
Do you have what it takes to be a great data scientist? | Tina Huang
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Tina Huang — Do you have what it takes to be a great data scientist? | Tina Huang. Machine-transcribed; use the interactive transcript above to jump the player to any line.
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Visible puts unlimited 5G data and hotspot in the palm of your hand, powered by Verizon's 5G network, with no contract holding you back. And for a limited time, you can get visible for just $19 a month for 12 months when you use promo code Save6. All the features of Big Wireless Service for half the cost. Tap the banner to switch today. As a apply, Standard Rate applies at month 13, see visible.com for plan features and network management details. Now, some of the few months into my data science job, I'm lucky to have interacted with many data scientists across a bunch of different teams. And I've also had the honor to interact with amazing data scientists that I really look up to. In this video, I want to share with you guys my observations on what makes a great data scientist. This is not just what the minimum skills are to get into data science, like math, programming. If you guys are interested in that, I can also make another video about it. This video is for those of you that have high aspirations for yourself. When deciding if data science is the right career choice, you don't want to be just a
media over data scientists. Or if you're ready to data scientists, what you can do to level up. So now without further ado, these are the five things that make a great data scientist. In my opinion, number one is growth mindset. Number two, unbiased curiosity. Number three, paranoia about the data. Number four, organization and number five, storytelling. And a bonus one, which is choosing the right domain. Now in my opinion, the most important trait that differentiates a great data scientist from an average one is a growth mindset. A growth mindset is when you believe that you're able to improve ever. You might not work really hard on it, but you know that you get there. Now contrast this with a fixed mindset where you believe that your skills and abilities are predetermined and set and stolen. This is just the way you are forever. Now a growth mindset is incredibly important because the speed on which a data science field is evolving is so incredibly fast. New data science technologies are literally being built faster than you possibly learned
though. And the standards of how to analyze data itself is shifting every day as well. When I was working in bi-comantics, one day everybody was, you know, happily working in R. And then it felt like suddenly it was all about Python because of the ML packages are just, they're just so much more powerful and easier to use. So guess what? I had to go and learn Python and the corresponding packages. Now, if I had a fixed mindset, it would be something like, oh no, I don't really know Python. I don't understand this documentation and syntax is so confusing. I must be too stupid. I should just give up. Now these days with new things popping up left, right, in the center, like auto ML, GPT-3, etc. You get what I'm coming from? Kind of hard to have a fixed mindset. What I like with my growth mindset though is something more like this. Oh no, I don't really know Python. I don't understand this documentation and the syntax is so confusing. I must be too stupid. So I need to get less stupid.
Yay. I'm not so syndrome as real life friends, but that's a top-rank for it all the time. Unbiased curiosity. You ever know some people that have really strong opinions about things without, you know, really thinking about it? Like for somebody that just truly believes that Tesla stock would skyrocket through a stock split and then continue to skyrocket again, which totally did happen. Or someone that just truly believes that coconut oil is a miracle drug and cure cancer in all diseases. Well, if you're either of these people, then data science is probably not for you. Let's deconstruct unbiased curiosity. Unbiased is when you go into something with an open mindset and perhaps then craft the story from the data. Yes, you should always have hypotheses to test that. But it's a big no-no to go on with a ready-formed opinion and just try to validate or support your opinion. Hey, your opinion can be very well, amazing and correct. Like our friend here that invested all their money and tasks when it came over that moment. But that wouldn't make you very good data scientists. Now data scientists need to be unbiased and with the data shaped their opinions.
