Alter Everything Podcast

MaddieJ · ‎07-24-2020

In our last episode of the Data [in the] Sandbox mini-series, Maddie ponders what she’d like to be when she grows up. Now that Susan has explained analytics and data, Maddie wants a job where she can solve puzzles using data! But, what kind of jobs deal with data and analytics? Luckily, Susan knows all about this, and shares her knowledge so Maddie can become an analytics mastermind!

Panelists

Maddie Johannsen - @MaddieJ, LinkedIn, Twitter

Susan Sivek - @SusanCS, LinkedIn, Twitter

Topics

Transcript

Episode Transcription

MADDIE: 00:03	[music] Welcome back to Data [in the] Sandbox. I'm Maddie Johannsen. And you know when your teacher or your parents or your grandparents ask you, "What do you want to be when you grow up?" And I'm always so sure of myself when my answer is, "How am I supposed to know? There's so many things I love, like art and math and science and running. And now, after talking to Susan for these past few episodes, I'm super interested in data and analytics too." [music] But what kind of jobs are in data? Would I just be a data person? Data person doesn't sound quite right. But thankfully, Susan is still here to teach me about all this stuff. So come with me. Let's go ask her and find out. [music]
MADDIE: 00:58	Okay. So now I'm pretty excited about all this stuff I can do with data.
SUSAN: 01:02	Yeah. Totally. You have a plan to become the fastest runner in school. You don't have to be scared of the sharks stealing your ice cream. You can sort of predict the future, and you can make a real treasure map.
MADDIE: 01:15	That is amazing. And I know what variables are and correlation and spatial analytics.
SUSAN: 01:22	Indeed. Those are all great things to know about.
MADDIE: 01:24	So my real question now, a very serious question, how do I make money with this stuff? And not just video game gold coins, because it's all pretty neat.
SUSAN: 01:34	Yeah. It is. The awesome thing about this is you can take all the things you know about data and use them in all kinds of areas. So basically, you can use the same knowledge and the same skills in lots of different businesses and professions. Wherever you work, you're just always gathering information. You're analyzing it. And you're coming up with important conclusions and ideas that you can share with other people.
MADDIE: 01:57	But what kinds of information? Sharks and stuff are fun, but will anyone pay me for that?
SUSAN: 02:03	Well, let's think about it a little more. So here's an example. [music] Imagine your giant bucket of Legos at home.
MADDIE: 02:12	Yes. Legos.
SUSAN: 02:15	Yeah. So how many Legos do you have? And how long would it take you to sort them all out by different colors or different shapes and sizes?
MADDIE: 02:23	A long time.
SUSAN: 02:26	Yeah. There's a lot of them. And then what if I said, "Now I want you to figure out what's the tallest possible tower you can build using those Legos," or, "What's the tallest tower you could build using only the blue and red Legos?" or, "How much of the floor of your bedroom could you cover with Legos, like a multicolor Lego rug that would hurt a whole lot to walk on?"
MADDIE: 02:50	Oof. Ouch. Yeah. That's not a good idea. Well, any of those things would take me a long time. And I'd have to make a lot of different towers and measure them all and destroy them and try again.
SUSAN: 03:01	Yeah. Exactly. It's super time-consuming. But what if you treated those Legos like data and you said, "Okay. I'll measure one of each of the different types of Legos. And I'll plug all that data into some math. And then I'll let the computer figure out what the tallest tower or the biggest painful Lego rug could be"?
MADDIE: 03:23	That sounds like it could be faster. Though I wouldn't get to destroy all those towers, because that's the most fun part.
SUSAN: 03:31	True. True. You do miss out on that. But I bet you could pretty quickly use some neat software or code to figure out the strategy for the tallest tower.
MADDIE: 03:40	That sounds great. I'm going to build a 20-foot tall tower.
SUSAN: 03:44	Oh, my. Wow. Well, that would be amazing. And here's the neat thing. So we don't build real buildings with Legos, but there are lots of situations where people use data to figure out how to make the most of something that they have. So we only have this amount of money to spend on advertising our business. Should we put our ads on TV or in a video game? Or maybe we have 5,000 students at our college. How do we make sure there's enough classes for them all to take of the right kinds and at the right times? Or maybe we know people have only so much free time to watch movies. So how do we show them one movie that they will for sure like?
MADDIE: 04:27	Those all seem like really different questions. Ads or movies or students.
SUSAN: 04:32	Yeah. They're different areas for sure, but they're all things that someone who works with data - so you might call them a data analyst or a data scientist - they work every day to figure those kinds of things out. So like you with your Legos, they get some information, like measuring those Legos, and they come up with some math that helps them solve that problem, like what's the tallest tower I can build?
MADDIE: 04:55	The math is like the models we talked about before, right?
SUSAN: 04:59	Yeah. Exactly. So it's all these different ways of combining the variables that they have, all the different pieces of data, and then they crunch the numbers to come up with some good answers.
MADDIE: 05:11	[music] Are they all mathematicians though? Super experts at math who can do all this fancy stuff with numbers and weird symbols written all over the walls of their offices like in the movies? [laughter]
SUSAN: 05:22	Well, some of them are definitely experts. There are some data analysts and data scientists who are really deeply knowledgeable about math. But there's also people who use different kinds of computer software to solve these problems. And they don't have to know every little bit of math that might be going on. They have to understand more or less how their analysis and their models work, but they don't really have to be able to do every single bit of the math behind it with pencil and paper.
