how to teach data storytelling

Speaker A: To me, data storytelling is something that's not going away. It's going to be there and it's going to be important.

Speaker B: Analysis alone doesn't tend to lead to action, but understanding does.

Speaker C: The class that I didn't have when I was in graduate school, I now get to teach, which is a lot of fun.

Speaker D: Welcome to the Storytelling with Data podcast where listeners around the world learn to be better storytellers and presenters. We'll cover a wide range of topics that will help you effectively show and tell your data stories. So get ready to separate yourself from the mess of 3D exploding pie charts and deliver knockout presentations. And with that, here's Amy.

Speaker A: Hi everyone.

Speaker E: Welcome to the podcast. For those of you who don't know me, I'm Amy Esselman, one of the data storytellers on the team at Storytelling With Data, Essentially my job is to help others develop their data storytelling skills. And at Storytelling with Data, we teach these skills to organizations and individuals through hands on workshops, interactive exercises, challenges in our online community, as well as through our books. Beyond my full time work as Storytelling with Data, I also teach university graduate students how to tell stories with data using the Storytelling with Data books as the core text. And I'm actually not alone in this. Our books are currently being used by over 750 instructors in courses across numerous disciplines and countries. It's really fascinating to see how data storytelling fits into fields like engineering, business, communication design, and psychology at uh, both the undergraduate and graduate levels. Corporate trainers are also teaching these skills to employees, highlighting their broad applicability. In this episode, we are going to share tips and ideas for teaching data storytelling skills, whether to university students, colleagues or your team. I've invited members of our Storytelling with Data University instructor group to discuss their approaches and strategies for helping others develop. Let's meet the group now. Hi everyone. Thank you for joining the podcast episode today and sharing your tips. I'll give you a moment here just to introduce yourselves.

Speaker F: Hello, my name is Francisco Cruzado Perni and I teach data Visualization at ie, uh, uh, University in Madrid, Spain.

Speaker B: Hi, I'm Lisa Carlson and I teach Data Visualization, the introductory course in the University of um, Washington's 10 month data visualization certificate Program.

Speaker G: Hello, my name's Simon Conliff. I'm a professor in the Department of Economics and Finance at Westchester University.

Speaker C: Hi, my name is Elizabeth Ricks and I'm a faculty member at Wake Forest School of Business in Winston Salem, N.C. i primarily teach in our graduate schools, so I'm teaching working professionals and Those professionals early in their career.

Speaker E: And last but definitely not least, Joel Dunohoe, tell us where you are teaching

Speaker A: data storytelling class as part of the executive MBA program at Xavier University in Cincinnati, Ohio.

Speaker E: Amazing. Thanks again for joining this chat. All right, let's begin our discussion around why data storytelling skills are so critical to learn. I'm really curious to, uh, hear how you are teaching these skills and what components of data storytelling you're covering in your courses. Elizabeth, why don't you kick us off first?

Speaker C: I teach business analytics as well as data storytelling, so you can imagine both analyzing data and communicating data. Now, the analyzing data I learned, uh, by trade. So my graduate degree is in business analytics. However, at the time that I went to graduate school, I did not learn anything about effective communication with data. So I learned that the hard way in my own analytical career that I needed to be more effective at communicating. That actually led me to discovering storytelling with data, which then led me to working with storytelling with data. So I was, um, one of the first hires on the team and got to spend, spend many years with Cole and, uh, my former colleagues, growing the business, reaching people, uh, both in the US and around the world. So I have great love for this organization and it's a lot of fun for me now to get to bring that back to the university level and, um, close the gap on the communication side. The class that I didn't have when I was in graduate school, I now get to teach, which is a lot of fun. The components that I teach of data communication really are very similar to the five lessons that storytelling with data teaches. Cover the importance of context and audience. I actually teach from all of Cole's books. So I'm most familiar with the white book, which is the one that I find really is the most practical for all audiences. Now, for my MBA students, I teach from the yellow book, which goes a little bit deeper into that context piece that I talked about. The planning process, the storytelling piece, the narrative. And then for my early career students, we spent a little bit more time on the data visualization side and the slide, uh, design side. And so for those ones, I use the white book as well as the blue book.

