S4E3: Learning Data Science as a Beginner With Alice Zhao

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Alice Zhao is a senior data scientist at Metis, where she teaches 12-week data science bootcamps around the country, from Seattle to Chicago. During her free time, Alice writes about analytics and pop culture on her blog, A Dash of Data.

Although data science is the focus of her professional life now, Alice didn’t get her start in data or computer science at all. After earning her bachelor’s degree in electrical engineering at Northwestern, she realized that her interests didn’t lie in that field. When data analytics caught her attention, she returned to Northwestern to get her M.S. in Analytics–and is now passionate about passing those skills to others.

In our conversation, Alice shares what earning a master’s degree in data analytics entails, how data can be used to tell stories and uncover fun connections, and where beginners should start when they’re first looking to enter the field of data science.

This episode was transcribed with the help of an AI transcription tool. Please forgive any typos.

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In today's episode, I talk with Alice Zhao, a senior data scientist at Metis. We talk about her background and analytics, a blog article of hers that went viral, and her advice for anyone who wants to get into data science. If you enjoy today's episode, and inspires you to improve your spreadsheet skills so that you can then get into data science you might want to know about Ben Collins. You'll hear more about his courses in Episode Five. But just quickly, I want to tell you that as a Learn to Code With Me listener, you can get 20% off his Google Sheets training bundle. If you want to improve your spreadsheet skills, just go through my affiliate link, learntocodewith.me/sheet To find out more about his courses and get that special 20% discount, again, the URL is learntocodewith.me/sheets. Now back to today's interview. Alice Zhao is a senior data scientist at Metis. She has a Bachelor's in electrical engineering and an MS in analytics, both from Northwestern University. During her free time Alice writes about analytics and pop culture on her blog, a dash of data.

Laurence Bradford 2:32
Hey, Alice, thank you so much for coming on the show.

Laurence Bradford 2:34
Thanks for having me. Is there anything else you'd like to add that intro before we jump into the actual interview?

Alice Zhao 2:40
No. I'm ready to jump right in.

Laurence Bradford 2:42
Whoo. Okay, awesome. So you were academically trained in data science more or less? Right? You have a master's in analytics, which sounds super cool. So I'm wondering how did you end up going to school for that for like analytics?

Alice Zhao 2:56
Oh, man. It's a really long story. So I actually So I started in my undergrad in electrical engineering, and I, but by the time I was a senior, I decided I didn't want to do electrical engineering anymore. And so I actually went to the Career Center at Northwestern. And they had me take a Myers Briggs test. And I ended up being an INFJ, which they said was perfect for consulting. And so at that point, I was like, I don't know what a consultant is. But let me try to apply to consulting jobs. And so then I ended up being consultant at Accenture for four years. And then after being consultant, I felt that the job wasn't technical enough for me. And so then I started looking for other options out there. And I read this article about target creating predictions on which of their customers were pregnant, and then serving them specific coupons. And I thought, Oh, man, that sounds so cool. And so then I learned that that type of analysis This for that field was called analytics. And then that's how I decided to get into analytics.

Laurence Bradford 4:06
That's such a wild story. And real quick, I feel like I know what article you're talking about. And wasn't that with the target thing was something where they were like figuring out women were pregnant based on their behaviors before the women themselves knew?

Alice Zhao 4:19
Yeah, in that article, there was a crazy story because this one dad came into the store, and he was really upset that his teenage daughter was getting these ads for baby products. And then it turned out that his daughter was pregnant and he had no idea but target knew because of their algorithms.

Laurence Bradford 4:37
Oh my gosh, okay, maybe that was it. Yeah. I remember some story or something with that. Like, yeah, something like that. So that's, that's really wild. Oh, my God. And this was something they were mailing home.

Alice Zhao 4:47
Yeah, it was. It was crazy. I mean, at the time, I thought it was crazy. But then I end up getting into data science, and now I have a better understanding of how that works, though. It doesn't seem as crazy though.

Laurence Bradford 4:57
Got it. So okay. So you you went to Back to Northwestern, you study analytics. And you don't have to get super in depth here. But I'm just wondering, like, what kind of classes were you taking? What technologies were you learning? What were they teaching you?

