Welcome to the second course in Data Analytics, Applied Statistics for Data Analytics. If you took the earlier Data Analytics Foundations course, you learned how to analyze, visualize, and communicate about data with particular focus on spreadsheet skills. In this course, you learned about the foundational statistical techniques that underlie rigorous analytics. Statistics was actually one of my undergraduate majors when I went to school some years back. Some people I know might think of statistics as a bunch of dry math, but I find it highly applicable in business and in making day-to-day decisions. For example, if you are considering taking a new vitamin supplement, you might read in the news or look up some studies about whether it works. If these studies mention a p-value or some other numbers, how should you interpret those numbers? How do you think about whether or not to trust the conclusions of that study and therefore to take the supplement or not? So in the application of statistics, and I'm thrilled to welcome back once again your instructor, Sean Barnes, to present these materials. Thank you, Andrew. I'm a huge believer in statistics as well. I think it's one of the most useful types of mathematics that you can learn. I see data as a window into the truth. There's some way the world is, and your job as data analyst is to use that data to better understand the world, essentially by looking through that window. There's always some amount of uncertainty in your analysis, just because the world is full of immense complexity, and statistics helps you reason about that inherent uncertainty. The world is uncertain, meaning we just don't always know what is the deep underlying truth out there in the world. Is this school better than that school? Is living here better or worse than living there? It's a lot of important decisions, important facts that we just don't know. Sometimes the world gives us little bits of data that give us hints of the underlying truth. Statistics as discipline gives us fantastic tools to approach this with rigor, to try to make sound judgments about this underlying truth that then helps us to make sometimes small personal decisions and sometimes really consequential decisions. Sometimes we try to make the world seem really simple, but in reality it can actually be fairly complex. This course is all about the core statistical techniques that will help you make reasoned decisions in the context of uncertainty. You'll start with quantifying and visualizing variability in samples, which are a subset of the group you're interested in. Then you'll use sample distributions to better understand the full population of interest. In the later modules, you'll employ the two workhorses of inferential statistics, confidence intervals, and hypothesis testing to estimate different aspects of your population and interests. So Andrew, what do you think about the role that generative AI has to play in the world of statistics? I think today, unfortunately, generative AI left as its own devices is not nearly as good a data analyst as one of us could be. If you copy paste a spreadsheet into a large language model, maybe you can actually write a little bit of code to do some basic analyses, but to have the context to understand how to maneuver. For that, I find generative AI to be sometimes a useful thought partner or brainstorming partner, such as, do I do this form of hypothesis test or that form of hypothesis test? It's actually pretty useful as a brainstorming partner, but best practices to how to use it as a thought partner, coding companion, advisor along the way to actually think through the critical business problem. I think that it doesn't solve everything, but it is actually a wonderful tool that's making the job of data analysis easier. Ultimately, you are the expert and the generative AI tools are there to help you be even better at the work that you do. In this course, you'll complete hands-on projects on forest fire prevention, music playlist creation, heart disease prevention, and more. You'll use statistics to quantify the uncertainty and risk involved in each case. In the generative AI labs, you'll get tips and tricks for prompting and practice deciding whether a large language model or another tool like a spreadsheet is best for solving a particular task. If you're comfortable with spreadsheet software, including calculating descriptive statistics and creating charts, as well as the basics of LLM prompting, I'm confident you'll do well and enjoy this course. So, if you've taken the Data Analytics Foundations course, you're in the perfect position to succeed and have a lot of fun. You know, in healthcare, they say stat to mean ASAP as soon as possible. I think there's so much exciting material here. Let's go on stat to the next video.