Speaker E: Awesome. I love how you're sampling different books for different audiences because they have different needs. Simon, I know you're teaching different courses. You want to tell us about how you're bringing data storytelling into your curriculum.

Speaker G: Among the courses I teach are econometrics, healthcare economics, analytics, and managerial economics. These courses tend to be quite quantitative in nature. Visualizations tend to be part of the research process, such as identifying extreme values or discerning patterns. However, visualizations are perhaps not emphasized enough in the end product. Analyses are often presented in table form, leaving the reader to sift through the rows and the columns to discern the key findings and takeaways. This runs the risk of our hard work falling flat. To remedy this, I ask students to provide a visualization that highlights their findings. The visual should be free of clutter. Uh, each element should be chosen with care to best convey the results. Also, the chart should be able to stand on its own with little or, uh, no explanation needed.

Speaker E: Okay, so much more technical group of students for you, Simon. But I love how you're still bringing these principles to light and making sure that they can effectively and clearly show their information in a graph. Joel, I know that you came upon teaching in a rather unique way. You want to share your experience with us.

Speaker A: One of the things that got me connected to Xavier was actually playing soccer in my free time and, and one of the fellow, uh, soccer players happened to be a dean at the business school and after a game one day just said, hey, I saw you have a career in analytics and would you be interested in teaching a course as part of the program? And you know, teaching is something I had thought of as a kind of a post corporate career opportunity and done a little bit of teaching in my career. So this felt like a great opportunity to pick up on that. So I naturally assumed he was going to turn around and hand me a, uh, curriculum and a syllabus and I was just going to execute that. That's what I said, you know, what's the, what's the class? What am I teaching? And he said, whatever you want. Which obviously creates a new scenario of opportunities for me in the sense of I better figure out what I want to teach this class. And so I coincidentally was reading the Storytelling with Data book. The original white, uh, version felt there was a lot of relevant content in there. Not only, you know, for my corporate career, but I was thinking about what would an executive MBA student look for and need to advance in their careers? To me, data storytelling is evergreen. It's a component of your ability to assimilate data, to take it and transform it into a way that an audience will understand and ultimately will act on. For all the advancements in technology and all the buzz around AI, things that may be shifting in the corporate landscape, to me, data storytelling is something that's not going away. It's going to be there and it's going to be important. And so that's what I did. I essentially just took the bones of the Storytelling with Data series and started to think about making that into a course.

Speaker E: Well, who knew playing soccer recreationally could land you a university teaching job? That's so fun. Lisa, uh, you want to tell us about your role, your database 310 plays in the certificate program at the University of Washington.

Speaker B: My role as the first instructor in the program is really about setting the foundation. So this is where the students, they learn both theory how people see, think and process information. That's the theory, uh, behind data visualization and then how to apply those ideas in real world situations. So a lot of students come, um, in comfortable using Excel or reporting. They have a lot of confidence in that area, but they really do tend to struggle with turning those skills into something clear and useful. So we initially, in our first course, we focus on moving them from those dense, ugly Excel spreadsheets to simple data visualizations like a bar chart, a chart that actually can help someone understand what's going on in the data and make a decision with the data. So I focus on teaching data storytelling, making that a, uh, priority. Because analysis alone doesn't tend to lead to action, but understanding does.

Speaker E: That is so well said, Lisa. It's not the data analysis alone that's going to lead to action, but rather it's our audience's understanding. I love that. Francisco, you want to tell us about what students at IU University in Madrid are learning about?

Speaker F: I focus a lot in creating impeccable and effective visualizations, but also in creating good stories that can help us telling the insights that are, uh, underneath our data.

Speaker E: That's amazing. Each of you is teaching in a different area, yet the same skills are applicable. So we've talked about the what and the why a little bit. I'm sure listeners, however, are curious on, um, the how as well. How does one teach these elements? We all want to develop these skills or instill them in others. What teaching methodologies or innovative exercises are you using in your courses to enhance these skills?