Alice Zhao 5:11
Mm hmm. And so within analytics, you typically think of it in three parts. So there's a coding aspect or a programming aspect. Then there's a math and statistics part. And then there's this business and communications part. And so our classes were split into those three groups. So on the coding side, we learned R and Python, and Java. And then on the math and stats side, we learned predictive analytic, then data mining. And then on the communication side, we learned about data visualizations, and then we took some MBA classes as well. So it was a good mix of all three of those things. Mm hmm. And that was about two years. So that master's degree was 15 months.

Laurence Bradford 5:55
Okay, nice. And then I'm peering over your LinkedIn a little bit but when you are done With this master's program, did you go into data science right away?

Alice Zhao 6:04
I did. So when I did the master's program that was back in 2012. And at the time, data science wasn't really popular yet. So as we were doing the data science program in 2012, Harvard Business Review came out with that article about data science being the sexiest job of the 21st century. And then after that, just there was a ton of interest in data science. And so when I graduated from the program, there were a lot of companies interested in hiring data scientists. And then I ended up joining cars.com right afterwards and starting up their data science team.

Laurence Bradford 6:39
Yes, I definitely know what article you're talking about in HBr. I feel like it's still referenced in mentioned all the time. But yeah, as you said, it's when they call data science this next week, data science, the sexiest career of the century. That was it, right?

Alice Zhao 6:53
Something like that. And then I just remember it was, it was just a hot term like before then when I went into my program, it was called a master's in analytics. And so then I just thought, Okay, I'm going into analytics, but then afterwards data science became such a hot term that that's kind of what the careers that people landed into after the program.

Laurence Bradford 7:15
Yeah, yeah. And what I mean what? Like, I was like good luck but it's like right when you're graduating like this article from HBr comes out right like sexy, his career center, you must have had tons of demand like you and your fellow on like, the people you're graduating, graduating with, right?

Alice Zhao 7:31
Yeah, absolutely. I felt very fortunate to have just kind of fallen into this field all because I read that target article.

Laurence Bradford 7:38
Yeah, that's, that's really funny. And also really awesome at the same time. And of course, you're still doing it to this day, and now you're teaching others so obviously really worked out for you and something you really enjoy. And you mentioned target. You mentioned the article you came across and you have this blog. I mentioned it in the intro, but it's called a dash of data and I was looking at it a little bit before we hopped on the call. Today, what led you to start this blog?

Alice Zhao 8:03
So basically, I, oh, okay, the whole reason I started this blog was because I really love this show, The Bachelor, and after my master's program, because I knew how to do predictive analytics and make predictions, I just always thought, you know what, I bet I can create a predictive model to figure out who's gonna win The Bachelor. And so I decided to start a blog. So I can write about just that. And then when I started the blog, before writing my article about the bachelor I, it was around my husband and my anniversary, and so I decided I wanted to give him some type of nerdy gift. And so I ended up just exploiting all the text messages that we had sent each other over the years and doing some analysis on that. And that was my first blog post and then from then on, I've done additional analysis like the bachelor as well, but it ended up just being did analysis on fun topics in my life.

Laurence Bradford 9:04
Yes, I love that. And I was looking at some of them. And I know exactly which article you're talking about the one with how text messages change from dating to marriage. And that was the first one you wrote. And I feel like it was quite popular when it first came out. Right? You have a ton of comments on it. There's like, I think like over 100 comments left on this single blog post is from back in 2014. How did people find out about it? Like, were they just googling like text messages from, you know, dating to marriage? Or did it was it posted somewhere and it kind of went viral?

Alice Zhao 9:36
Yeah. So I actually ended up doing a whole presentation at work about how that post went viral. But it all started just from me sharing it on Facebook. And then I guess a lot of people related to it. And so they ended up sharing it even more, and then it got posted on Reddit, and it got uploaded a bunch of times onto the front page of Reddit. And so at that point, I had radio shows calling me and different shows like today's show wanted to do a bit on it. And they're just a lot of interest around it. And so and then I think it ended up getting translated into a bunch of different languages as well. And so my parents who lived in Shanghai at the time, they actually read a Chinese version of the article.

Laurence Bradford 10:22
Oh my goodness. That's amazing, though. That's like, that's like insane that you're, I mean, that's so rare that your first blog post would be such a hit right? You're getting like, calls to do intelligent shows and your family and Shanghai's reading a translated version. That's absolutely wild. And that's so, so amazing, but it makes a ton of sense to me. Because I mean, just the title alone, like how text messages change from day to marriage, and the fact that you found all this data yourself, and you studied it and you you analyze it, it's really intriguing. So could you give like a gist of like how text messages change, I'm sure you're tired talking about it. But just for the listeners who We're listening right now maybe they're driving, you're multitasking, they can't run over to your site and look at it. What were some of the things that you found?

