Skip to main content
HomePodcastsData Literacy

How the Data Community Can Accelerate Your Data Career

Listen to Kate Strachnyi, founder of DATAcated, on how to build a personal brand in data and accelerate data careers.
Sep 2022

Photo of Kate Strachnyi
Kate Strachnyi

Kate Strachnyi is the Founder and Community Manager at DATAcated, a company that is focused on bringing data professionals together and helping data companies reach their target audience through effective content strategies. Kate is also author of several books on data science, including a children’s book about data literacy. She has built a massive online following through social media content creation and established herself as a leader in the data space.

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways


To understand where to upskill, going through skills requirements across your dream job descriptions can give you a very good idea of what skills are in demand and what you should become proficient at.


When breaking into data science, sharing your work and building community helps you stand out from the crowd.


Data literacy enables organizations to derive value from data. 

Key Quotes

I think the first thing you would do, if you're trying to stand out and get a career, as let's say a data scientist. In this example, I would pull 10, 20, maybe even 30 job descriptions of the job you want to have. And read through the skills requirements, just technical knowledge that they're expecting you to have. Obviously, you won't have it all. I think it's very rare that a person checks all the boxes, but at least they'll give you an idea of, okay, between these 20, 30 job descriptions I looked at, I saw the word SQL and every single one. Right. So maybe I need to learn that maybe I need to take some courses and really make sure I know the skill before I started apply to jobs. It also gives you a sense of the skills that are really in demand. So lets you sort of pick and choose what you want to study. We're living in a time when you can learn anything you want by going online by going to data camp, by going to any of these educational platforms. And it's a matter of just knowing what it is you need to learn.


Adel Nehme: Hello everyone. This is Adel. Data science educator and evangelist at DataCamp. Today is the second episode in our data literacy month special. Last week we had Jordan Merrow on the podcast, author of Be Data Literate- The Data Literacy skills. Everyone needs to succeed, and he gave an excellent overview of the data literacy landscape and why it's important.

So now, of course with all of this talk on data literacy, it's also really important to think about how to build a career in data and how to leverage the data community to accelerate a career in data. And there's no better person to chat about these things with than Kate Strachnyi. Kate's the founder of DataCated, she's a prolific content creator in the data space and has delivered courses on data storytelling, dashboard techniques and visual best practices. Additionally, she's the host of the DataCated conference and the DataCated OnAir podcast. Kate was appointed a LinkedIn top voice of data science analytics in 2018 and in 2019. She's the mother of two girls and enjoys running ultra-marathons and obstacle course races. She's also on the data IQ 100 list for 2020. Throughout the episode, we talk about building a personal brand and data, how to break into data, careers, how data literacy impacts organizations, individuals, and society at large and much more.

If you enjoy this episode, make sure to rate like, and subscribe to data, framed and check out our content for data literacy month now on today's episode. Okay. It's great to have you on the show.

Kate Strachnyi: Awesome. Glad to... See more

be here.

Adel Nehme: I'm excited for you to be joining us for data literacy month. Talk about your background, how to break into the data space, how you built your personal brand and how our audience can learn from it, and how you see the data literacy space evolving.

But for the very few data folks out there who may not be aware of you, can you share a bit about your background and what got you so well known in the data space?

Kate Strachnyi: Awesome. Yes, I'd love to share. So my journey didn't actually begin with data and I think that's the case for a lot of data professionals. We all sort of start somewhere. And my career started in risk management and regulatory compliance in the financial services space with consulting. So very, very far from data. But when we fast forward to the point where I was expecting my first child, right, This is the reason I got into data. I decided I would like more flexibility with my schedule, and with my travel. And I actually just started looking internally where I was working to see if there was a job that let me work from home. Now, this was about eight, or nine years ago. So working from home was not the norm as it is today. So what happened was I found a role. It was a Data Strategy and Analytics Manager- had no clue what it is- But I'm like, “hey if it keeps me at home, let's do it!” That was my first introduction to data. And what I was tasked with, I was given the data set and I was given a tool which was Tableau. And what happened was it was love at first sight, I started creating dashboards. I created charts and my manager at the time really loved the work I was doing.