MADDIE: 05:51	That's cool. So data analysts are, basically, figuring out different puzzles all the time with data? Because if they get to work on Legos and movies and stuff, I'm ready to sign up.
SUSAN: 06:02	Yeah. Yeah. Some folks work on those things. But if you're really into the puzzle-solving and figuring out complicated problems, [music] you'd probably find doing data stuff fun even if your focus wasn't necessarily Legos or movies. It's kind of like a workout for your brain. You're just coming up with creative ways to get information and analyze it.
MADDIE: 06:26	I don't know. That sounds neat, but would I just be sitting in an office all by myself with a bunch of numbers?
SUSAN: 06:31	Oh, no. Not necessarily. You'd probably work with a team of people who try to figure out the same problem. And maybe they all have different perspectives from the organization that you work for. So sometimes, you'd work on your own and spend some time really digging into problems and figuring stuff out, but other times, you'd talk with others and share what you figured out. You'd work with other people to help make decisions, and sometimes really big decisions, about what your company or your organization should do.
MADDIE: 07:00	Yeah. That sounds important.
SUSAN: 07:02	Yeah. Yeah. Data is super important. So people who can understand it and offer some insights into it are super important too.
MADDIE: 07:10	So all right. You talked about data analysts. What's a data scientist then?
SUSAN: 07:14	Well, this gets a little confusing. And the truth is, there really aren't clear definitions. Sometimes, what data analysts and scientists do can be very similar depending on where they work. [music] Some data scientists might use a little more code, like computer programming, than analysts to do their data analysis, to build models, but not always. They might use some different technology in their work. And they might have a bit more knowledge in math and statistics and maybe computer science too. And like your science experiments at school, they might even design experiments to gather certain kinds of data that they need. But again, the names of the jobs and what those people do can vary a lot.
MADDIE: 08:04	Interesting. So sort of the same but also a little different. This is sounding pretty good so far. What else do they have to know?
SUSAN: 08:12	Well, good. I'm glad you like it. So in terms of other things they should know, well, data analysts and data scientists have to share their work with other people. So yeah. You don't just crunch a bunch of numbers and say, "Okay. I'm done," and just go home at the end of the day. It's really important to be able to talk to people about data in a way that's easy for them to understand. You would probably also make data visualizations, like the heat map or the treasure map we talked about last time, to help people get the points that you want to make. You might think that data is really interesting and your analysis makes perfect sense to you, but if you can't communicate about it by talking and writing clearly, other people aren't going to benefit from all your cool ideas.
MADDIE: 08:55	Well, I definitely want other people to enjoy all my cool ideas. But it also seems like people will think numbers are scary sometimes.
SUSAN: 09:02	Yeah. That's true. Not everyone is as into data as you are. Some people can find data a little intimidating and scary, but you can guide them through it if you work on learning to explain things clearly and to tell them a story about the patterns you're seeing in the data.
MADDIE: 09:19	That makes sense. Use your words, like my parents always used to tell me.
SUSAN: 09:23	Yeah. Exactly. And I'd add the knowing about how business operates and about technology and what's going on in the world. All those are important, too, so you can understand your data and the patterns that might be in there. And you want to get a sense of how your data might be affected by stuff that's happening. So if you see in the news that a hurricane is coming, that can really affect how you use your data to plan ahead or when you analyze your data after the storm. Or maybe there's a new law that gets passed that affects something related to your company. It might not seem like that's relevant to the data, but it totally might affect things that you don't initially think of. So you do have to keep up with world events a bit.
MADDIE: 10:04	Sure. That's a good idea anyway. So if I wanted to describe what my mom does at work to my friends, I could say, "She analyzes data to help find patterns and solve problems"?
SUSAN: 10:15	Yeah. That's a really nice, short way of saying it. It's definitely a bit more complicated, and there's variations at different workplaces, but that's a really good start.
MADDIE: 10:25	No kidding it's complicated. It took us five podcast episodes to get here.
SUSAN: 10:29	Yeah. But look at all we've talked about. You learned what data is. You learned what variables are and how correlation and causation are not the same thing. We learned about how to make predictions within reason and how maps and data visualization is super helpful. And plus, you have a whole new idea for a possible career.
MADDIE: 10:48	And I also learned how to use data to get all these other things done in my life. So that's pretty neat. Track trophies and ice creams and gold coins in my game.
SUSAN: 10:57	Yeah. Awesome. Yeah. Having these data skills will help you in all kinds of areas of life. That's totally for sure. So see how many things in your everyday life you can think about as data that could be analyzed or that you could make predictions about. And, of course, there's lots more ways to learn about data and analytics on the Alteryx Community. [music] We have a whole new set of learning resources just for kids. So please be sure to check that out.
MADDIE: 11:20	Awesome. Thanks for listening to Data [in the] Sandbox. This miniseries was written by Susan Currie Sivek, and our theme music is by Andy Uttley. If you know a K-12 educator or student or are one yourself, we're excited to offer a new learning and certification program designed for kids and young adults. To sign up or learn more, visit alteryx.com/forgood. That's alteryx.com/F-O-R-G-O-O-D. Catch you next time. [music]

