/Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)

Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)

Video: Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)

Subtitles

I get paid a hundred thousand base salary like I'm hired as a new grad Hey guys welcome to this episode of Reality vs Expectations where I get people from different careers and I have them write about five cards of what they thought their career was gonna be like versus what it's actually like today I got Joma currently his a Data Scientist at Facebook previously he has worked at LinkedIn Buzzfeed and Microsoft he graduated from the University of Waterloo with the degree in Computer Science lets get this video started Hey guys today I got Joma he works as a Data Scientist at Facebook but Joma can you tell me the story about how you became a Data Scientist? Yeah sure so I did a lot of internships when I was in college I did some Software Engineering internships and I also did a Data Scientist internship at Facebook and then after that I worked fulltime at Buzzfeed as a Data Scientist and then finally I came to Facebook as a Data Scientist can you walk me through a day in your life as a Data Scientist? Yeah sure so mostly what we do is we come into work and then usually we have a lot of meetings because we have to talk about what are like the next goals and what metrics to track for our team so for example I work for videos at Facebook and basically what I do is I query a lot of querries getting some data to try to make decisions for the PMs give me an example of a specific type of data that you're looking for yeah sure like for example you wanna see alright what countries are doing like most well for our shows cuz now we have a watch tab on Facebook and we wanna know which countries are doing the best and which countries should we invest in and that's one way to look at it and how did you become qualified to get a Data Science job? yeah so It's actually a lot of different background you could come from a lot of different backgrounds my background is in Computer Science so with the Computer Science degree I was a little bit more technical and then when I did the internship because the internship they allow anyone to get it you don't need to be technical and that's where I learn how to do Data stuff like for example basic stats did you come from like extremely qualified school? is that why your looking for internship? I went to University of Waterloo which is a Canadian school and they do a lot of internships I wouldn't say its like the best school in the world like its not ideally at all but we do a lot of internships and maybe that's why we get more preference over other schools okay so lets go to the Reality vs Expectations questions what's your first card? so my first card is most people think you need a Ph.

D to be a Data Scientist and that's actually a myth because I don't have a Ph.D I should just have a bachelor and then at Facebook I've met many people that have the Neuro Schience degree or even someone that had a Family and Sexuality degree and most people come from consulting backgrounds so yeah so you deffinitely do not need a Ph.D I think the reason why peope are confuse by it it's because when you think of Data Science you think of the machine learning of Data Scientist since they came from different backgrounds how did they teach themselves or how did they learn the skill well enough to get a job? so theres two ways either you do an internship or you just study basic stats because to be honest its less about learning the fundamentals or like being really good at the stats to be good at this Data Scientist you usually have to have more empathy to be good at Data Scientist because you have to ask the right questions and then answer them thoroughly because technically its not that hard you only need to know some sequel queries maybe a little bit of Python which everyone can learn what's Reality vs Expectation card number two? yeah so this is a little bit related to the previous one most people think Data Scientist works so solely on machine learning and like artificial intelligence but that's not true I just wanna talk about the three arc types of Data Scientist theres one is Data Science analytics? that's what I am and then I'll talk about that later and then there's Data Engineers and then there's also Data Science Core that's what people at facebook calls so Data Science Analytics this is like us we just like a Data we do some sequel queries we process it we make graphs and we communicate with to the Product Managers and then Data Engineers those are the one that retrieves the data build the infrastructures so we can actually look at the data and then Data Science Core those people are like the hardcore Ph.