And I just became so passionate. Visual best practices. I kept wanting to improve all the dashboards I wanted to automate everything. And that's sort of how the love with data began. I've always wanted to work for myself. So I always had this entrepreneur bug ever since I was a kid. So when an opportunity appeared for me in March 2020, I left that company and started DATACated. And that brings me to where we are today because, at DATACated, I do a lot of things. I have courses I write. I run conferences and live shows. I help other data companies reach their audience on LinkedIn and other social media. And literally just having fun all day, every day.

Different Types of Data Careers Available

Adel Nehme: That's really great. I'm sure a lot of the audience will recognize what you do from your work on LinkedIn as well and have interacted with dedicated somehow.

But really preparing for this episode was actually very challenging- given the wealth of content that you have, the wealth of knowledge that you have, and the different topics that you cover. But given this data literacy month and many folks in the audience there looking to break into a career in data. I think maybe you're one of the best people to chat with about what it takes to build a career in data today and how to build a personal brand to accelerate that career ascension. So maybe starting off with a former, when I first joined the industry myself, and that was maybe like five to seven years ago, you'd only see two main roles companies hire for like data analysts and data scientists. Now it's still a bit of the same, but there are a lot more niche types of roles. There's a lot more specialization. Some roles require less technical skills to break into data as someone who's really embedded in this space. What are the different types of data careers available to aspiring practitioners today?

Kate Strachnyi: Yeah, I think these days there are just so many different roles and we're not in a place yet where we have clearly defined what each of those rules does, but I'll list off some of the commonly known positions. So data analysts, data scientists, like you mentioned, there's the data engineer. Now we have an analytics engineer, a machine learning engineer.

We have data storytellers. We have data visualization specialists. What else is there? There's just like Chief Data Officers, right? So the list goes on and on. And within each of those roles, you can even specialize a bit, depending on the specific industries and companies that you're working. So lots of different roles. Some are easier to get started with than others. Some require more technical skills, knowledge, and knowledge of mathematics and statistics than others. But I think anyone can do this meaning break into the data career, maybe at different levels and sort of move their way up, but lots of different options to choose from for sure.

Adel Nehme: And what have you seen key skills or main skills across these roles that prepare someone to break into these data roles at the start?

Kate Strachnyi; I think starting with more of the basic roles, the ones that don't require as many technical skills, I think knowledge of some sort of business intelligence software.

Can likely get you started. So Tableau, Excel, Power BI Qlik, all those sorts of data visualization, data analytics tools that are fairly simple to get started with. I would say that's a key skill moving up on that technical spectrum. I think having some programming skills like knowledge of R and Python, definitely SQL knowing how to work with databases, how to pull data and then again, as you get more technical, having a better understanding of the underlying mathematics statistics that run all these algorithms that you might be working with, if you get into a machine learning then lastly I'll mention the soft skills, right? So being able to work in a team, communication skills, and presentation skills, where you actually need to deliver the results that you've been working on.

Trade-off between Soft Skills and Technical Skills for Beginners

Adel Nehme: And I think soft skills are something that is needed across all of these roles in data. This is what we call data storytelling skills, which is so important. How do you assess the trade-off between the importance between soft skills and hard technical skills when someone's trying to break into data science? And what do you think are the most important types of soft skills practitioners need to develop? Yeah.

Kate Strachnyi: So I definitely think you need both, right? So if you are a technical data scientist and you know how to program, and you understand all the algorithms, but you don't have the soft skills to actually deliver the results. I think that becomes THAT issue because I see it as running a marathon. Right? I'm a runner. So I, I like to throw that into any conversation, but it's, it's almost like running a marathon and this is sort of the last mile of that marathon. So we call it the last mile of analytics, where we actually get the results over to the key stakeholders.

Now, if you run the 25 miles before you get to that last mile, which is the 26th mile to cross that finish line, you've done a lot of work. You've put in a lot of training, a lot of effort, but if you don't get it over to the key stakeholders, it's sort of all for nothing because they can't really take action on the analytics that you've done, but you can't really run that last mile without having done all that technical work as well. So it's definitely a balance. And I think in terms of the specific soft skills, working as a team, and just being able to communicate internally with the team, making sure that you're all driving towards the same goal and then presentation skills, storytelling skills that can actually communicate to your stakeholder, why this is important and what action and decision they need to make based on.

