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Learning about Data Science Careers: For Middle School, High School and other Kids

Updated: Apr 11, 2022

How can you prepare for a Data Science career? What kind of career is a Data Scientist?

Data Science (a part of Artificial Intelligence) is one of the fastest-growing careers in technology and the world in general. In this blog, we answer common questions about Data Science, and include a presentation by an amazing Data Scientist, Sarah Haq, from AIStars 2021 - where she discussed her work as a data scientist with elementary, middle and high school students!

First, answers to a few common questions:

What is Data Science?

Data Science is the area of Artificial Intelligence that focuses on understanding data, data patterns, and ways to get information and insights out of data. Data Science is used by companies in every industry to understand their customers, improve their products, become safe, more profitable, etc.

Is Data Science a good career?

Yes indeed! Data Science jobs are growing every day and data scientists can work in many fields since all fields benefit from a better understanding of data.

What jobs and careers can you get with Data Science?

Data Science is a career in itself. There are many jobs in different fields, but they all focus on making sense of data. For example - a data scientist who works for a shoe company may focus on understanding what features people want most in a shoe, while a data scientist working in a large food services company might try to understand what makes customers stay with the company or which customers are at risk of leaving.

Why should kids be interested in Data Science?

Data is all around us! With data science and AI, kids can build cool projects even before college. Data Science is also entering every field since everything benefits from data. So,no matter what field you go into in the future - what you learn about data will help you do better.

How can you learn more about Data Science jobs?

Watch the video below! Sarah has been a practicing data scientist for many years, and she shares her experiences, what made her want to be a data scientist, and why she loves her job.

Is Data Science a high-paying job?

The exact pay depends on where you work of course, but data science is considered a high-paying career, and the demand for data scientists is growing every day. It is normal in the United States, for example, for entry-level data scientists to make $100,000 per year or more.

How can Middle School and High School students prepare for data science careers?

Learn Artificial Intelligence and Data Science while in school. Even kids as young as elementary school can learn about AI and better understand the devices around them and how these devices use data. Middle School and High School students can build data science and AI projects for science fairs and other competitions. Watch Sarah’s video below to see how you can get started!

Sarah Haq shares her experiences



Sarah is a data scientist. And I'm not sure how many of you might know what that is, but I'm sure she'll be able to tell you and she's done all sorts of really cool data science stuff, as well as you know, done work with ethics and standards and things like that. And so she'll be talking to you about what some of those things are like.

Go ahead, Sarah.


So Hello, everybody. Um, I didn't prepare anything. It was just meant to be like a really informal chat with everyone. Like we just said this to tell everyone about the world of data science. And really, what is the application of AI? Those are really amazing workshops. I just saw, I don't know if everyone just wants to, like, grab a glass of water or just take like, a few, few minutes to just, yeah, take a break. So feel free to just quickly grab something. And then, yeah, I'll just resume in a minute or so. But if everyone Yeah, so

I think that was a pretty intense workshop. So it was really amazing to watch that in another couple of minutes, then Sarah? Yeah. Well, it looks like an amazing day, though. It looks like a really amazing day.


I think it's been really good. You know, we had a version of this session last night with the aisa teams, and I think they built like, 10 different websites.


Wow. Wow, that's amazing. That's amazing. Oh, where's everyone from? If anyone's online, they can unmute themselves. We're typing the chat. But yeah, Where's everyone attending from? And what brought you to this? How did you find out about this amazing seminar?

Well, my, my uncle enrolled me for this. And it's really I think this is really fun.

Oh, cool. Um, what do you been doing then? I like our response my house. What have you been doing for the last 24 hours?

Um, I've been learning about AI. So go on, why don't you tell me? What do you think AI is so that I know what you know already?

Oh, so far, we've learned how to build AI. Like, we've learned how to build one that detects like if it's a cat or a dog, or we've learned that one, too. It's called mood detector. And you can find out someone's mood by that one. Oh, cool. How did you find that? So there was like the there was a data set, and then we like, so for that. So what we did was that we inserted a data set. And then it showed like, if someone writes this sentence, that means they're sad. If someone wrote the sentence, that means they're happy. And then, yeah.

Oh, very cool. Well, that's basically my job in essence, so I can, yeah, if everyone's back, then I can start. You. Should I've just yeah, just begin then.


