Why being a data scientist is awesome!

hey friends how's it going so in the previous video we talked about why you shouldn't become a data scientist and i thought it was a really important beep and i thought it was a really important subject to talk about because data science is such a hyped up field but in this video we're going to talk about why being a data scientist is awesome and don't worry i'm not just going to go take the video about why you shouldn't become a data scientist and flip it around these are fresh real reasons for why data science is awesome let's get to it the first thing i'm going to talk about is like the elephant room is the most obvious thing but i can't not talk about it it's the fact that you get paid a lot of money to be a data scientist and you get a lot of perks especially in tech what inspired you to become a data scientist money so i'm going to give you guys some numbers and these numbers are going to be referring to entry-level data scientists and we're going to talk about california because that is currently where i am right now and i'm also going to make a comparison between software engineering and data science because these are the careers that are kind of similar to each other and then people generally have a better sense of software engineering and you can use that to gauge the data science side so entry-level data scientist in california earns on average 130k a year and in comparison a entry-level software engineer makes 122 000 a year this obviously varies hugely in terms of area especially in silicon valley in the bay area where i am right now so these numbers may seem really really high but you really need to factor in the cost of living and as you progress more in your career as both a data scientist and a software engineer they're still pretty comparable in numbers there are some companies some locations where one is higher than the other there's also places where they're the same and there's also places where one is lower than the other if a data scientist is being paid more than a software engineer the reason is usually because they have some very desired skill sets such as machine learning um they might be research scientists or they have some data engineering skills so i can't say this for all companies and not even like all industries or anything like that but in tech in many companies in tech which is my industry software engineers and data scientists also have very comparable perks and stock options if you would like to learn more about the differences between data science and software engineering and how i myself chose between software engineering and data science you can check out this video over here second reason why being a data scientist is awesome is the freedom to explore the role of a data scientist is very discovery oriented which makes you feel like an explorer like dorado explore you're basically the person in the team who's always thinking ahead thinking about what opportunities there are to pursue uncovering non-obvious insights that can improve your product your business or your model you're also always thinking of ways to change or optimize the way that things work questioning if it is really the best way to do it where there's actually better ways of doing things he also thinks a lot about modeling and forecasting i personally find it really exciting because i'm like an explorer exploring the unknown except i don't have to leave the comfort of my house or the office if i were you know not working from home i always get this feeling like um i'm not sure how to explain it's almost like a tingle of excitement when i'm about to run like the last line of code in which it could potentially like reveal a nugget of insight or something really interesting that i didn't know about before for example finding out that two really unrelated variables are actually extremely correlated with each other or freaking out a way in which a small small change can have a huge disproportionate amount of impact third reason why being a data scientist is so awesome is the fact that it's so applicable to pretty much every single field are you indecisive i can't figure out what you want to be when you grow up even though you might be already grown up and data science might be the right field for you the skillses of a data scientist is pretty much applicable wherever there is data and we kind of are in the information age which means that there is data everywhere say you're interested in finance you could be a data scientist at a fintech company you can be a data scientist at a hedge fund you could also be a data scientist at a bank if you're interested in biology you can be working in bioinformatics you can be working in computational biology you can be working in biotech you could be working in genomics or if you're interested in general technology and internet based companies with lots of products and services like social media food delivery or maybe even dating apps you can easily work in any of those as well there's transportation there's e-commerce there's music just pretty much the world is your oyster of course the disclaimer here is that if you're going to jump around from product and product service or service are very very different from each other then you definitely will have some ramp up time where you have to kind of understand what it is that you're working on but the data science skill set itself is extremely applicable to all of these different industry and all of these different types of companies fourth reason why being a data scientist is so awesome is the fact that you will never get bored you might get overwhelmed but you will not get bored so if you're someone that gets bored easily and you don't like doing things that you already know how to do then data science could be an awesome job for you so there's two specific things i want to touch on about why data science is very conducive to people who are bored very easily the first one is that there's new technology that's being developed constantly like all the time every single week there's something new that's