#104 How the Data Community Can Accelerate Your Data Career

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com. You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or or you're a data leader looking to scale data driven decisions in your organization. Join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in.

Hello everyone. This is Adele, Data Science educator and evangelist at datacamp. Today's the second episode in our Data Literacy Month special. Last week we had Jordan Murrow 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 so important. 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 Strachni. Kate Strachni is 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 Dedicated Conference and the Dedicated On Air 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 ultramarathons and obstacle course races. She's also on the Data IQ 100 list for 2022. Throughout the episode, we talk about building a personal brand in data, how to break into data careers, how data literacy impacts organizations, individuals and society at large, and much more. If you enjoyed this episode, make sure to rate, like and subscribe to Data Framed and check out our content for Data Literacy Month. Now, onto today's episode. Kate, it's great to have you on the show.

Awesome. Glad to be here.

I'm excited for you to be joining us for Data Literacy Month to 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?

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. This is the reason I got into data. I decided I would like more flexibility with my schedule, 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, 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 analytics manager. Had no clue what it meant, 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, he really loved the work I was doing. And I just became so passionate about 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 entrepreneurial bug ever since I was a kid. So when an opportunity appeared for myself in March 2020, I left that company and started Dedicated. And that brings me to where we are today, because at Dedicated, I do a lot of things. I have courses, I write books, I run conferences, do 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.

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 Datacated 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 it's Data Literacy month and many folks in the audience today are looking to break into a career in data, I think maybe you're one of the best people people to chat around. What it takes to build a career in data today and how to build a personal brand, 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's a lot more niche type 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?

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 roles do. But I'll list off some of the commonly known positions. So, data analyst, data scientists. Like you mentioned, there's the data engineer. Now we have an analytics engineer, 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 with. 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.

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, 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 sort 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 that you need, 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 machine learning. And then lastly, I'll mention the soft skills. Right. So being able to work in a team, communication skills, presentation skills, where you actually need to deliver the results that you've been working on.

And I think soft skills is something that is needed across all of these roles. And 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, so I definitely think you need both. Right. So if you're 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 an issue because I see it as running a marathon. Right. I'm a runner, so I like to throw that into any conversation, but 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 result 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 this, 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. Develop dashboards and communicating with stakeholders that you found worked a lot for you when delivering data stories.

So I'm very passionate about visual best practices. This started early when I built my first data 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, Kate, I don't know what you're changing, but every time I see this, it's looking better and better. Right. So those are the visual best practices. They're 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 about 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 said, lastly is reducing clutter. So removing everything from the visualization that takes away from the main story that you're trying to tell.

I 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 data viz to the next level. And I think there's never been more interest in data science as a career path today. Right. There's 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 roles, but the competition for the roles is getting tighter. So what would you think are top principles for standing out in the job market today for aspiring data practitioners?

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 this skill before I start applying 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. 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 the 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, I use this classification model for blah, blah, blah, and I use the regression for this. And then you could talk about 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, like, 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 space, you might also get input and feedback from those who have done it before that say, hey, Del, 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.

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 virtual cycle of you embedded in this space. Given that you mentioned here, portfolio projects is something that we really like to talk about at datacamp. What do you think are best practices for a portfolio project that really stands out when writing about it online?

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, create 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 end result 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 Adele's created today. No, nobody really cares until, 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, I 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 that's what I would focus on for building that project portfolio.

Yeah, that's amazing. And I think this is an awesome segue, especially to building a personal brand and data, how to even start posting online and becoming a public personality in the data space. Of course, you know, breaking into data and really sending in the data space can really benefit from also building a personal brand. And I think you're 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 on how exactly to go about building a personal brand in 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?

Yeah, I love sharing this 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 natural 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. Tableau, desktop, something right back in the day. And all I did was post saying, hey, I'm taking this test. Anyone have any tips for me? Right? Because I really wanted to pass it. And a couple people commented, a couple likes, and the comments were something like, 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 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 space. 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 LinkedIn top voice and 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 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 would read an article, I would share it online. I took a course, I'd 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.

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 told like, 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?

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 are they going to say if they see this? And the other fear I had was, we have how many hundreds of millions of people 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, financial services. I was at a different stage of life. 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.

I love those two challenges. I think even I, as someone who's been also a content creator for the past year and a half, empathize a lot with these challenges as well. I think you're operating on a much, much higher SC 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 when they're trying to share their work. As someone who puts themselves out there, you struggle with that at the beginning. Kind of overcame that. How have you dealt with imposter syndrome? And what advice can you give to the rest of the community?