Alice Zhao 11:06
Sure. So my main finding was that when my husband and I were dating, we would text each other "Hey" a lot. So H-E-Y. We would be like, Hey, how's it going? Or we also said each other's names a lot. Or we would say, I love you a lot and things like that. And then once we got married, we would say things like, okay, because it was our most common word. So we ended up saying, okay, a lot in our text messages, or what's for dinner? Are you home yet, and the text ended up being more boring. But then my final conclusion was that even though that the text messages became more boring, it was only because all of the interesting things were said in person, because we would see each other all the time so we didn't really have to say like, I love you, over text, we could just say that in person and ended up having kind of a sweet ending. At the end of the post, because yeah, even though our text messages were kind of mundane, all the things we're sending person.

Laurence Bradford 12:08
Yeah, yeah, that definitely makes a lot of sense. So I'm wondering how has your blog and then the related press and other just blog activities? How has that affected your career?

Alice Zhao 12:19
Oh, that's a great question. I, I definitely think it's helped my career. So when I had applied to my current job at Metis, as a data science instructor, they had read my blog as well. And they thought it was a great way of making data science understandable for everyone. And so I think that's definitely helped me in this next phase of my career. And one of the other things that they do at Metis is for all the senior data scientists, all the senior instructors, they have everyone work on a passion project for one quarter of the year, and I was interested in doing some type of public Culture and analytics projects, my passion project. And they thought that my blog was a great tie in to that. So, yeah, I definitely think it's helped my career in that I'm able to showcase that I'm really interested in data science and making it fun and engaging. And that can apply to data science education as well.

Laurence Bradford 13:20
Yeah. And you just perfectly segwayed into the next thing I want to talk about because of course, you were a data scientist at cars calm after you left Northwestern with the Master's in analytics. And now, most recently, as you already said, you're a senior data scientist at Metis. And you're teaching so what drove you to make that transition from working like in the industry, and then kind of switching into helping others get into the data science industry become data scientists themselves?

Alice Zhao 13:53
Yeah, so I loved teaching my whole life. So in high school, I was teaching math and science. camps during the summer in college it courses. And then after I got into data science when I was at cars calm, I was teaching some of my co workers about SQL and then also are one of the data science tools that we use, just because I was the first data scientists there. And then a lot of people were interested in learning more from me. And so I would hold these sessions on Friday afternoons to teach people on the basics of data science. And then, so my coworkers thought that was pretty great at teaching it. And so I thought to myself, why not make this into an actual business? So while I was at cars, calm on the side, I started a company with two of my friends my master's program, where we taught a two day data science workshop at 1871 in Chicago, so the startup incubator over there and I ended up teaching with those two of my classmates For two years, and we taught data science on the side, on the weekends, and that was really fun for a while. But then two of us ended up having kids in the last few years. And so we decided we can't do that part time anymore. So we retired that. And then this past year, medics reached out to me, and they asked if I want to teach full time, and I love teaching, and so it was the perfect job for me.

Laurence Bradford 15:23
Oh, wow. So I did not realize that you were teaching data science on the side before starting at Metis. And you had your own little business or side hustle going on, and you're doing weekend workshops.

Alice Zhao 15:34
Yeah, we were teaching these two day weekend workshops where we would teach two full days, so 16 hours of content, and it was an intro to data science in our course. And we had a lot of people come in who had heard about the term data science, but they didn't know really what it was. And so we gave them a taste of it over the weekend. Right. And that was all done in person. Yep. Yeah, cuz at the time, there were a lot of online courses. We is great. I learned a lot of things online as well. But I found that a lot of people who are signing up for our courses, they wanted that classroom feel. And so we had two days in the classroom, three instructors, and we'd have two dozen students for each class, and then we could help everyone with their code in person.

Laurence Bradford 16:19
Wonderful. So you, you've been at Metis. Now you're teaching full time, how has that been going? How has been like the switch from working full time to being in a classroom.