MADDIE: 00:03 [music] Welcome back to Data [in the] Sandbox. I'm Maddie Johannsen. And you know when your teacher or your parents or your grandparents ask you, "What do you want to be when you grow up?" And I'm always so sure of myself when my answer is, "How am I supposed to know? There's so many things I love, like art and math and science and running. And now, after talking to Susan for these past few episodes, I'm super interested in data and analytics too." [music] But what kind of jobs are in data? Would I just be a data person? Data person doesn't sound quite right. But thankfully, Susan is still here to teach me about all this stuff. So come with me. Let's go ask her and find out. [music] MADDIE: 00:58 Okay. So now I'm pretty excited about all this stuff I can do with data. SUSAN: 01:02 Yeah. Totally. You have a plan to become the fastest runner in school. You don't have to be scared of the sharks stealing your ice cream. You can sort of predict the future, and you can make a real treasure map. MADDIE: 01:15 That is amazing. And I know what variables are and correlation and spatial analytics. SUSAN: 01:22 Indeed. Those are all great things to know about. MADDIE: 01:24 So my real question now, a very serious question, how do I make money with this stuff? And not just video game gold coins, because it's all pretty neat. SUSAN: 01:34 Yeah. It is. The awesome thing about this is you can take all the things you know about data and use them in all kinds of areas. So basically, you can use the same knowledge and the same skills in lots of different businesses and professions. Wherever you work, you're just always gathering information. You're analyzing it. And you're coming up with important conclusions and ideas that you can share with other people. MADDIE: 01:57 But what kinds of information? Sharks and stuff are fun, but will anyone pay me for that? SUSAN: 02:03 Well, let's think about it a little more. So here's an example. [music] Imagine your giant bucket of Legos at home. MADDIE: 02:12 Yes. Legos. SUSAN: 02:15 Yeah. So how many Legos do you have? And how long would it take you to sort them all out by different colors or different shapes and sizes? MADDIE: 02:23 A long time. SUSAN: 02:26 Yeah. There's a lot of them. And then what if I said, "Now I want you to figure out what's the tallest possible tower you can build using those Legos," or, "What's the tallest tower you could build using only the blue and red Legos?" or, "How much of the floor of your bedroom could you cover with Legos, like a multicolor Lego rug that would hurt a whole lot to walk on?" MADDIE: 02:50 Oof. Ouch. Yeah. That's not a good idea. Well, any of those things would take me a long time. And I'd have to make a lot of different towers and measure them all and destroy them and try again. SUSAN: 03:01 Yeah. Exactly. It's super time-consuming. But what if you treated those Legos like data and you said, "Okay. I'll measure one of each of the different types of Legos. And I'll plug all that data into some math. And then I'll let the computer figure out what the tallest tower or the biggest painful Lego rug could be"? MADDIE: 03:23 That sounds like it could be faster. Though I wouldn't get to destroy all those towers, because that's the most fun part. SUSAN: 03:31 True. True. You do miss out on that. But I bet you could pretty quickly use some neat software or code to figure out the strategy for the tallest tower. MADDIE: 03:40 That sounds great. I'm going to build a 20-foot tall tower. SUSAN: 03:44 Oh, my. Wow. Well, that would be amazing. And here's the neat thing. So we don't build real buildings with Legos, but there are lots of situations where people use data to figure out how to make the most of something that they have. So we only have this amount of money to spend on advertising our business. Should we put our ads on TV or in a video game? Or maybe we have 5,000 students at our college. How do we make sure there's enough classes for them all to take of the right kinds and at the right times? Or maybe we know people have only so much free time to watch movies. So how do we show them one movie that they will for sure like? MADDIE: 04:27 Those all seem like really different questions. Ads or movies or students. SUSAN: 04:32 Yeah. They're different areas for sure, but they're all things that someone who works