Now the second component is curiosity. You have to be a curious person. The best data scientists that I've met are the ones that always ask themselves questions. Why is this venture going to be falling? What other products do our customers use? Why is mitochondria the powerhouse of the cell? No, that's just me. That led me two, four years and under the scientific science to answer that question. Well, when you get unbiased, plus curiosity, you know what that makes? A, hello good scientists. And what is the second part of this drop title? Oh, it's scientist. Judge, I asked my case. Paranoia. This was the one that was really surprising to me when I was not creepily observing great data scientists. I noticed a common trait among them is paranoia. Not that they think that the government is out to get them. I think not at least. Have you guys heard about sanity checks? It's when you write some code or some constraints and queries to make sure you haven't gone and invested in a company. The problem with data science is that even if you do something wrong, you'll still get results provided that you didn't make a syntax error. An example that I'm very embarrassed to say I have done is something like basically messing
up the denominator of a fraction that was trying to compute. I did, like, some instead of counting in SQL to be exact. And well, if I did a sanity check, I would have done a real realized that the number of distinct rows was completely off. But I did it. And this was extra embarrassing because a senior data scientist caught this for me. In fact, though, I was lucky that the data scientist would caught it for me. It can be very bad if you're not constantly checking yourself because some things are really hard to catch. And realistically, nobody's going to go through your code line by line to catch what you do. And then everything you do downstream is wrong. And then you present the wrong findings. And then people make the wrong decisions. So yes, great data scientists are always very paranoid and putting sanity checks everywhere. Organization of skills. Yes, this is by far my greatest weakness. But what I've observed in great data scientists is that they are very, very organized. They spend a lot of time thinking through their objectives, where hypotheses and write down the steps that they would take to accomplish them. And then actually follow through with it. This is incredibly important because it's incredibly easy to go into analysis paralysis
or if you're like me, get distracted by all the cool things you can do that you lose track of those important questions and objectives that initiated the project in the first place. A great data scientist. When you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job posts the boost it needs to be seen and helps reach people with the right skills, certifications and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this shelf will get a $75 sponsor job credit at Indeed.com slash podcast. That's Indeed.com slash podcast. Terms and conditions apply. Need a hiring hero? This is a job for Indeed Sponsored Jobs. Fall has never looked or tasted this good. Sweet Greens Fall Harvest Menu is back with seasonal favorites dressed to impress and made to be devoured. Warm roasted sweet potatoes, crisp apples, maple glazed Brussels and crave worthy flavors in the autumn harvest bowl, maple glazed salmon plate and roasted bacon Brussels side.
The season's most desirable menu has returned to sweet green. Featuring falls best dressed. Make your move. Order on the sweet green app. The best possible puts unlimited 5G data and hotspot in the palm of your hand. Powered by Verizon's 5G network. With no contract holding you back. And for a limited time, you can get visible for just $19 a month for 12 months when you use promo code SAVE6. All the features of Big Wireless Service. For half the cost. Tap the banner to switch today. Terms apply. Standard rate applies at month 13. See visible.com for plan features and network management details. Also, organized as their code. So this does not happen. Yeah, what is a stand for again? Storytelling. You spend a lot of time on a problem and you're really into it. You find some really cool results. We're building awesome models that solves like half the problems that people are always complaining about. So you know, you go and document everything and show it to people.
Super excited. But guess what? Nobody cares. Everybody is too busy working on their own things and making sure that their own deadlines are met. This is why Storytelling is so important. A great data scientist knows that the work is not done with their data or the technical stuff. They need to convey the awesomeness of their findings or what they made to non-technical people. This is usually business people or product managers. And you need to do it in a way that they can understand. Because you have to convince these people to actually use your work. It sounds crazy, right? So this is what I learned very quickly when I did my first analysis and I was super proud of it because it totally gave a team direction. I wanted to do for the next project and nobody cared. And it's totally my fault because it's not very hard to decipher some obscure legal analysis. I just popped into their inbox. So yes, Storytelling is very important. Both verbal and through writing and visuals. Bonus time. Great data scientists care about the business issues and their doing. This one they're making out to the core list because I believe this is something that is more about finding the right domain for you to do a data science as opposed to a skill
in itself. This is extremely important though because the data science role works very closely with business function and leadership. And those in business function that leadership I'm really concerned with while the business. So you use a data scientist. If you don't care about tech, you would hate being a data scientist because who cares when you get into services the team is building. And you know, if you don't like medicine, why would you even bother to think about how to improve it? Yeah, I just to find the right business domain for you, I think you've really got to put yourself out there. I don't mean that you have to go get an internship or a job in like different domains, which requires a lot of work and a lot of commitment. Instead, you can totally do projects about domains that you think you might be interested in. Resources like Kaggle have so many different datasets and the time commitment can be as long or as short as you like. While I hope you enjoyed this video and it's helped you move forward into signing data science right for you. Or what you can do to level up as a data scientist. And finally, don't forget to always minimize effort and max my dog. I'll see you guys in the next video. Fall has never looked or tasted this good.
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