Speaker B: Well, it's very hands on. So it's not just lecture. We do have lectures, very short lectures. And then we have wonderful guest speakers, we have guests, Alex Velez from Storytelling with Data and other wonderful guests in the industry. Students are taught to critique different data visualizations. They find out they're in the wild. And we do a lot of hands on workshops where we actively build data visualizations and then we iterate and we revise and we make them better. And really one of the Most effective exercises we do in this first course, which is 10 weeks, by the way, is we build up to the three minute story. So students have to distill their message down to a single big idea. If you've read storytelling with data, you've read about the big idea, the three minute story, and the ability for students to communicate it clearly in just a few minutes, three minutes. Right. And so what they're learning, it's frustrating for them because many students want to go on and on. But the constraint, that time constraint is very powerful because it forces clarity and it mirrors those real life situations where time is limited. Your audience is in a hurry kind of, huh, Tell me what's going on. I need to know what's happening with this data. And you need to get to the point quickly to honor your audience.

Speaker E: Joel, you want to explain how you're using real world data in your course?

Speaker A: I found some, some data sets on Kaggle from the Shark Tank series and just acclimate around that. The idea that you've got these investors that are sitting around and deciding how much of their own wallet they're going to spend with these various entrepreneurs. And the entrepreneurs are the ones essentially telling a data story. They're trying to get these investors to buy in to their idea, to their company, to their vision. So they got to make a pretty compelling case in a, in a fairly short amount of time. So I thought that held true in many cases to what we want to try to get through within a data storytelling exercise. So what I did is, is essentially take that Shark Tank data, give that to the class. Obviously they can work with that data set. But then I introduced this element of who's the storyteller? And in this case I said it's Thomas Edison. And so we want to use Thomas Edison as the person that's standing up in front of those sharks trying to get their, their dollars. So there's a little bit of an education in terms of knowing your audience. We've got to get Tom educated about what this whole shark concept is. So there's a little bit of work there. Obviously, Thomas Edison has done a number of different inventions. And so as you looked at what the matchup is to the different sharks, right. How many of them have invested in call it, you know, technology companies and innovative companies from that standpoint, and might be a better fit for, for Thomas Edison than, than maybe somebody that's one of the sharks that's not as interested in that space. So a lot of pattern matching relative to matching sharks behavior to what you think Thomas Edison would be interested in and ultimately just allowing that creativity to happen. Let the class envision what that experience is for for Edison, what the experience is for the sharks, what that final pairing is going to be. So the class has fun with it, but at the same time it uh, a. It's a pretty interesting connection to data sets, data storytelling in again I think uh, a fun and creative way.

Speaker E: I love how you gamify it a little bit with getting the investors to fund the entrepreneurs. I bet it gets really interesting and exciting within your course. Simon, you've told me that you are also using real world data sets as well, right?

Speaker G: I draw from numerous real world data sets. Real world data tends to be messy. Quality control is important. I have students use visualizations to identify problems in the data. Survey data is particularly prone to issues where missing data or refused responses may be coded as negatives or extreme positive values. So visualizations can play a key part in the quality control process when conducting research.

Speaker B: Almost everything we do uses real world data sets and realistic scenarios. So students aren't just practicing techniques. They're working with messy data, ambiguous questions, and real decision contexts. And there's a lot of great data sets out there. Storytelling with Data has a whole list of data sources that you can grab. And we frame assignments around things like explaining change over time or comparing two or uh, more different groups with each other so students can see how storytelling choices shift depending on the situation and the audience.

Speaker E: So it sounds like a lot of us are using real world examples as teaching material, as case studies for students to learn from, as ways for them to demonstrate their skills. I'm doing something similar in my course. I have two major projects. There's a midterm dashboard project and then there's an end of course data story presentation that students deliver and they get to pick their data set for those two projects. So a lot of times they are going out to places like Kegel, which Joel mentioned, that has a lot of open source data sets or sometimes they're actually doing projects for work if they're able to anonymize it. Not usually collecting data firsthand or surveying. We're trying to use existing data, but it is helpful for them to see, I think what Sam mentioned, the messiness of the data and really grapple with real world constraints and real world data and real world scenarios. All right, I want to shift gears a little bit. Audience, as we all know, is the center of data storytelling. It's an important topic we cover in our books, in our corporate workshops how do you teach students to adapt their data stories for different audiences, such as executives versus technical teams or perhaps communicating to the general public?