Adel Nehme: That's really great. And as someone who is really embedded into the data visualization space and the communication space, maybe walk us through some of your best practices that you've learned as someone developing dashboards and communicating with stakeholders that you found work a lot for you when delivering data stories.

Kate Strachnyi: So I'm very passionate about visual best practices. This, started early when I built my first data of visualization and it was terrible. I didn't know it was terrible. And my manager didn't either. He was like, yeah. Okay, this does the job. But then I kept learning more about the proper use of color, which chart to use and when and why, and what size should the font be? And I started applying that to my work, and then I started getting feedback like, Hey, I don't know what you're changing, but every time I see this, it's looking better and better, right? So those are individual best practices. There are things that you sometimes can't even point out what the difference is, but it just looks better because we're playing to the human brain and making it easier for our audience to understand. So one of the things to pay attention to is selecting the right chart for the type of data that you're trying to represent. Number two, I would say using color intentionally, I am extremely passionate. Proper use of color. I'm even writing an entire book on the topic of color for data storytelling and data visualization.

And then I'd say, lastly is reducing clutter. So removing everything from the visualization that takes away from the main story that you're trying to tell. I

Top Principals for Standing Out in the Job Market

Adel Nehme: Completely agree, especially on that last point. I think there's a lot of intelligent use of decluttering that can be used in data visualization that can really take a database to the next level. And I think there's never been more interest in data science as a career path today, right? There are a lot more learning resources, a lot more organizations opening up data science departments, and a lot more data skills that are needed across the board. This means that there's a lot of higher demand for data. But the competition for the roles is getting tighter. So what would you think are the top principles for standing out in the job market today for aspiring data practitioners?

Kate Strachnyi: I think the first thing you would do, if you're trying to stand out and get a career, as let's say a data scientist, in this example, I would pull 10, 20, maybe even 30 job descriptions of the job you want to have and read through the skills requirements, just technical knowledge that they're expecting you to have.

Obviously, you won't have it all. I think it's very rare that a person checks all the boxes. But at least they'll give you an idea of, okay, between these 20, 30 job descriptions. I looked at, I saw the word SQL in every single one. Right? So maybe I need to learn that maybe I need to take some courses and really make sure I know the skill before I started applying for jobs.

It also gives you a sense of the skills that are really in demand. So lets you sort of pick and choose what you want to study. We're living in a time when you can learn anything you want by going online, by going to data camp, by going to any of these educational platforms. And it's a matter of just knowing what it is you need to learn.

So that first step sort of nails that down. Number two is once you've picked up some of those skills, I would say, start doing projects. Find a data set, do a project, create a portfolio around it, and then don't keep it to yourself. Right? Number one. It's good for you to have when you're in an interview and you could talk about, oh yeah, if you, I use this classification model for blah, blah, blah, and I use the regression for this.

And then you could talk about it, it gives you something to talk about if you don't have prior experience, but I would say talk about it even outside of the interview. So creating content online. Hey everyone. I'm starting this project. I got this data and I did this and this to this. And not only will you start to be known as somebody who is passionate about the.

You might also get input and feedback from those who have done it before that say, Hey Adel, no, you're doing it wrong. You try using this one instead. Right? So you get this immediate feedback as you're learning. So I'd say doing a couple of those things can definitely help you stand out

Best Practices for a Portfolio Project

Adel Nehme: And you get potential feedback as well from potential hiring managers, right?

Or people who work on data teams that you may join, and it creates this virtuous cycle of you embedded. Space, given that you mentioned here at portfolio projects is something that we really like to talk about at data camp. What do you think are best practices for a portfolio project that really stands out when writing about it online?