Alice Zhao 16:29
I love it. I love teaching. I always. I always tell people like this is my dream job. This is actually what I wanted to do. Later in my career, I thought I had to be a data scientist for a really long time and then maybe move to a university. But then Metis found me and I love teaching. And so the way that our strip schedule works here is we start the day with a pair programming problem where the students code in pairs, which I think is a great way to learn how to code and then me and then a co instructor of mine, the two have us lecture for two, maybe three hours. And then the rest of the day is spent on project work. And so basically my job is to help students with their projects. So they'll come to me We'll brainstorm project ideas together, or they might have some questions about modeling or the code. And I have this whiteboard that I roll around the classroom, I basically roll it around and then I help them figure out what what they need to fix in their code or their models. And that's what we spend the whole second half of the day doing. So I really like it because I'm, I never sit in the same seat for more than two hours. I'm just constantly moving. That's a lot of fun.

Laurence Bradford 17:38
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Laurence Bradford 19:36
That's awesome. And correct me if I'm wrong, but the Metis program is strictly for beginners, right?

Alice Zhao 19:42
So to get into the Metis program, we expect you to know basic Python skills to have basic Python skills and then also to know basic linear algebra, calculus and staff. So with all those kind of basic math and coding skills in place, then we believe on top of that in the program.

Laurence Bradford 20:01
Got it. Okay, so pretty basic, not like a PhD in statistics or something?

Alice Zhao 20:07
Oh, no.

Laurence Bradford 20:09
Because I know there are a few data science boot camps that require they have a pretty high bar for what your background has to be, and especially in the world of academia. But any case, okay, so pretty, pretty much beginner level, you said there are some requirements, but basic Python, basic linear algebra and stats. So when a student is moving through the program, and when you guys first start off, what is the first topic or area that you that you get into a teach?

Alice Zhao 20:36
So the first week of the bootcamp is spent on exploratory data analysis? So the first week we, I think, on day one, yeah, day one, we give this students this huge messy data set. And their job is to work with their fellow students to read all that data in Python, and then try to figure out how to clean it and then try to figure out if they can find Some general patterns in that data and then tell a story with that data all within one week. So their final presentation is that Friday of the first week that they start,

Laurence Bradford 21:08
Wow. Right Really? Well, there's a phrase for that we throw people in right away. I forget there's whenever I think of it later, but but it's like some some saying when you just throw someone in right into the situation right away and have to do something, right. They their presentation their first week, and then you don't have to go into like every week of the program, because of course, you know, it's 12 weeks. But what are some of like, the higher level topics that you then teach throughout the course and like, what kind of skills are the students walking away with at the end?

Alice Zhao 21:37
Yeah, so we have the students to five different projects throughout the course. And then each project focuses on some combination of programming and math skills. So I explained the first one. So the second project, another two week project, that one focuses on linear regression, and then also a lot more Python skills and then The third project is on classification. And then we teach them sequel as well. And then the fourth project goes into natural language processing and clustering. And then that final project we call their passion project, they can basically go into any of the tools or algorithms that we've taught them. At that point. They know how to work with big data tools. They have some basic deep learning skills. Just a lot of there's a lot of stuff that we teach them and then they can bring it all together at the end.

Laurence Bradford 22:34
Nice, nice. So a 12 week program at the end of the day, his passion project, and of course, you you know, you're working with beginners, essentially, right day in and day out. For someone listening to the show and wanting to get into data science, maybe they're, you know, they haven't even taken an online course if they really haven't looked into it very much yet. What are some things you could advise them to do first, maybe some resources To check out or maybe, yeah, I don't know, I would love to hear what you think would be a good place for me to start.

Alice Zhao 23:05
So for me, I always like to start with a goal or a project idea in mind. So, for me, for example, for my text message analysis, so my goal was to see how my conversations with my husband changed over time. And then, after I had that goal, then I had to figure out what tools I need to solve that problem. And so then I ended up pulling a lot of that data into Excel. And so then I learned how to use Excel to manipulate that data. And then I learned how to visualize that data using a program called wordle. Online. And then I was able to create word clouds with that data. And that basically, with that goal in mind, I was able to pick up kind of the small skills that I needed to be able to accomplish that goal.

