DataFramed
DataFramed

Episode · 2 months ago

#104 How the Data Community Can Accelerate Your Data Career

ABOUT THIS EPISODE

Data Literacy may be an important skill for everyone to have, but the level of need is always unique to each individual. Some may need advanced technical skills in machine learning algorithms, while others may just need to be able to understand the basics. Regardless of where anyone sits on the skills spectrum, the data community can help accelerate their careers.

There’s no one who knows that better than Kate Strachnyi. Kate 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 has created courses on data storytelling, dashboard and visualization best practices, and she is also the author of several books on data science, including a children’s book about data literacy. Through her professional accomplishments and her content efforts online, Kate has not only built a massive online following, she has also established herself as a leader in the data space.

In this episode, we talk about best practices in data visualization, the importance of technical skills and soft skills for data professionals, how to build a personal brand and overcome Imposter Syndrome, how data literacy can make or break organizations, and much more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

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 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 adult data science educator and evangelists at data camp. Today is 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 the stock 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's trackney. Kate's trackney is the founder of dedicated. 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 and she's the mother of two girls and enjoys running ultramarathons and obstacle course races. She's also on the data I Q on Listo. Throughout the episode we talked 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 the literacy month. Now on 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 the literacy month to talk about your background, how to break into data space, how you built your personal brand and how our audience can learn from it, and how you see the day 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...

...go, 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 entrepreneur bug ever since I was a kid. So when an opportunity appeared for myself in March, I left that company and started dedicated, and that brings me to where we are today. Because I dedicated I do a lot of things. I have courses, I write books, I've 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 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 that the different topics that you cover, but given this data literacy month and many folks in the audience they're looking to break into a career in data. I think maybe you're one of the best 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 the 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 higher 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 a list of 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 rules 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 share. And what if you're seeing 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, to the ones that are 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 B I click, 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 sequel. 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 a machine. And then, lastly, I'll mention the soft skills, right so being able to work in the 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 set the trade off between the importance between soft skills and heart 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 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 miles before you get to that last mile, which is the miles across that this line, we've done a lot of work, we'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 in 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. So I'm very passionate about visual best practices and this this started early when I built my first data visualization and it was terrible. I didn't...