Kate Strachnyi: So for me, I was big on data visualization, right? So what I was focused on was building out my Tableau public profile because it wasn't a way for me to showcase my skills. So for those who are not familiar, Tableau Public is a free version of Tableau where you are, you can use data sets. Dashboards create infographics, whatever it is you want to put out there and you can make your work public. So it's like having a Facebook profile, but instead of sharing pictures of your cat, you share your, uh, end result of your, of your dashboards and data visualizations. And the cool thing is people can see it. It's sort of like your photography portfolio as well. So potential employers can come in there and check it out.

And again, it's easy to set up. It's easy to create that, but don't keep it to yourself just because it's public doesn't mean everyone's sitting. Out there looking for it saying, I wonder, what did those create today? No, nobody really cares. That's like you put it in their face. So as you're building this out, maybe share your final result online and ask for feedback, say, Hey guys, it created this. Is there anything you would change? And then as you get that feedback, maybe make those changes and take those learnings in. So for me, that's what I would focus on for building that project portfolio.

Building a Personal Brand

Adel Nehme: Yeah, that's amazing. And I think this is an awesome segue, especially to building a personal brand and data and how to even start posting online and becoming a public personality in the data space. Of course, you know, breaking into data and really ascending in the data space can really benefit from also building a personal brand. And I think you are an incredible example of that. And I'd love to pick your brain on how you got started. What made you successful? Any lessons that you can share with the audience and how exactly to go about building a personal brand and data? So walk us through maybe in more detail, how you got started as an entrepreneur, as a content creator, and what was that initial phase like?

Kate Strachnyi: Yeah. I love sharing the story, so I never set out to be a content creator. It all started with my first job in the data space. And as I was learning, I just wanted to share my experience online. Now, it didn't really come naturally to me. I'm not a very, “let me post my life online” kind of person. At least I wasn't before before I actually started doing this, but I remember studying for the Tableau certification exam, the Tableau desktop, something right back in the day. And all I did was a post saying, “Hey, I'm taking this test.”

Does anyone have any tips for me? Right? Cause I really wanted to pass it. And uh, a couple of people commented, a couple of likes and the comments were something. Oh, wow. congrats, best of luck. And some other comments were like, Hey, you should check out these sample questions. We have to help you prepare. And I got hooked, to be honest, I'm like, wow, people actually care. I don't know these people. And they care enough to help me and cheer me on. So I thought that was great. And it sort of kept me coming back. So I started participating in this makeover Monday challenge, where it was a Tableau, a weekly challenge where you get a data visualization and you have to like make it better.

So I sort of got into this challenge every week. And as I kept posting, I think what helped was I made some real friends who were on that same journey. So maybe not really studying Tableau or data visualization, but they were in the data. And we got on zoom calls. We got on. Sometimes we even met up in person and we built those relationships and sort of, as we posted online, we engaged with each other's content and it sort of helped us all grow together.

The interesting thing that happened was in 2018, LinkedIn contacted me and they're like, Hey, you're on a short list for a LinkedIn top voice in data science and analytics. We're picking 10 people. They're like, don't tell anybody I'm like me really? Like what, what do I know? Like, I wasn't just starting out I was probably four years in, but in my brain i’m like, I'm just starting out. So luckily I got on that list and then the year after I got on that list again, and I'm like, maybe I'm onto something and that's something was just talking about a specific topic. Every day, right? Talking about data and that's all I did. I just kept talking about data. I always, I would read an article. I would share it online. I took a course I've shared online. So sort of. Learning and living out loud and using LinkedIn as my personal journal for all things, data is what really helped me get to where I am today.

Challenges of Being an Early Content Creator

Adel Nehme: That's really great. And I love that story, you know, of starting off just like preparing for that certification and then ending up being contacted by LinkedIn and being told “Hey, you're on the top 10 Voices.” Yeah I definitely empathize with being like, I'm just a beginner. What? So it's definitely, it's definitely great. What were the main challenges that you encountered in the early period and what were the different ways that you overcame these challenges when you were growing out as a content creator?

