Welcome to Data Analytics. Data is important. As the world has become more digital, a lot of activities now create data. When you send an email, when you go to a website and click on some things and not on others, when you buy something, when you decide to watch one movie and not another, the world has lots of data. And this means that people, they're able to look at the data and analyze it and understand it, can make much better decisions. This also shows in the number of job openings in data. The U.S. Bureau of Labor Statistics estimates that there will be 36 percent growth in data science jobs by 2033. This is much faster than the average occupation. So by learning about data analytics, you're going to make better decisions in your personal life, in your professional life, and hopefully, if you want, also build an amazing career out of this. I'm delighted to introduce your instructor, Sean Barnes, who is a data science engineering leader at Netflix and has delivered lots of insights to Netflix and to Netflix's customers about data relating to watching various entertaining shows. Sean is also a form professor at the business school at the University of Maryland and so has deep insight into the use of data in business, in academia, and in government. I look forward to him sharing all of these insights with you. Welcome, Sean. Well, thank you, Ezra, for having me. It's pretty exciting to be here. I actually started in aerospace engineering and learned a lot of interesting things along the way, but I did really enjoy using computers and data to help make better decisions. And now I work in a completely different space here at Netflix, where we are using data analytics to help inform the production, the distribution, and promotion of content. I have a friend that also works in the movie entertainment business, and he's told me stories about how there may be, you know, let's say $100 million invested in the show, and then data drives insights to positioning or marketing or changing the show just a little bit. That makes this very expensive investment, you know, deliver positive rather than negative return on investment. Absolutely. I see analytics as an investigative activity. It's both analytical and creative, and that's really how we design this course, to work with practical, real-world examples from hotel bookings to baby names, product sales, home prices, and a lot more. I'm curious, was there ever a time where you used data and it actually changed your mind to the decision that you're going to make? Yeah, one example that came up in my work at AI Fund was a surprise to me, which is as a venture studio, we work to bring in a lot of entrepreneurs, CEOs, to lead different businesses. And so for a long time, a lot of hiring goes by gut, you know, you kind of interview someone, you like them, you patent match, you know, using your brain, and then you hire someone or not. So for a long time, we thought that CEOs need to have fundraising experience. We looked at our data of the number of CEOs we worked with, and it turned out we were wrong. It just wasn't true. And so that data changed our processes, our criteria for what types of founders we now look for. One example, in my experience, a surprising insight was some of the research I used to do on hospital operations. So this work involved predicting when patients would be discharged from the hospital. And one of the signals was whether the patient had a visit from a physical therapist. Oftentimes, that was actually a signal that the patient was not close to going home. So that was a really surprising result. And insights like these can really affect hospital operations and resource allocation and staffing. These are, you know, surprisingly consequential sometimes. That's right. Hospital beds are extremely valuable resources. So you want to be able to send patients home so that you can make room. I really love that you brought in this magic eight ball. I find that there are still too many organizations today that make decisions using processes that are only somewhat better sometimes than this. When a company faces a decision, if there's someone around the table with access to data, then go ahead, bring in the facts, show their analysis so that you can, in minutes or tens of minutes, get that much deeper facts than was possible without data. Yeah, definitely. You don't always have to apply the most sophisticated analysis. In a lot of cases, a simple data story can ultimately help you get to the best decision overall. You don't always have to jump to the most complex method. Today, a lot of people are excited about AI. And with the rise of AI, like most professions, generative AI is changing the job of the data analyst. What do you see in this sector? I mean, I think it's interesting, right? The foundations maybe never change, but we do find creative or innovative ways to do the same thing either better, more efficiently, more reliably. So whether it is using generative AI as a thought partner to interpret visualizations, just so that you as an analyst are not all alone. I think we've taken a really creative approach to how to introduce generative AI. Maybe you want it to help formulate the problems that you're trying to solve. Maybe you actually can leverage it to help interpret the insights or communicate the insights to your stakeholders. It's as if you have a slightly inexperienced but not bad intern that will work for you really hard at very low cost to help you do some tasks so you can think of the high level and ask them, please plot this data, help me figure out how to load this data. So it's been really great to do data analysis with the help of genAI. Absolutely. I'm proud to say that this course is a very hands-on experience with applying these techniques from foundational data analytics, like descriptive statistics, working in spreadsheets, and also exploring our data analytics lifecycle. This course includes an introduction to large language models and hands-on labs in every module. I think you'll get a lot of value out of it whether you're looking to start a career in data analytics or level up in your current role. So these are skills really useful definitely for anyone interested in a career in data, but frankly in probably many other roles as well because of the primacy of computers and digital assets and data in so many roles across the economy. So lots of important exciting topics that you learn in this and the courses to follow. Let's go on to the next video and get started.