Speaker B: The audience is really deorganizing principle in all of this and in our course. So students learn that the same data set can and often should be communicated differently depending upon who they're talking to that all important audience. Every design choice should connect back to what the audience needs to hear and see to understand and what decision they are trying to make. Because every audience is unique. So if the audience doesn't understand the M message, the work really hasn't done its job. So throughout the course we talk about how people interpret visuals, how design choices influence meaning, and how easy it is to overwhelm your audience with too much information. Storytelling becomes the way we connect all of that.

Speaker E: I love it. Making sure we aren't creating a one size fits all communication, but rather tailoring it is really critical learning.

Speaker F: Uh, put a lot of effort on, um, making them think about having different roles and how these stories will change depending also in the way how they are going to present these stories.

Speaker C: This is a fun one for me to talk about because I teach in so many different programs, so I reach so many different audiences as far as their familiarity with how important this is. Now, uh, this has been a little bit of a change for me because when I worked with storytelling with data, we were mostly working with working professionals. So everyone was pretty much aware when they came to our workshops that this is something that was important and that they were invested in spending their time. My MBAs and my graduate students are very similar to this. In fact, they come into class and they are ready to learn. They've got some battle scars. I don't have to spend a ton of time selling them or bringing them up to speed on how important this is. Now, my students that are much, much earlier in their career without work experience, I have learned over the past couple years that I have to take a different approach with them. And it's funny, right? It is all about your audience. So I've had to learn for this specific audience the importance of teaching audience to them. So one thing that I've started doing to get them to understand this is using within my data storytelling class, building on a project that they've already started working on earlier in their program. So I happen to teach in a program that has a cohort design. So they are with a team and they go through this entire program in the same team. So they've already spent some time analyzing data. So that works really really well coming into my class that they can just use one of their projects that they've already worked on. So the very first week of the class, I asked them to present an analysis. And so you can imagine there's some room for opportunity here, there's some room for improvement. But what's interesting about having them do it on day two of the class is they, they recognize immediately and by watching their peers that, hey, there's another piece of this, right? It's not enough just to stand up and give no thought whatsoever to their analysis. So I have found for that audience that doesn't yet have that workplace experience, that very uncomfortable day two presentation is the catalyst to getting them to understand why this class is so important.

Speaker D: If you're curious about how AI can actually help you become a better data storyteller, Cole and Simon are hosting a free live mini workshop on July 13, 2026 at 11:00am Eastern. It's called AI for Data Storytelling. Better Graphs, Slides and Presentations. This isn't about handing your work over to AI. It's about learning where AI can help you to generate ideas, refine your message, and improve your visuals while keeping you in control of the story. Through real examples, they'll show where AI helps, where it falls short, and why human judgment still matters. To register, visit storytellingwithdata.com, click resources, then AI for data storytelling. We'll see you there.

Speaker E: Now, a topic that comes up a lot during our workshops and when I speak with instructors as well, is the topic of tools. What tools should we be using in our work or what tools should we be teaching to our students? And my typical response is that these skills are tool agnostic. Meaning as long as you know the foundational lessons, you can apply them in any tool that you are familiar with enough to be able to implement the formatting changes needed to tell a clear and compelling story. However, I recognize students need to know tools to put the lessons into practice. I teach my students Tableau specifically when we are going through the exploration process and then PowerPoint when we're doing the explanatory analysis piece. But I'm curious for others, how do you balance teaching technical data analysis skills with narrative and visualization techniques in your courses? What tools or platforms have you found most effective for teaching this?

Speaker F: Um, we use, uh, the software Tableau, which I believe is incredibly useful for creating effective visualizations, but also the story feature is very accurate, uh, for this skill. One of the main challenges that I, uh, fight during the course with my students is specifically that to be Tool agnostic, uh, material, uh, besides learning this tool which is great, I love them to focus on the storytelling techniques that can be used throughout different tools that they may use in the real life.