Kate Strachnyi: Yes. My first challenge was I was afraid that somebody would see my content. I know it sounds pretty dumb now. Because now I'm like, Hey, I need people to see my content. But my biggest fear was like, what if my brother saw this post? What if my colleagues, this was still, I was still working. What if my colleagues saw this post? What would they think? Right. Until I quickly realized nobody really cares, they're thinking about themselves. Right. But that was one of my biggest fears is what I think I want to say if they see this. And the other fear I had was we have how many hundreds of millions of people were on the platform. What do I have to say that is different than somebody else? Right? What can I say that hasn't been said already? And I, again, overcame this by understanding that I had a unique perspective, I had a specific background, like I said, consulting risk management, and financial services. I was at a different stage of life. Right? So it's like, we all bring our unique perspectives to the topics.

And I think that helped me get over the fact that, oh, maybe I have nothing new to say

Adel Nehme: I love those two challenges. I think, even I, as someone who's been also a content creator for the past year and have to empathize a lot with these challenges as well. I think you're operating on a much, much higher scale than I am. But when you talk about how would people react when you see this content? I think this ties in a lot to the imposter syndrome. Many people may face this when they're trying to share their work. As someone who puts themselves out there, you struggle with that at the beginning, but kind of overcame that. How have you dealt with imposter syndrome and what advice can you give to the rest of the community?

Kate Strachnyi: Yes, so, I think I had imposter syndrome for a very quick moment in my life until I realized it was okay. Not to know everything. So I think imposter syndrome comes from. Thinking that you are not good enough. You don't know enough, but when you outright come out and say, like, I had this with coding, right?  Cause I was in data and people assume that I'm a programmer, no clue why. And they thought I knew Python R so I announced that I don't know it. I just don't. And that was it. And it, it was such a weight off my shoulder because once you stop pretending that, you know, something or making it seem like, you know, something, or just trying to show that, you know what you don't actually know very well.

It becomes scary and you start feeling like a fraud. But when I came out there and said, Hey, I've never coded a thing in my life, but I'm learning. It became so easy because then everyone's like, well, let me help you. Like, you should, you should be using Jupyter notebooks for this. And I'm like, okay, great. Like, I'm learning. So I think coming out, just telling people what you don't know, you know, I'm great with Tableau and data visualization, but I'm not great with this. It makes everything so much easier because now you're, you're just showing your true self. You're not pretending to, to be something that you're not.

Adel Nehme: You're leaning into your strengths and weaknesses essentially, and you're announcing it to the world. And if you're someone let's say trying to break into data, you wanna share your project. So you're still an early beginner and your skillset. Walk me through how you would share a project. For example, if you're starting off in data, right? 

Kate Strachnyi: IfI was starting off with data and I needed to share a project, I would first, would probably do a poll and see like, Hey, what project do you think I should do? Right. And pick three of my favorite projects. Cause I actually usually do that cuz I, I like to bring my community on the journey with me. So I would say, Hey, here are the three things, and have people vote. So that'll probably be my first post, get people engaged. So now those people who voted for option B, I'm like, okay, next post is I'm going with this project.

This is the announcement. Where do you think I should get the data, right? Or here's where I found the data and announce it that way. Some people might say, oh, Hey, here's more data you can use. Great. And actually makes life and work so much easier because people are so willing to help. This is why I love the data community.

They truly want to be there for you. And like, likewise, I love helping people as well. Once you have that data, you can, you know, tell people what steps you're taking. You can share your approach again, get feedback on it, maybe you should do things differently, and then share your final results. I'd say if you're up for it, make a video.

Right. Make a three to five-minute video. That explains what you did, how you did it, and share the results because that helps you verbalize everything that you've worked on helps you practice for interviews helps you work on your presentation skills, and your communication skills that sort of brings it all together for you.

A Day in the Life of a Data Science Content Creator

Adel Nehme: And connecting back to your career as a content creator, you know, you've, you're really prolific outside of your work as well on social media, right? And you've created courses, and books, you've led conferences, and much more. How do you balance your time as a content creator? And what does your day-to-day look like?

Kate Strachnyi: My day-to-day is actually not that bad. You know, it's, it's funny, cuz a lot of people ask me like, Hey, how do you do it all? And I'm like, what do I do? I didn't really know that much. I think I used to work a lot more a couple of years ago. And I'm now in a place where I'm, I'm saying no to a lot of opportunities and I'm really focusing on the things that I truly enjoy.