Laurence Bradford 23:57
Yeah, and I love what you're saying. No, well, you're just speaking right before about about this series of things that you went through, right? Having the goal finding the tool, the visualizations, right? It's kind of like this story, that it's like you're, you're have this question. And then you start to, you know, analyze, dig deeper, and you kind of like tell the story of the day, then you end it, you wrap it up at the end with this conclusion. And I know for you, with that conclusion, with your text messaging between marriage and dating, you found a few things like some of the word differences and what that really meant, and so on and so forth. And I love that because, you know, I work I have a full time job and which is separate from you to learn to code me podcast and blog and all that. And I do get to work with a data team there on the on the day team, but I work I work with them. And I feel like there's such a difference between someone or just, whether it's you yourself or someone you're working with, just like running the numbers and just getting this information. And then and then like really Thinking about like, again, like the goals and the story it tells and what do you also think of that? I know, at least for me in my work, like, sometimes I start off wanting some information. And then I start to see these patterns. And I'm like, wait, actually, all the data I asked for Originally, it doesn't even matter. Can you actually find XYZ? Because it's way more important to whatever problem I'm trying to solve? So I think, yeah, I think it's just that curiosity is such an important part of being a data science and being a data scientist, I don't know. Would you agree and like, have you found like, to me, you sat, you seem like, obviously very technical and analytical, but you also are really great, you know, writing, communicating, and having these questions and finding answers. And I just love to hear like, your thoughts and all that.

Alice Zhao 25:43
Yeah. Oh, man. I agree with all of that. So one of the things that we always talk about in our boot camps is sometimes people think as a data scientist, because data is in the title, you should start by looking at the data and then go from there. But that's it. That's not the way that we believe you should be looking at these problems, you should have that curiosity like you said, and start with a problem or a goal in mind. And then once you start thinking through that problem, then you start figuring out what pieces of data you need to gather to answer that question. Because if you start with the data, then he said, like you that data might not apply to a final question, right? So you should start with that problem in mind, and then try to answer it. And then you had also talked about storytelling. And that's such a huge part of data science, and that's one of my favorite parts.

Alice Zhao 26:35
So I start with that question in mind, and then I'm trying to gather data and the whole time I'm trying to, I'm doing my analysis. I also think of what story do I want to tell, and I always had that story in the back of my mind, as I'm doing my analysis, and then for whatever final result I want to present I need to make sure I communicate it with a good story. And so Right now our students in the boot camp, they have a presentation coming up two days from now. And we're telling them stop all of your modeling and data cleaning your goal for the next two days is to figure out your story, because they have five minutes to present on all of the things that they've done in the last however many weeks. And sometimes students want to focus on the technical aspects, which is good to some degree. But at the end of the day, if you have a story that's much more likely to get people's attention and to have people listen to what you're saying. So I would completely agree with that focus. Data Scientist, the great data scientist, they focus on that story, and they're able to tell their results from their data analysis in a really creative way.

Laurence Bradford 27:48
Yeah. 100% and I think this probably applies to other technical fields as well, not just data science, but I know again, from my work in my full time job interview Viewing candidates were really technical roles. The candidates that always stand out to me are the people who Yeah, sure, like they have the technical skills to be honest. I'm not the one vetting them on the technical, the really technical skills and the coding assignments or something. So obviously, that matters. And you know, to get in the door, they have to pass a certain type of assignment or something like that. But yeah, what really stands out is the person who can communicate and explain the technical concepts to someone who is maybe not as technical as they are.

Alice Zhao 28:29
Yeah, absolutely. I think the same things when I'm interviewing candidates. They don't have to have that basic, like Python and the modeling skills. But at the end of the day, the differentiator is how well they communicate or how passionate they are about a particular data set. So my last job, I was working@cars.com and I remember the intern we hired, he was a car enthusiast. And then he also had a had product idea during the interview because he knew the story. Though well, and he was able to not just focus on the data, but think of the business applications as well.

Laurence Bradford 29:07
Yes. The business applications of course, that's important, right for like any, any role, right understanding how whatever you're doing ties into the bigger picture of the of the business. Yeah. Yeah, hundred percent. Okay. So I know listeners and I even know myself like, a couple years ago, if I was listening to this interview, I'd be thinking, I'd be thinking, I think, oh, storytelling is gray. Okay. Yeah, sure, sure. storytelling, but in my head big, okay. Like how does that actually apply to the real world? Like, what do you mean? Like, how do I tell a story and how does that go into like the workplace? So I was wondering if you could share any tips or information on how a person they don't have to be data science but person who's maybe doing a presentation, they're using data in one way or another to make a case? Any tips on communicating that, huh?