Speaker B: I'm also very clear that data storytelling is tool agnostic. Now we use tools like tableau because they're great for practicing and iterating, great for beginners, but the tool really is never the point. And software doesn't make storytelling decisions. We know that people make those decisions. So clarity and responsibility to the audience come from the person and not the platform. And we know that. So when it to comes prompts to assessments I like as an instructor I like to focus on outcomes rather than that uh, perfectly polished chart.

Speaker G: I try not to let technology obstruct good data visualization and storytelling. The analyses may be performed using variety of software. However, emphasis is placed on making the chart clear for the intended audience. Excel's recommended charts can be a good discussion starter. So those recommended charts offer uh, a way to display the data. Some options are clearly better than others. Students can start to build some proficiency in what visual might best suit the type of data at hand.

Speaker A: One of the funny anecdotes from just building out the course when I got asked to to uh, put one together for this executive MBA program was don't make it too technical, don't make the people code, don't make the people put fingers on, on keyboard relative to writing SQL or anything along those lines. So I was very sensitive to the use of tools. And so really what I did was consider Excel to be table stakes Excel knowledge. I would again making the presumption that, that everybody had a base use of Excel. So minimally we should be able to execute the charts and the graphics that we want at an Excel basis. But then I brought in Power BI as another alternative. And again it wasn't the intent to say like we're going to just build everything in Power bi. It was simply to say you have an option if this is something you want to pursue, something you're already familiar with. Here's Power BI that's adjacent to Excel can do some more things. Ultimately those were two options and then I really left it to the student as well. So again they already in their current workspace may already have tools that they're more familiar with. The BI tool, tableau, looker, etc. They could use those.

Speaker E: Power BI is one that has come up a lot from my students. I think it's becoming more and more popular. Something I'm exploring trying to figure out how to make it work for both Mac and Windows users in my class, but always great to hear what others are focusing on. Now, in my own experience, and perhaps yours as well, our best planned efforts don't always pan out. So I want to shift gears now to discuss challenges or obstacles when it comes to learning these skills. I'm curious, what are some of the common issues students are facing when it comes to learning data storytelling? And have you found something that works to address these barriers?

Speaker G: Creating compelling audience specific visuals to drive decisions and discussions is an underdeveloped and underappreciated skill. Texts tend to pay short thrift to the topic. Maybe one chapter at most is included, and then the topic of visualization is seldom revisited. This tends to contribute to students paying less attention to the visuals than the analyses. Both are important. Great analyses and results can lose their impact if the message is muddied by poorly executed visuals. Therefore, in my courses I return time and time again to the topic of visualizations, both as part of the research process and the final product.

Speaker B: One of the biggest challenges students face is wanting. They want to show everything, and so they put everything in the chart, every color, every color into the sun. And they often feel like leaving something out is going to weaken their message, their work. When in reality we know that it usually makes it stronger. It keeps the focus on the insights. Over time they learn that good storytelling requires editing, restraint, and respect for the audience's attention. I often tell students, create your chart and then show it to your husband, your friend, your grandmother, whoever, who may know nothing about the data set or topic and see if they can make sense of it. See if the message is clear to them, a general audience, and listen to their feedback. So because, uh, oftentimes we get that tunnel vision, we understand the data set and we assume our audience already understands it because we've spent so much time with it. We make that assumption, but it's our false assumption.

Speaker E: One of the common challenges I've seen is around fair and accurate data storytelling. How do you encourage students to develop a critical eye for identifying misleading or biased data representation and stories? Are you teaching ethical data storytelling practices and if so, how topic of fair

Speaker G: and accurate data storytelling connects with ethics in research? There are many examples online of misleading data storytelling. Showing examples can help in the early development of being critical consumers of data and visuals. Students may also experiment with trying to create their own misleading visual and accompanying narrative.

Speaker B: We also talk a lot about fairness and accuracy, so it's very easy to mislead with data, often unintentionally through bad design choices or missing content. So we encourage students to question assumptions and validate their data and think about what's missing and not what's just shown. And ethical storytelling is really about respecting your audience. And then we let students peer review their work too, to not just get the instructor's feedback, but to get the feedback from their peers, which is very, very helpful.