I'll start. Hello, everybody says we've been we've been introduced. I'm Sarah. And I'm a data scientist at a company called artsy. So I'm not sure if anyone's aware of artsy but artsy is, is really cool platform. And basically, it's a platform for galleries for artists, for users to purchase any sort of artwork from all across the world. So it's, yeah, we have an incredible amount of data, not just galleries, but also artworks. It's really interesting. And I've only been there for two months. So I'm still sort of letting the roll and finding my feet. Before that. Well, actually, before I start going into that, whatever you really liked to know from everybody is what sort of what sort of apps the people use? Was it a product that people use? And then I can sort of like get tell everyone a bit more about my background. But I feel free to just unmute yourself, just type in the chat. But yeah, what sort of apps are people addicted to? Real people use Instagram or tik tok. I don't know. What's cool nowadays?

Kids, you want to talk about what kind of apps you use? Maybe anything that you've already told you parents.

We use Instagram, tik tok and snapchat mainly.

Okay. And do you know where AI plays a role in those apps?

Like, well, I don't know, kind of everywhere, especially with the code part. I mean, without having like, those things like that the websites and apps wouldn't be able to like work. So.

Yeah, exactly. So the reason I also just put everything into a little bit of context, so a lot of companies use AI through people like myself. So it all starts with data, just like the project that you're working on where you were. Where are you, we're trying to identify people's mood, there's so much that we can identify with data. And that's what the role of a data scientist is, what exactly can you provide the user? So for example, everyone using Instagram out there, what sort of content you should see that's powered by AI? And that's built by data scientists like myself? If anyone uses Netflix, for example, what show should you be watching, that's something that's powered by AI. And again, it's built by a data scientist, companies are also interested and, for example, the chatbots that you're going to learn how to build or if you've already built them, companies are also interested in knowing what do they users look like, so that everyone is profiled into a group or a cluster. And that's an example of also how people are using data scientists AI. So I've worked in lots of different types of companies doing lots of different types of projects. And it all starts with data. And it always has assess use case. So I worked at gyms, where we were trying to understand how do we make sure the user like how do we make sure a customer comes back to the gym? What can look sort of cool ways? Could we use AI to identify risky, customers? And then how do we bring these customers back in? I've worked at news aggregator apt trying to cluster users into search in groups so we can provide them the greatest use that they could possibly read. I've worked at underwear companies, where we've also clustered users. Also, we design tights based on clustering. So we'll color tight should we have based on our users? So we had a range of colors of tights, which was based on an AI algorithm? There's lots of different ways that data scientists are utilized. And yeah, it's, it's really, there's infinite opportunities out there. And it sounds, it's they all are similar to this cat and dog problem that you're learning. We're trying to classify data into a bucket? Is that cats and dogs? Is it a risky user or not? Is it one of many colors or not? It's all just a classification problem. So what you're learning is really useful. And yeah, in the industry will come in really handy. But one thing, which I think is really important, and we don't talk about a lot, so we're building all these recommendation tools. And we're telling people, hey, you based on your behavior, you should listen to this music, or you should look at this Instagram content. But we're not really thinking about the social implications of the work that we're doing. So for example, a lot of time recommendations will only have it will be one gender over another. That's unfortunate, whereas all the great female artists, when it comes to Instagram, for example, are you as you providing content that people want to see? And how do we make sure that we're providing positive content? So there's a lot of that there's, for example, a lot of like, controversial content that's being shared, which is unfortunate. So there's using social and ethical concerns as well, that it's really important to talk about, and I feel like the new generation and yeah, definitely your generation cares more about these things, and the people that already built these things. So please do challenge. Yeah, do challenge all these products and think about how to use AI, how to use AI to do good and not just build something from an engineering point of view. So yeah, that's sort of my building I wanted to share about about social implications. But if anyone has any questions or anything about the industry, maybe I could answer that. Otherwise, yeah, I can go in a bit more detail about my experience.

Would anybody like to ask any questions about what in our data scientists like Sarah do in their jobs? You know, that might give you an idea of whether it's something that you might enjoy doing later in your life? anybody like to ask some questions about that?

I realized I forgot to mention sort of the coding languages I use. So we use a lot of Python in our day to day job. So it all starts with data. We use Python. That will be SQL and we use Python and then we use other languages to put these Things in production. But ultimately, Python is the main language. So I don't know if anyone's interested in learning Python or has already started learning Python.