Yes. So I. I think I had imposter syndrome for a very quick moment in my life until I realized it's 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, I had this with coding because I was in data and people assumed that I'm a programmer, no clue why they thought I knew Python or R. So I announced that I don't know it. I just don't. And that was it. And 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.

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 want to share your projects, you're still an early beginner in your skillset. Walk me through how you would share a project. For example, if you're starting off in.

Data right now, if I was starting off with data and I needed to share a project, I would first, I 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. Because I actually usually do that because 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, getting feedback on, maybe you should do things differently and then sharing 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 a, that helps you verbalize everything that you've worked on, helps you practice for interviews, helps you work on your presentation skills, your communication skills. That sort of brings it all together.

For you 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, 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?

My day to day is actually not that bad. You know, it's funny because a lot of people ask me like, Kate, how do you do it all? And I'm like, what do I do? I don't really do 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 in on the things that I truly enjoy. So conferences, live shows, podcasts, 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 because it's like watching Netflix for me. I absolutely love it. I love to get on LinkedIn and I could spend hours on there just engaging with people, communicating with people, dreaming up of a random project that I want to 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 about 5am 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 drop them in school, Then I have a couple of hours for calls, then I pick them up. And I don't really work much after they're home from school because unless you count going on LinkedIn and just 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, I do have projects that come up, like working on courses, 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.

Highly recommend that people tune in as well to the dedicated show. Of course, given that it's Data Literacy Month, I think this marks a great segue as well to talk about your perspective on data literacy, your projects that you covered on data literacy as well. Last week we had Jordan Murrow 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.

Yeah, so in terms of a definition, I'd say data literacy is the ability to read, write, 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. Like, like 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.

And 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 going to have a high return on investment.

Yes, absolutely. And people in general. Right. Just like Grandma. Grandma needs to be data literate. Like you're watching news, you need to be able to question some of what you're seeing.

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 like, stuff like Spotify wrapped. This is a data story that you need to be able to interact with and understand. So deep diving on 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 what do you think kids need to know about data literacy?

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 like 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 ages 6 to 10, if not younger, but it walks you through this treasure map of how do 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 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 3 and 4 year olds about data. We took them to a little project where they each got to pick a Skittles candy. It's a colorful candy for those who don't know what skittles is. 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 green, 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 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 it. Some of the kids like started licking it, started melting in their little hands. Don't use Skittles. But it inspired me because I'm like, these kids actually get it. I was shocked. 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, it's really an amazing idea and I love the book. And definitely that skills example is really great once you present it for 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 engagement and how easy was it for them to assimilate these ideas?

It was extremely easy. Like 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. We built the axes for them and they were like, okay, red we have how many? Four. They counted it up and they were really into it. So I think it was helpful to show them how you can quickly organize information of 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, similar concept.

And we're thinking about here, the book for Daily for kids. If you want to 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?

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, they don't really know how to use Excel. Create a chart like the basics, pie chart, bar chart, when do you use it? Why do you use it? 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. Because 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 with the charts that are put in front of us. I'll give you a quick Covid example because you mentioned that earlier. I shared this on LinkedIn a while back, but there was a bar chart 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 vast 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 was obviously a problem.

And this is kind of. You connect here to one really important aspect of data literacy to create like 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 are managing an organization, what are the tools and concepts you teach as part of a data literacy program?

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 and charts that can tell a story. And like a pyramid, I guess, you teach more and more technical skills and the number of those individuals get smaller, where at the top, you're able to collect data from different sources, being able to clean it, wrangle it, make sure it actually makes sense, making sure it also makes business sense. So you're not just working in a vacuum, being able to communicate with everybody else in 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. No, 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 being 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 a 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? 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 box 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.

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

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 datacated.com is another great place in terms of projects I'm working on. So I've got courses on Dedicated Circle. We might have an Alteryx course coming out soon. I'm working on a book with O'Reilly called Color Wise where we do talk about intentional use of color for data storytelling. And then in terms of 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.

That is really exciting. I cannot wait to check out especially the book on data visualization. Very excited about in Colors and data storytelling. Kate, it was great to have you on the show. Do you have any final call to action before we wrap up today's episode?

Call to action is Stay dedicated everyone. Thanks for having me on the show.

Thank you so much Kate, for the time and thank you so much for the insights.

Awesome. Thank you. Adele, you've been listening to Data Framed, a podcast by datacamp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share updates you love that helps us keep delivering insights into all things data. Thanks for listening. Until next time.