with data - so you might call them a data analyst or a data scientist - they work every day to figure those kinds of things out. So like you with your Legos, they get some information, like measuring those Legos, and they come up with some math that helps them solve that problem, like what's the tallest tower I can build? MADDIE: 04:55 The math is like the models we talked about before, right? SUSAN: 04:59 Yeah. Exactly. So it's all these different ways of combining the variables that they have, all the different pieces of data, and then they crunch the numbers to come up with some good answers. MADDIE: 05:11 [music] Are they all mathematicians though? Super experts at math who can do all this fancy stuff with numbers and weird symbols written all over the walls of their offices like in the movies? [laughter] SUSAN: 05:22 Well, some of them are definitely experts. There are some data analysts and data scientists who are really deeply knowledgeable about math. But there's also people who use different kinds of computer software to solve these problems. And they don't have to know every little bit of math that might be going on. They have to understand more or less how their analysis and their models work, but they don't really have to be able to do every single bit of the math behind it with pencil and paper. MADDIE: 05:51 That's cool. So data analysts are, basically, figuring out different puzzles all the time with data? Because if they get to work on Legos and movies and stuff, I'm ready to sign up. SUSAN: 06:02 Yeah. Yeah. Some folks work on those things. But if you're really into the puzzle-solving and figuring out complicated problems, [music] you'd probably find doing data stuff fun even if your focus wasn't necessarily Legos or movies. It's kind of like a workout for your brain. You're just coming up with creative ways to get information and analyze it. MADDIE: 06:26 I don't know. That sounds neat, but would I just be sitting in an office all by myself with a bunch of numbers? SUSAN: 06:31 Oh, no. Not necessarily. You'd probably work with a team of people who try to figure out the same problem. And maybe they all have different perspectives from the organization that you work for. So sometimes, you'd work on your own and spend some time really digging into problems and figuring stuff out, but other times, you'd talk with others and share what you figured out. You'd work with other people to help make decisions, and sometimes really big decisions, about what your company or your organization should do. MADDIE: 07:00 Yeah. That sounds important. SUSAN: 07:02 Yeah. Yeah. Data is super important. So people who can understand it and offer some insights into it are super important too. MADDIE: 07:10 So all right. You talked about data analysts. What's a data scientist then? SUSAN: 07:14 Well, this gets a little confusing. And the truth is, there really aren't clear definitions. Sometimes, what data analysts and scientists do can be very similar depending on where they work. [music] Some data scientists might use a little more code, like computer programming, than analysts to do their data analysis, to build models, but not always. They might use some different technology in their work. And they might have a bit more knowledge in math and statistics and maybe computer science too. And like your science experiments at school, they might even design experiments to gather certain kinds of data that they need. But again, the names of the jobs and what those people do can vary a lot. MADDIE: 08:04 Interesting. So sort of the same but also a little different. This is sounding pretty good so far. What else do they have to know? SUSAN: 08:12 Well, good. I'm glad you like it. So in terms of other things they should know, well, data analysts and data scientists have to share their work with other people. So yeah. You don't just crunch a bunch of numbers and say, "Okay. I'm done," and just go home at the end of the day. It's really important to be able to talk to people about data in a way that's easy for them to understand. You would probably also make data visualizations, like the heat map or the treasure map we talked about last time, to help people get the points that you want to make. You might think that data is really interesting and your analysis makes perfect sense to you, but if you can't