Alice Zhao 29:56
Okay, so some things I can think of right off the bat are, starting instead not starting your presentation with, like, data or code or technical terms, but starting your data, starting your presentation with the question that you're trying to answer trying to get everyone in the mindset of the problem that you started with, right? And then the way I like to tell presentations, instead of like starting with the problem, going through my whole process, and then communicating the result, I like to start with my problem, and then tell people, and this was the awesome result that I got. And then once you get their attention, like you're able to save 20% by doing whatever model that I did, and so basically, I start with the problem, then I say, Okay, this is a way you can, this is how great my final results were. And then I kind of go back and I add in some of those details, and then build up to my awesome results again, so I don't know if that helps at all.

Laurence Bradford 30:59
Yeah, yeah. Definitely as with presentations, I guess maybe I was I was just kind of free balling off the top of my head or freewheeling whatever it whatever the term is. But aside from a presentation, that's a great way right to convey and to tell a story. Was there any other tools that you would use? Or maybe today at Metis? Or maybe before cars calm to just kind of get information across in a story like way was presentation like the main medium?

Alice Zhao 31:24
Yeah, I would say in my last job, I was in the marketing team. And so people love PowerPoint presentations. And so that was a flow that is typically used for PowerPoint presentations. And then at Metis, one of the things that we talk about is data visualization. And so we spend a whole week on data visualization tools. And so one of the popular tools that we talked about is d3. And so if you think about like, all those interactive visualizations that you see in news articles like New York Times, that's all using d3 to do that. But then in addition, there's a bunch of easier tools that you can use as well like tableau, or within Python, we use bouquet and plotly. They're basically these ways that you can visualize data. So instead of just telling people what happened, you can put that into a really beautiful visualization. I'm using these already built libraries. So it's not as hard to code.

Laurence Bradford 32:24
Yeah. Awesome. Thank you so much for sharing that. And I was wondering, before we wrap things up, if there is any other general advice you could share with folks who are listening to the show right now and may want to pursue data science. You You mentioned some great advice before about starting with the problem. And then, you know, finding the data to sort of answer the problem and you went through that. But is there anything else maybe it's a blog you really like or a podcast or some online course you took or something, just something that is kind of small and a quick win that they could do right away and see if it's maybe good fit for them.

Alice Zhao 33:00
Mm. That's a good question. So I would say one thing that I would suggest is looking for small wins. So, for example, so if you already use Excel sheets at work, maybe if you want to take your skills up a notch, then you can learn how to write more complex, complex formulas in Excel. And that would kind of take you on the path to doing more advanced analysis for something like data science. Or maybe at work, you use a tool to like a visual tool to pull data from a data warehouse, and you want to actually understand what's happening, what specific data you're pulling, then you can learn a language like sequel that a lot of data scientists use to directly pull that data. So I'm thinking of small ways that you can up either your program skills like with Excel or with SQL, or then also just like small ways to up your math and stats skills. So I watched a lot of YouTube videos on some, like machine learning concepts. So maybe under trying to watch a YouTube video to understand what a linear regression is, or what classification means. Those are my tips.

Alice Zhao 34:23
Yes, I really like that advice. I really like the first bit you shared of like finding ways at your current job to maybe use data more. So maybe you've a project where involves an Excel sheet. And taking a step further may pass the project requirements to like visualize the data or to play with charts or something. I really like that. So yeah, thank you so much again, Alice for coming on the show. I really enjoyed chatting with you. Where can people find you online?

Alice Zhao 34:52
Sure so you can find me on my blog. It's called A Dash of Data. And that's also my Twitter handle.

Laurence Bradford 34:57
Awesome. Thank you again for coming on.

Laurence Bradford 35:05
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Key takeaways:

  • People often think because ‘data’ is in the title ‘data scientist’, you should start with the data–but in reality, the data step comes last. Start with the big-picture problem or question you want to answer, think through it, and then figure out what kind of data you’ll need to answer that question.
  • Storytelling is a huge part of data science. When you’re putting together a data science project or report, think of the story you want to tell. The best data scientists are able to frame and contextualize their data in a way that’s easily understood by a non-technical audience.
  • Start with a goal or project in mind. Then, figure out which tools to solve that problem or achieve that goal, and start by learning those.
  • Look for small wins to level up your skills, whether that’s watching YouTube videos or learning new Excel formulas.

Links and mentions from the episode:

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