So conferences live shows, podcast courses, books, just media content. They all have something in common, right? So it's all about bringing people together and sharing knowledge. So as long as I'm doing those two things, it honestly doesn't even feel like work. Cuz it's like watching Netflix for me. I absolutely love it.

I love to get on LinkedIn and I could spend hours there. Just engaging with people, communicating with people, dreaming up a random project that I wanna launch. And then not having to ask anybody if I should do it or not just simply going for it. The power in that is amazing. And. It's because of that, that my day-to-day looks very different.

So I tend to wake up at about 5:00 AM every day. I work usually for about one or two hours, and then I'm with the kids. They're starting school soon. So I, you know, I drop 'em in school, then I have a couple of hours for calls. Then I pick 'em up and I don't really work much after they're home from school, because unless you count going on LinkedIn and just like commenting a bit work for me, it doesn't feel like work, but I tend to do that a little bit here and there.

And then in between that, you know, I do have projects that come up. Working on courses, and working on a book, but I tend to do that mostly on my schedule. And then the only thing that happens on a weekly basis these days is my weekly show, which is the dedicated live show on Tuesdays at 11.

Defining Data Literacy

Adel Nehme: Highly recommend that people tune in as well to the dedicated show, of course, given that it's data literacy month, I think it’s smart and as a great segue I wanted to talk about your perspective on data literacy, your project that you covered on data literacy as well.

Last week, we had Jordan Morrow on the podcast who is also a co-author of yours on a children's book that I love called Data Literacy for Kids. Before we talk about the kids, I'm very excited to actually deep dive into this, because I do think that there should be a data literacy education for kids. Maybe walk us through your definition of data literacy first and how it impacts society and individuals at large.

Kate Strachnyi: Yeah. So in terms of a definition, I'd say data literacy is the ability to read, write, and analyze, communicate with data. And it really is fairly simple. I think we tend to overcomplicate it with crazy definitions, but I think it is as simple as that. And the reason it's important is we are seeing more and more data. I know people are probably sick of hearing that every day we're producing X amount of data, which we didn't produce in the past 200 years, whatever. But the gist of it is there is a lot of data. We collect more data, we create more data.

And we need to use that data to make decisions, organizations need to use that data to stay ahead of their competition. And I think the higher the data literacy rate within an organization, the higher, the chance that you'll be successful at utilizing the data that you collect, which is important because that is how you can get ahead. And I think it applies to every industry and every company.

Adel Nehme: And come out, focusing on just like companies and organizations, if you do massive investments and you're getting new tools, getting proper infrastructure set up, but you don't have necessarily the skills within your organization to make use of that, then you're not gonna have a high return on investment.

Kate Strachnyi: Yes, absolutely. And people in general, right? Just like grandma, grandma needs to be data literate. Like you're watching the news. You need to be able to question some of what you're seeing.

Data Literacy for Kids

Adel Nehme: Exactly. And this was really kind of evidence in elections. COVID-19, for example, over the past few years, we've seen a lot of graphs, a lot of charts, even stuff like Spotify wrapped, this is a data story that you need to be able to interact with and understand. So deep diving into the data literacy for kids book, I'd love to know what was the inspiration behind it and how you've approached creating a data literacy book for kids. And. Think kids need to know about data literacy. 

Kate Strachnyi: Okay.So it all started with the fact that Jordan and I are friends and we both have kids. So I have two kids, he has five kids and we both like data. So I forget whose idea it was, but one of us said, Hey, let's create a data literacy for kids book. And we're like, okay. Yeah, sure. Let's do it. So we thought through the structure. I think he had one of his kids create a treasure map, like drawing up a treasure map of seven kids that go on an adventure.

They're like hanging out by the lake. They see an iPad. They pick up the iPad. It asks them a data question about how do you read data. And it's a very basic book. So it's probably aged 6 to 10, if not younger, but it walks you through this treasure map of how you actually become data literate. And in the end, they all sit down.