Speaker E: This has been a fascinating chat, but we're nearing the end of our time together. Before we wrap, I'll ask our guests to share their perspective on the future of data storytelling, specifically regarding AI technology in innovation. What role do you see AI playing in the future of data storytelling or data storytelling education specifically?

Speaker A: Actually introduced an AI component to my course in the latest class that I'm teaching. And the way I was thinking about it was around using a chatbot as a Persona to bounce off ideas with. And the way I did it specifically was we were early on in the class obviously starting to use the Big Idea worksheet, which was one of the, kind of one of the first elements that we start to use to frame out who we're talking to and what we're going to be talking to them about and what we ultimately want them to take an action on in the end. And so as you think about the, the layout of that and the Persona that you're trying to speak to, what I wanted to do was use the chat agent or the chat bot as that Persona. Um, and so we allow the students to go through their normal exercise of filling out the Big Idea worksheet, but then say, example, this was a financial use case, we're going to be talking to the cfo. Um, we let the chatbot be the CFO and start to raise the questions that a CFO might, to either challenge the thinking, maybe start to introduce some other questions. Um, uh, again, interesting points that the CFO might be looking for that the initial Big Idea worksheet didn't have in it. So it allowed us to have a bit of a dialogue, almost like a preview of what the ultimate delivery is going to be to that audience, but allowed you to get that pre challenge of, uh, your thinking and being on a refine. Again, the worksheet and the messaging, um, at that point. So it was really fun experience, I think, for the students to do that ahead of time. Again, new elements to the class. But I think that was a natural use of, um, AI tools, um, in the coursework that we had at this

Speaker B: point in terms of AI, I see it as a support tool, not as the data storyteller. So it can hardly be helpful with things like cleaning very messy data sets. You have to check it. Double check, or, um, it does a really good job of acting as a sparring partner to refine your story. You think your story is good? Is the story clear to a general audience? What about this type of audience? You can go back and forth, um, but it doesn't understand context or ethics or your audience needs. You have to include that in your prompt. So responsibility for the message does always stay with the person telling it. And that would be our student, the data storyteller.

Speaker C: Yeah, this is an interesting one. And I would even argue it's not the future of data storytelling, it is the present. I have run into many situations where my students are going to an AI tool first to start develop slides or things that those of us who have had enough of our career that we would maybe jump to Excel and PowerPoint. Students who are younger are going straight to AI tools. This really only happened, I would say, this year that I noticed this happening for the first time. And so there's a lot of work and there's probably a completely different podcast around the ethics and the responsible use of AI and how do we approach that and how we think about it? That's the reality. What I think when it comes to data storytelling is that these tools and these capabilities are just that, right? It is a. Another tool. It can be really helpful. It can be not so helpful. And as seen AI claim to be able to put an effective executive presentation together, that's just, just absolutely awful, right? So it still takes us, the human, to be able to evaluate that and critique it. And if we are choosing to use an AI tool, learn how to prompt it better, right? Because by default, these tools don't really seem to have a baseline good understanding of what good looks like. So that leaves us with two options. Either we take the time to prompt it and educate it what we want it to do, or we just continue to not use an AI tool. And there's not a single right answer there. I think, again, it just depends on the context. Now, one thing that my students and I played around with a little bit this semester is we took an entire day and I told them that this is just a sandbox day, right? We are just going to play. There's no necessarily a specific plan or outcome that we're designed for. But I gave them all a graph and then I gave them a better graph, right? So I said, this is an example of an existing graph. This is an example of what I would consider good looks like. And their challenge that class was to open any AI tool and try to replicate what good looks like with the existing graph. And it was really interesting what we found. Most of us were very frustrated by the end of this class because we had spent a ton of time prompting, coaxing and none of the AI tools, and we used everything from Chat to Gemini to Claude, were able to design graphs in the way that we would like them to. Claude seemed to do the best, but even it had a lot of opportunity. Now, my students are very technical. They're in a technical program. So some students figured out that if you actually ask the AI tool to give it the Python code, you could drop it into a separate program, use that code, and then put it back into ChatGPT or whatever AI tool you're using, and then it could actually make a more effective graph. So I think if you're very code savvy, that is a better approach.