...know its terrible and my manager didn't either. He was like yeah, okay, that's that's 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 froont 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. 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 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'd 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 vise to the next level, and I think there's never been more interesting 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 our top principles for sending 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, data scientists. In this example, I would pull ten, twenty, maybe even thirty 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 at all. I think it's very rare with that a person checks all the boxes, but at least I'll give you an idea of okay, between these twenty dirty job descriptions I looked at I saw the word sequel and every single one right. Maybe I need to learn that. Maybe I need to take some courses and really make sure I know the skill before I started playing 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, if you 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, let's say, Hey, they'll know 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 the space. Kevin, that you mentioned here portfolio projects is something that we really like to talk about a data camp. What do you think our 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 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 your dashports 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 it 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 it els created today, nobody really cares. That's why you put it in their faith. So, as you're building this out. Maybe share your final result online and ask for feedback. Say Hey, guys that created this, is there anything you would change and and 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 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'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 and 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's 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 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 of people commented, a couple of 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 your 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 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 engage with each other's content and it sort of helped us all grow together. The interesting thing that happened was in Eighteen Lincoln contacted me and they're like hey, you're on a short list for Linkedin top voice and data science and analytics, or picking ten people. They're like don't tell anybody like me really like what, what do I know? Like I wasn't just starting out. It 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 was I would read an article, I would share it online, I took a course and 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 being told like hey, you're on the top ten 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 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 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 this management, 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 or maybe I have nothing new to say. I love those two challenges. I think even I, as someone who has been also a content creator for the past year and to have 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 contact, I think the snides in a lot to the impostors 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 impostors syndrome and what advice can you give to the rest of the community? Yes, so I think I had impostor syndrome for a very quick moment in my life until I realized it's okay not to know everything. So I think impostors syndrome comes from thinking that you are not good enough. You don't know when enough. But when you outright come out and say, like I had this with coding right because I was in data and people assume that I'm a programmer. No clue why, and they thought I bike unter are. So I announced that I don't know it, I just don't and that was it and it was such a weight of 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 quoted 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 would be using Jupiter 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 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 in the data you want to share your projects. So you're still an early beginner and your skill set. 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 will probably be my first post. Get people engaged. So now there are 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, 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 it three to five minute video that explains what you did, how you did it, and share the results, because aid 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. The connecting back to your career as a content creator. You know you you're really prolific outside of your work as well, on social media, right, and you've created courses, books, you've let 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 it's funny because a lot of people ask me, like Heith, 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 opportunit unities and I'm really focusing in 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, 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 and that is amazing, and it's because of that that my day to day looks very different. So I tend to wake up about five 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, you know, 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 their home from school because, unless you count going on Linkedin and just like commenting of the work, for me 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, 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 life show on Tuesday's at eleven. Highly recommend that people tune in as well to the dedicated show. Of course, given that it's data literacy month, I think, Asmar, as a great segue as well to talk about your perspective on Data Literacy, your project that you've covered on data literacy as well. Last week we had Jordan Murraw on the PODCAST, who is also a CO author of yours, on the 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 impact society and individuals at large. 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 overcomplicated 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 every day we're producing x amount of data which you didn't produce in the past two years whatever, but the gist of it is there's 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 in every company and come out focusing on just like companies and organizations, if you do massive investments and 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 literally, 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 nineteen, 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 interract with and understand. So keep diving on the literacy for Kids Book, I'd love to know what was the inspiration behind it and how you've approached creating a data book for kids, and what do you think kids need to know about their 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 asked him a data question about how do you read data and it's a very basic book. So it's probably ages six to ten, if not younger, but it walks you through the stressure 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 the 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 or four year old, 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 the little project where they each got to pick a skittles candy. It's a colorful handy for those who don't know what skitters, 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 pick red, and drow up a bar chart. So, to my surprise, I think almost every kid understood the...

...concept immediately and they were able to tell you what 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 are looking at it, started melting in their little hands. That you skittle. But it inspired me because I'm like, these kids actually get it. I was shocked and like they understand data asalization. So we decided to move forward with this book. We hired an artist who actually created the visuals and then we self published an Amazon for fun. Really yeah, it's really an amazing idea and I love the book and definitely that skiles example is really great. Once you presented 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. There 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 kid's 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, like we we built the axes for them and they're 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 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 hearing the book for De Lucy for kids. Do you want to project that into the broader educational system? What type of datotacy skills would you teach if you have magic wand and you can change the educational system as we have today? I think it's starts 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 suxcel create a chart like the base six, Pie Chart, bar chart. When 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 the sound length in 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 the x amount of cases. But what was interesting, what maybe not interesting? First of all, the bars are 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 the people know what they're talking about. Right, UM, trust the data. It's all in the data. But I saw that and it drove me crazy. So, like, does nobody care about this? Right, and it could have been a mistake, 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 their literacy to create like a very informed citizen read is that the 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 their literacy programs. If you were managing an organization, what are the tools and concept you teach us 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 charge that can tell a story. And it's like a pyramid, I guess, and you teach more more technical skills and the number of those individuals get smaller. Where at the top and you're able to collect data from different sources, being able to clean and wrangle and 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 the 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 write that. Some people just need to understand how to read a bar chart properly and being able to understand that 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've identified 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 box because the manager's managers 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, they couldn't agree more. One on personalizing a the literacy program for in groups or individuals, but also creating the enthusiasm. But but what's in it for me, which is something we're going to cover in the lacy a month as well as part of our Webin or some podcasts. Now, as we close out, Kay and,...

...while we have you here, work and listeners follow your work and what are any upcoming projects working on that the community can get excited about? I think the main place you can usually find me is linked in. 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 dedicated dot 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 all tracks course coming out soon. I'm working on a book with O'Reilly called Color Wise, where we to 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 of little 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 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 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, Dal 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,.

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