So they will be having a Python session starting at I believe, 11, noon, or so.

Great. So they're really making art. We're making our young data scientists already.

So maybe I can ask some questions, which is, you know, kind of maybe, how did you get into data science?

Yeah, that's a really good question. I actually studied maths at university. And traditionally, maths have been more, you know, accounting type jobs or a finance job. So that was sort of the route that I was planning on taking. But I just realized I enjoyed coding so much, it just happened that I was working in insurance. And we were working a lot with Excel, and I just got really interested in coding. And then I started getting more information about machine learning and AI. And I was just like, wow, this world is amazing. And it requires such simple maths as well. So like, I don't know how much detail everyone is studying. Ai in but regression, for example, that's something that we all learn at school. And that's something that I find really interesting. And I thought it was like I had, it was my calling. And for the last five, six years, I've been working, I said, different companies trying to solve different challenges using data using AI. And it's Yeah, it seems never ending, which is, which is great.

Wonderful. And so one of the things that I thought was particularly cool was, you know, I don't know exactly what you do at artsy, but I've always loved AI and art, like an art. So can you tell us a little bit anything you've been, say publicly about? What is the intersection of AI and art that happens in your company?

Absolutely. So Right, right, now, let's just artsy, let's just treat it like any other company. And every company really benefits from, like I said, trying to identify what sort of risky users they have. So these sort of trend prediction models. But also, let's say someone is both research and piece of art. Now, art is such a personal thing, the reason why one of us likes our over another, it's very, very unique to arts a little bit more complicated. But the idea is that, okay, if you purchased x piece of art, or if you like such an artist, so for example, I really like female artists, and I really support female artists, or people who see my T shirt, but it's a it's a list of female artists I support. And the idea is that based on my behavior, and what's an art I like, or browse or purchase, what should be recommend somebody. So that's one of the ways we use it. But we're not using obviously, we're not using Oh, yeah, we're not trying to recreate art. We're not trying to use AI to create art. But we also do some price estimation work, which is really interesting. And there's different theories, like you would think that the art itself should be used to predict price. But actually, what we're finding is more numerical features, such as the size of the artwork has a higher correlation with the price. So that's, that's another example. But we have yet maybe 20 machine learning models right now in production, and wow. And yeah, we do. We do. We do. We do a lot, a lot of different types. But I would still say that every company would benefit from that. So for example, yeah, based on someone's behavior, what's your what's your recommend them next? But aren't like I said, it's so much more interesting, because how do you really model something that's so personal, so intuitive? So can AI really model art behavior? It's a really interesting question.

Absolutely not. And I think it's really cool. And one of the things that I think you you're kind of really pointing out is one thing I've noticed over the last few years, I don't believe we've we, as an industry or a planet have discovered something we cannot apply. enough that you know, I mean, it may be we don't know how to yet but we, you know, it's not like there's been anything, there's never going to be any AI in that. We always seem to find something that we can do. Yeah. So that's Yeah, that makes sense. So do you have any advice to kids who are maybe like, if they're looking to sort of explore these kinds of careers, you know, what are some of the things that they could do now to see whether or not these kinds of careers are right for them?

I mean, that's starting off at the right place, but during more than I was doing that at going to go into the AI club, learning how to code during, you know, I just watched people's participation in the workshop. It's so impressive and it starts starts with that. I think AI is not going anywhere, as we as you mentioned, and really the world is your oyster and it could apply to any field even if decide to become a doctor after all of this AI still applies in healthcare, if you decide to become a lawyer AI will still apply in law. So just having this as a fundamental or getting interested in this. It's just going to open up more doors. Yeah, start with Ai clubs start by Yeah, learning how to. Yeah, learning how to how to code. Trying to build your own chatbots for example, doing kaggle competitions. That's a starting point. Yeah, just really just Yeah. Have fun they codings are as well in a way. And

absolutely. No, thank you so much, Sara. We really, really appreciate it. And I think it's very inspiring for the students, you know, to hear I hope you enjoy what you're doing, and how passionate you are about it. You know, I appreciate that.

No, thank you so much. Thank you for thank you for inviting and yeah, good luck to the Yeah, good luck for the rest of the day. And yes, thank you. I hope to see everyone in the field very soon. Thank you so much, sir. Take care.

Sarah gave this talk at AIStars 2021.

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