They have ice cream because that's the prize for all data people. It's an attempt to get kids just interested in data. I actually, I was inspired by  this presentation that I did for, it was at that point, my daughter was three years old. It was a three-year-old program and, or maybe three and four-year-olds. And they asked the parents to come in like a career day type of thing. So my husband and I were both in data. So we came in for one of those sessions and we talked to the three and four-year-olds about data. We took them to a little project where they each got to pick a skit. Candy. It's a color for candy for those who don't know what skis are.

And we told them to pick a color. And then at the end, we were going to tally up the colors, like how many picked picked green, and how many picked red, and draw up a bar chart. So to my surprise, I think almost every kid understood the concept immediately and they were able to tally you up, fill in the bars and you know, say, oh look, green was the most popular color.

The problem was don't use Skittles because some of the kids ate them. Some of the kids like started licking. It started melting in their little hands. Don't use Skittles, but, uh, it inspired me cuz I'm like these kids actually get it. I was a chef I'm like they understand data visualization. So we decided to move forward with this book. We hired an artist who actually created the visuals and then we self-published on Amazon for fun. Really? Yeah,

Adel Nehme: it's really an amazing idea. And I love the book and definitely that skills example is really great once you present it to these kids. For example, I think what's really nice here is that a lot of times people think data skills are for the technical folks they're for nerds.

Right. But really what you do with this book is that you prove that data skills are for everyone. Kind of walk me through the kids' reaction when you presented that, how excited were they engaged and how easy was it for them to simulate these ideas?

Kate Strachnyi: It was extremely easy. As I said, I was pretty surprised how quickly they grasped it. It's almost as if they studied bar charts before because they were just very easily able to find the line. Like we, we built the the, A axis for them and they were like, okay, red, we have how many, four, like they counted it up and they were related to it. So I think it was. Helpful to show them how you can quickly organize information about people's preferences, at least for different colors of Skittles. And we go into that in the book as well with the different flavors of ice cream, a similar concept.

Adel Nehme: And we are thinking about here, the book for literacy for kids. If you wanna project that into the broader educational system, what type of data literacy skills would you teach? If you had a magic wand and you can change the educational system as we have today.

Kate Strachny: I think I'd start with the very basics. I think as data professionals, we assume that people know things that we know. I was surprised to realize that most of my friends outside of the data friends that I have, don't really know how to use Excel, or create a chart like the basics pie chart bar chart. When you use it, why do you use? and even before we even teach them how to create charts and why being able to interpret the data that they're seeing around them, cuz we're surrounded by data all the time. And I think it's important to understand. And it's important to know how not to be fooled at times by the charts that are put in front of us.

I'll give you a quick COVID example, cuz you mentioned that earlier, I shared this on LinkedIn a while back, but there was a bar. That showed COVID metrics for it was in a specific state. And they're showing like for the past five days we had X amount of cases, but what was interesting? Well, maybe not interesting, first of all, the bars were all different colors.

I think that was confusing. But the crazy part was, as the bars got larger, the numbers sometimes got smaller and that part was not even consistent. It's as if someone took a random bar chart and I put different numbers on it that made no sense. And I won't be surprised if the best majority of people saw this, heard the story, and neglected the numbers because they assume that people know what they're talking about. Right? Oh, trust the data. It's all in the data. But I saw that and it drove me crazy. So I'm like, does nobody care about this? Right. And it could have been a mistake. It could have been intentional who knows, but it, it, it was obviously a problem.

Basics of a Data Literacy Program

Adel Nehme: And this is kinda you connect here to one really important aspect of data literacy to create a very informed citizenry is that data literacy allows you to be a data skeptic and something that we spoke with Jordan about last week. It allows you to not take data at face value and challenge it and try to understand what are the dynamics behind the visualization that I'm seeing. So connecting back here to a broader data literacy program, a lot of organizations right now are thinking about data upskilling and creating data literacy programs. If you were managing an organization, what are the tools and concepts you teach as part of a data literacy program?

Kate Strachnyi:  I would start with, like I said, the basic understanding of reading different types of charts and being able to take away the key information that is out there. I think. That's for the vast majority of people, I think step two. And that won't be for everybody is how to create and design effective data visualizations in charge that can tell a story and like a pyramid, I guess you teach more and more technical skills. And the number of those individuals gets smaller, where at the top you're able to collect data from different sources and being able to clean it, wrangle it, make sure it actually makes. Making sure it also makes business sense. So you're not just working in a vacuum, being able to communicate with everybody else on a broader scale. But I guess the point I'm trying to make is data literacy skills have to be designed specifically for different groups of people. It's not like, okay, here, everybody needs to know statistics, right?