Speaker E: Amazing. One final question, y'.

Speaker C: All.

Speaker E: What advice do you have for anyone hoping to teach data storytelling to others? It could be at the university level or within their organization or just to themselves, perhaps.

Speaker F: My recommendations for other professors will be to practice a lot here and to make, uh, exercises and examples with your students instead of just giving them theory, which is great, by the way. One of my favorite books is Storytelling with Data, and I use it a lot in my classes.

Speaker A: In terms of advice for anybody that might want to teach data stories, telling skills to students or peers, etc. Other than just do it is, uh, again, part of the reason that I started my own course built up around this, the Storytelling With Data book series is, to me, it just felt natural. It is taking things that at the surface may seem complicated and scary. And you're talking about data. No, data is. It's so technical and it's hard to understand. But I think ultimately what we're trying to do is be translators and help people not only understand the data, but again, ultimately what we're trying to drive to is an action, a behavior change, a shift. And so to me, that's the exciting point, is now taking just what might be thought of as well, that's just data and it's boring. No, it's boring by itself. But now I'm going to wrap it in a story and I'm going to put it in context and I'm going to create tension and I'm going to, I'm going to take you along, audience member, to ultimately where I want you to go and so to me that is where the creativity comes in and the interest comes in from the students. But, and like I had mentioned previously, to me this is evergreen. This is something that is going to be useful for the, for years to come. As long as we write books, as long as we make movies, storytelling is part of our lives. And I think it's just a natural extension for that storytelling to happen in the workplace just as it happens everywhere else around us. I think that's my advice to, to anybody thinking about it is, is, uh, just do it, make it happen, because it's important.

Speaker C: Couple thoughts here. I think one is really understand your program's expectations and definitions of data storytelling. So I have found, depending on where you teach, there still is a lot of awareness about what data storytelling is and is not. I think some people still think of this as a tableau class or a, uh, power bi class. And it's strictly the data visualization piece. So I have been surprised that I've had to do a lot of education around. Yes, that is actually one piece of it, but there is so much more that goes into it. So that would be my first piece of advice is make sure that your departments and those around you really have a good understanding of what you mean by it, data storytelling. Because that can have a lot of implications for curriculum that overlaps with other classes, how things flow together. The other piece of advice I would have is always make sure that you are very clear on whether you're teaching analysis or communication. And I found that where people are on that spectrum of awareness, sometimes they don't know that there is a difference. So at storytelling with data, one of the things that the team always talks about at the very beginning is this is a class in communicating data, right? We are assuming that you have already analyzed your data and now you're ready to communicate it to someone else. So I have found that phrase, that verbiage, is a really nice, succinct way of clearly differentiating between those two. The other piece of advice I would have for teaching data storytelling skills is the more people can use their own topics and projects, the more they will learn and be invested in learning. As much as it is nice to show examples when you're teaching through a lot of real world case studies or data sets that you have or have found online, when it comes to actually learning and iterating and giving feedback, the more students can use either a work project of their own, a passion project, or just a topic that they are personally interested in, the more they will be invested in learning lead with the

Speaker B: importance of people and not tools. Because one of the reasons I believe storytelling with data has had such a lasting influence is because it emphasizes clearly that audience rules and audience understanding is of, um, ultimate importance. Also, the book is always every group of students I work with that is their favorite textbook that they purchase and they use continuously in their career. And I also believe that Cole is such a, an incredible role model for women in technology in the tech world because it is very hard and competitive. And she's a great example of showing that you can succeed by putting people first and without sacrificing rigor. So if students walk away understanding that data storytelling is isn't about just making charts look nice or pretty or impressing people, but about helping people make better decisions and seeing the insights in their data. And once that is accomplished, I believe that the course has succeeded.

Speaker E: All right, we could spend more time on this topic, but we have to end it there and save what remains for a future episode. I want to just say a big thank you to all, all of our guests for sharing their insights on their teaching of data storytelling to our listeners. Remember that mastering the skill is a journey. Keep practicing, stay curious, and never underestimate the power of a well told data story. I hope those of you tuning in enjoyed the show. Thanks for listening and until next time, Goodbye.

Speaker B: Sam.