film said:

    https://www.youtube.com/playlist?list=PLzzO0XkEP4ljmBkTuhRUi0x3l3_nOgBJA

    June 26, 2019
    Reply
  37. Testa Rossa said:

    Does an information systems degree help.in order to become a data scientist?

    June 26, 2019
    Reply
  38. Jeet said:

    I have a bachelor's degree in IT but no experience in IT sector (I have 7 years experience in marketing) , can i go for A PG program in data science? Please reply.

    June 26, 2019
    Reply
  39. Justin Justin said:

    I LOVE DATA !!! DATA IS MY PASSION !! I DREAMED OF BEING A DATA MAN SINCE I WAS A KID. I LIVE FOR DATA

    June 26, 2019
    Reply
  40. Young Armani said:

    What about MATLAB ? I hear that is popular too.

    June 26, 2019
    Reply
  41. Brian Roco said:

    You made me like your video because i get what your "Saying" 👍👍👍

    June 26, 2019
    Reply
  42. Daniel OKeefe said:

    11:38 LOL

    June 26, 2019
    Reply

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