Like everyone needs to know advanced statistics. That's just not reasonable. You, you don't even need to, right? Some people just need to understand how to read a bar chart properly and be able to understand this looks right. This doesn't look right, but then there are some people who need more technical skills.

So I think starting with the baseline assessment of understanding, what are the skills that are needed once you identify the skills that are needed for each group, doing an assessment to see where the gaps are. And then. Slowly filling in those gaps, but I think it's even more important to get those individuals to understand why they need this. Going back to my corporate life, whenever there was a course that was given to us and said here, take it. It was almost like, oh no, do I have to, like, why do I need this? And we don't even ask why we just know it's mandatory. We have to do it to check a. Because the manager's manager said so, and you're like, okay, I'll take this course. But I think getting people excited and showing them that they get to take this course and that those skills are valuable and transferable to your next job potentially is very important.

Adel Nehme: Yeah, I couldn't agree more on personalizing the literacy program for groups or individuals, but also creating that enthusiasm. But what's in it for me, which is something we're gonna cover in the literacy month, as well as part of our webinars and podcasts? Now, as we close out Kate, and while we have you here working listeners, follow your work and what are any upcoming projects you're working on that the community can get excited about?

Kate Strachnyi: I think the main place you can usually find me is LinkedIn. That's where you can actually have a conversation to know what we're up to and sort of all the stuff that we're doing. I think is another great place. In terms of projects, I'm working on. So I've got courses on teh DataCated circle. We might have an Altrics course coming out soon. I'm working on a book with O'Reilly called colorwise where we do talk about the intentional use of color for data storytelling. And then in terms of4 other courses, I've just launched a. Build your personal brand course on LinkedIn Learning and currently working on another course where we talk about the different data careers and sort of a day in the life of those careers and what you can do to get started.

Call To Action

Adel Nehme: That is really exciting. I cannot wait to check out, especially the book on data visualization, very excited about data and colors and data storytelling. Okay. It was great to have you on the show. Do you have any final call to action before we wrap up today's episode

Kate Strachnyi: Call to action is stay DataCATED everyone. Thanks for having me on the show.

Adel Nehme: Thank you so much, Kate for the time, and thank you so much for the insights. you've been listening to data, framed a podcast by data camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love that helps us keep delivering insights into all things. Data. Thanks for listening until next time.



How to Build a Meaningful Career in Data Science

You can wield the power of data science for good and change the world.

Michael Burkhardt

6 min


The Data Literacy Imperative: Why Upskilling in Data is Essential for Your Career

Discover the importance of data literacy for your career growth, job market potential, and societal impact. Learn how to upskill in data and stay ahead in the competitive professional landscape.
Matt Crabtree's photo

Matt Crabtree

7 min


[Infographic] 5 Best Practices for Building a Data Academy

With the rising need for data skills, organizations are building internal data academies to accelerate their data transformation. Here are 5 best practices learned from DataCamp for Business customers.
DataCamp Team's photo

DataCamp Team

4 min


[DataFramed Careers Series #3]: Accelerating Data Careers with Writing

Today is the third episode of this four-part DataFramed Careers series being published every day this week on building a career in data. In today's episode, we discuss with Khuyen Tran, Developer Advocate at Prefect, how writing can accelerate data careers.
Adel Nehme's photo

Adel Nehme

30 min


Building the Case for Data Literacy

Valerie Logan shares insights on what a successful data literacy journey looks like.
Adel Nehme's photo

Adel Nehme

38 min


[DataFramed Careers Series #1] Launching a Data Career in 2022

Today is the start of a four-day careers series covering breaking into data science in 2022. In the first episode of the DataFramed Careers Series, we speak with Sadie St Lawrence about what it takes to launch a career in data in 2022.
Adel Nehme's photo

Adel Nehme

40 min

See MoreSee More