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D with like recommendation models in forecasting awesome, what's card number three? yeah so card number three is Data Scientist is just about putting a bunch of data in a Blackbox model and then I would just output an answer so that's very not true because what's most important in Data Science is about you know like I said empathy and also understand what the real questions are I'll give you an example why you can't just put in a Blackbox now Blackbox what happens is you give it input and then you say what to optimize for now imagine you have a video product and you wanna focus on a specific country in the emerging market and then to help boost your video product and then what you wanna optimize is time spent it makes sense right because you wanna make people watch more videos and then you put in a Blackbox and it says oh Vietnam or Thailand is the best country but then that doesn't tell you the whole story because what if the reason why they spent so much time it's because their just spending time buffering or loading the video so that's why you can't just put things in the Blackbox cuz you have to understand exactly what's happening to the users on the other side can you define Blackbox? yeah so Blackbox meaning like models that people pre create for example a simple linear regression or like a random forest or even like a deep learning model sometimes you can't solve problems just by encoding data in these models so that's why I mean by Blackbox It kinda relates to like everyone thinks correlation equals causasion like a two things correlate they think it's causing obviously this Data Scientist you know better that just cause two things are correlating doesn't mean that you know the causation exactly so one of the biggest mistakes is you know correlation vs causation and you will always find a correlation and then you would optimize on that certain thing thinking that it would benefit the other thing for example time spent correlates with likes for example and then what you see later on is that maybe if you increase time spent it doesn't necessary mean the more likes you'll get cuz maybe you'll just get wasting time spent and stuff like that time spent there are lower quality exactly, what's number 4? you need to know Hadoop Mapreduce and Spark if you wanna be a Data Scientist cuz these are like the buzzwords that you hear the most and that's not true at all cuz I've never written a Mapreduce job in my life I have but not at my job and the reason for that is that usually the reason why you think you need these is because your applying to startups and startups they don't have enough resources to hire the three arc types of Data Scientist so Mapreduce, Hadoop and all of these things those are usually the Data Engineers that work on this or Software Engineers cuz technically you don't need to know much about data or statistics to create these pipelines so the Reality vs Expectations is that you don't need to know these things when you thought you did yup so for example working at facebook especially because it's so big they have three separate jobs for that you know they have the Data Science Analytics the Data Engineers and the Data Science Core so Data Engineers would do all that and you wouldn't even need to think about it you can just focus on you know impact and thinking about how to you know how to make a product better with the Product Managers if someone wanna to get to Data Science today what website should they go to to learn more I personally don't use any websites and I wouldn't recommend websites I think you should definitely just try to get an internship and to be honest this a little bit harder but if you do have a technical background like computer science it would be better and if you still can't do it maybe try to get a consulting job and then move into Data Science okay, what's the last card? the better you are at statistics the better Data Scientist you'll be yeah so what I mean by better at statistics we usually think about complicated models advance forecasting techniques and stuff like that I just wanna to tell you a little bit about what happen to my internship we had five interns one of them did some hardcore forecasting thing that's like really complicated but the only thing it forcasted was for example the number of active users for that specific product and maybe it was very accurate but what is that give us for product what kind of like product recommendations does it give us it doesn't really give us anything so in the end it's not about how good your stats is or how technical you are value can you add to the company and that's what matters the most because if you do many complicated things and like a lot of machine learning stuff but in the end your not giving any value to the company even if it's so cool even if it's like like really advance stuff it doesn't matter cuz I can do the same thing with a simple logistic regression or like a linear regression as long as it has impact to the company then that's what your valued at it's interesting you are saying that it's almost like working as a as a developer too it's related that you can develop this complicated code but if it hasn't have any functionality then there's no point like it's kinda like the difference between like doing something theoretical versus doing something practical theoretical can get so complicated but we can't use it then what's the point right exactly so I mean a lot more then often I see people over Engineer softwares and then yeah it's really good and it's marginalized but nobody can touch it because they just don't understand how to use it and that's useless alright Joma last question if you could go back to the beginning of your career your freshmen you just graduated from highschool would you do Data Science all over again? that's a little bit of a hard question because I do enjoy Data Science now but I think there are somethings that I would like to do more I always wanted to be a Product Manager rather than a Data Scientist unfortunately like through this schooling that I had done I didn't develop the skills as a Product Manager I develop the skill as a Data Science or as a Software Engineer so if I had to redo it I probobly would have focus more on like the business side of things what did you see in your professional career were now you would prefer to be a Product Manager than a Data Scientist yeah so I saw that Product Managers they focus more on execution and they also get more of the credit when things go well in terms of different products like in some sense a Data Scientist is like the right hand man of a Product Manager a Product Manager is like a mini CEO so I always love thinking about products and thinking about you know new and innovative way to think about you know how to reach users and how to make their lives better but usually it's the PMs that have the final say I get paid a hundred thousand base salary like I'm hired as a new grad I get paid the minimum I get a hundred twenty thousand equity for four years that means like thirty thousand equity per year and then for the first year you get thirty thousand dollar bonus for the first year and then you'll also get some random relocation bonus that's worth like fifteen thousand or something like that I hope you enjoy that interview with Joma if Data Science sounds like a profession you might wanna start learning about consider taking this course I link in the description on Skillshare I'm your instructor Frank Kane and I spent over nine years at Amazon.

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com and IMDB.com developing and managing some of their most famous features like recommended for you and I think it's a great introductory course and with Skillshare you get access to over eighteen thousand courses for fifteen dollars a month what a great deal and with that being said I'll see you guys next week bye.