Welcome to Data Storytelling, the fifth and final course in this Data Analytics Professional Certificate from DeepLearner.ai. You are nearly to the finish line of this Professional Certificate. After drawing a set of conclusions from analyzing data, being able to tell a story around that will make your presentation much more memorable. For example, the dry data might be product A outsold product B by 20%. That's a fine conclusion to draw. But if you're able to additionally tell a story by digging deeper about how, say, product A, because of a viral social media campaign tied to how cruel the product looks, reached a million viewers and thus took off compared to product B. That's an example of the type of storytelling that helps people better understand the conclusions behind your analysis. Some studies estimate that stories are maybe 6 to 13 times more memorable than just facts and statistics alone. So this is a useful technique to add to your tool chest for how you present the results of your data analysis. With that, I'm glad to welcome back Sean Barnes, your instructor for this course, who is here to share these materials with you. Thanks so much, Andrew. As you pointed out, data storytelling is all about communication and building common understanding. If you just dump a bunch of numbers on someone, it's really challenging for them to make sense of it. And in most cases, as a data analyst, you'll be communicating with someone who's not as familiar with the technical intricacies of your work as you are. As you may know, when a movie gets made, there are many milestones during the process, from initial concept to distribution. One thing Netflix tries to do is to estimate how long those milestones will take by creating a schedule. One of my team members recently developed a visualization to summarize the accuracy of these estimated schedules. Like, are we off by a little bit or a lot? Creating the visualization involved a lot of iteration because there's a natural tension between showing detail and making the trend clear. We found our stakeholders preferred seeing the bigger picture, how estimated dates become significantly more accurate as you get closer to the launch date. I'm glad you mentioned iteration. One of the things I found is that when I'm trying to trough a story to explain my analysis conclusion, the story too needs iteration. Maybe the first time you quickly create some sort of data visualization, but then based on the initial feedback from your audience, you can gradually tune it over time to make it better. For example, when working on building a new data dashboard, say my team's launching a new product and we might want to monitor the number of active users, the number of clicks, website latency, and lots of other things. I usually start with a ton of visualizations of a lot of data, frankly, usually too much. But starting out this way lets me then go to my collaborators to get the feedback and figure out what's useful, what's clear, and what's unclear. And that helps me to iterate to make that dashboard clearer and more streamlined. So using this strategy, starting with say a quick and dirty implementation and then getting feedback, maybe just by talking to a handful of people, I bet you find yourself able to create great data visualizations and stories much faster. Absolutely. Both being willing to try something new and getting fast feedback can help speed up your process tremendously. In this course, you'll start right away creating many of the core products you'll develop daily as a data analyst, including reports, memos, notebooks, and slide decks. You'll learn how to select the right insights to communicate, how to order evidence, and how to design the final product. In modules two and three, you'll learn the fundamentals of Tableau. Compared with static visualizations that you might create in your Python notebook, Tableau has two distinct advantages. You can connect to live data showing up-to-the-minute results, and you can create interactive visualizations. Tableau is an industry standard tool with analogs like Microsoft Power BI and Google's Looker Studio. In the final module, you'll explore the job search process for a new data analytics role. I'll share some great tips and tricks for you. Throughout the modules, you'll also learn to use an LLM as a thought partner throughout the data storytelling process. You'll explore tools for improving your visualizations and providing feedback on your work products. So with that, let's go on to the next video and get started.
Module 1: Data storytelling fundamentals
Certificate