communicate about it by talking and writing clearly, other people aren't going to benefit from all your cool ideas. MADDIE: 08:55 Well, I definitely want other people to enjoy all my cool ideas. But it also seems like people will think numbers are scary sometimes. SUSAN: 09:02 Yeah. That's true. Not everyone is as into data as you are. Some people can find data a little intimidating and scary, but you can guide them through it if you work on learning to explain things clearly and to tell them a story about the patterns you're seeing in the data. MADDIE: 09:19 That makes sense. Use your words, like my parents always used to tell me. SUSAN: 09:23 Yeah. Exactly. And I'd add the knowing about how business operates and about technology and what's going on in the world. All those are important, too, so you can understand your data and the patterns that might be in there. And you want to get a sense of how your data might be affected by stuff that's happening. So if you see in the news that a hurricane is coming, that can really affect how you use your data to plan ahead or when you analyze your data after the storm. Or maybe there's a new law that gets passed that affects something related to your company. It might not seem like that's relevant to the data, but it totally might affect things that you don't initially think of. So you do have to keep up with world events a bit. MADDIE: 10:04 Sure. That's a good idea anyway. So if I wanted to describe what my mom does at work to my friends, I could say, "She analyzes data to help find patterns and solve problems"? SUSAN: 10:15 Yeah. That's a really nice, short way of saying it. It's definitely a bit more complicated, and there's variations at different workplaces, but that's a really good start. MADDIE: 10:25 No kidding it's complicated. It took us five podcast episodes to get here. SUSAN: 10:29 Yeah. But look at all we've talked about. You learned what data is. You learned what variables are and how correlation and causation are not the same thing. We learned about how to make predictions within reason and how maps and data visualization is super helpful. And plus, you have a whole new idea for a possible career. MADDIE: 10:48 And I also learned how to use data to get all these other things done in my life. So that's pretty neat. Track trophies and ice creams and gold coins in my game. SUSAN: 10:57 Yeah. Awesome. Yeah. Having these data skills will help you in all kinds of areas of life. That's totally for sure. So see how many things in your everyday life you can think about as data that could be analyzed or that you could make predictions about. And, of course, there's lots more ways to learn about data and analytics on the Alteryx Community. [music] We have a whole new set of learning resources just for kids. So please be sure to check that out. MADDIE: 11:20 Awesome. Thanks for listening to Data [in the] Sandbox. This miniseries was written by Susan Currie Sivek, and our theme music is by Andy Uttley. If you know a K-12 educator or student or are one yourself, we're excited to offer a new learning and certification program designed for kids and young adults. To sign up or learn more, visit alteryx.com/forgood. That's alteryx.com/F-O-R-G-O-O-D. Catch you next time. [music]

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ) and @TreyW.
Special thanks to @SusanCS for writing this episode, @andyuttley for the theme music track, and @jeho for our album artwork.

Alter Everything Podcast

Podcast Guide

Data [in the] Sandbox Episode 5: When I grow up, I want to be an analytics mastermind!

Panelists

Topics

Transcript

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ) and @TreyW.
Special thanks to @SusanCS for writing this episode, @andyuttley for the theme music track, and @jeho for our album artwork.

Alter Everything Podcast

Podcast Guide

Data [in the] Sandbox Episode 5: When I grow up, I want to be an analytics mastermind!

Panelists

Topics

Transcript

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ) and @TreyW.Special thanks to @SusanCS for writing this episode, @andyuttley for the theme music track, and @jeho for our album artwork.

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ) and @TreyW.
Special thanks to @SusanCS for writing this episode, @andyuttley for the theme music track, and @jeho for our album artwork.