going to be coming out i also listed this as a negative point because it means that you have to constantly be upskilling yourself and learning new things but if you're someone that really loves learning new things then this is a huge plus also because data science is such an interdisciplinary field that's very about exploring new things generally the company that you work at doesn't really care how it is that you do something or what tools it is that you're using as long as you're able to accomplish a task or solve the problem in fact if you can learn something that can save the company more money and you can do things more efficiently and you can do them better then that's even better so as a data scientist you get to explore and use these new tools that are coming up or you can actually develop your own tools as well if that's what you're interested in doing and that's able to allow you to become a better data scientist do better analysis and find out better things and do better forecasting my word of caution though is that it is sometimes really easy to fall down a rabbit hole and just dig deeper and deeper and do more and more complex analyses and use more and more complex tools another core mantra of what i believe a data scientist should always hold at heart is ocom's razor entities should not be multiplied beyond necessity second reason why it's kind of hard to get bored as a data scientist is the fact that there isn't a lot of boilerplate code and each analysis usually has this very unique component to it this is especially in comparison to a software engineer where there's a lot more boilerplate code so yes you definitely still have some boilerplate code like etl cleaning basic segmentations and correlations between variables generally once you grab onto something interesting and start digging into it like i said earlier it's like that excited tingly feeling that you get because you don't know what the end result is going to look like i'm so freaking excited the reason why i think being a data scientist is so awesome is the fact that you often get to specialize and define your own niche data science is a very new field especially compared to fields like software engineering in software engineering there's a lot of specializations that have already been predetermined like front-end engineer backend engineer full stack engineer ios cloud infrared in data science they're starting to have these more clearly defined paths as well but it's still in a very very early stage so oftentimes people get to decide what it is that they want to focus on and kind of define their own archetype for what kind of data scientists they want to be i'll give you some examples of specialties that i've seen the first type of data scientist is what i call like product specialized it's all about making that product better improving that product so they're all about finding opportunities uncovering insights just doing whatever analysis there is in order to improve a product the second specialization i often see is a more technical data scientist and they're ones that tend to overlap more with data engineers who are building tools like platform tools for data scientists and data engineers third specialization is machine learning focus which is the specialization that gets a lot of hype oftentimes people who specialize in machine learning can also be known as machine learning engineers next there's people who are more research focused and generally these are people who have phds who are like really interested in math and statistics and the final archetype is a jack of all trades we will double as a data engineer uh double as a product manager double as a business person and and a software engineer and just kind of like everything and whatever it is that you throw out them they're able to figure out learn and do all right last one i know that the lighting has gotten darker and darker and darker i don't actually have my additional light to put in the front but we're almost there so i apologize that my face is becoming really scary and creepy while i'm looking at you okay before i waste more time the sixth reason why being a data scientist is awesome is that you it's very easy for you to transfer to adjacent fields and not just as a manager i mean so if you're extra indecisive and not only do you not know what industry you want to work for and you don't even know if you want to stay a data scientist then unintuitively being a data scientist may be a good career for you give you some examples and these are examples that i have actually seen personally so there may be more that i have not seen as well um but data scientists who become data analysts very common data scientists become product managers i think even more common data scientists who go into marketing and also data scientists will go into consulting and strategy then on the more technical side so the caveat to these transitions though are the fact that you come from a technical background like computer science or you're willing to put in the extra work if you don't come from a technical background and these are transitions from data scientists to machine learning engineer extremely common data scientist software engineer and data scientist to data engineer and that's it those are my reasons as to why being a data scientist is so awesome and you can barely see my face at this point but i hope this video was useful and was helpful for you and i will see you guys in the next video or live stream you

Channel: Tina Huang

Views: 277,515

Keywords: data science, data scientist, why be a data scientist, why you should be a data scientist, how to become a data scientist, why data science, tina huang, faang data scientist, facebook data scientist, amazon data scientist, apple data scientist, netflix data scientist, google data scientist, data science salary, how much do you make as a data scientist, data scientist salary, data scientist salary in usa, what is data science, data science course, data science for beginners

Id: DoFZtsV25Zw

Length: 9min 42sec (582 seconds)

Published: Sun Sep 26 2021