Hello, and welcome to this course where we'll talk about data wrangling, analysis and AB testing. I'm Kat Glaeser, and I'll be your instructor for this course. I currently work as a data scientist at a company called Scribd. I've been working in tech for about five years now. My background is in mathematics, but most of what I know about data I learned on the job at Microsoft and at Pandora radio. My favorite part of grad school was teaching, so I'm really excited to get the opportunity to teach you about something I use every day to do my job. I'm always learning new things and my goal is always to unlock a new skill, something that allows me to complete a new project or get better use of the tools at my disposal. When I learn new things, there's always a balance between feeling confident that I understand what's going on, and feeling a little bit uncomfortable with a new concept. Too much in either direction and you'll be bored or completely overwhelmed. In this course, I'll walk you through four example filled modules, each with a different focus to help you apply SQL, like a data scientist. In the first module, you'll be given access to a schema of data. I want you to develop a healthy skepticism around the quality of the data. You'll also learn techniques to validate and clean the data. In the second module, you'll discover how the data gets to you. This is critical in your work as you need to be able to assess the data you receive, if it's clean or if you need to create clean data sets to answer your questions. For the third module, we'll dive into solving problems with SQL. You'll receive tons of practice mapping out complicated queries. In the final module, we'll start to touch on the topic of AB testing. We'll walk through a case study and you'll be able to build out a simple testing framework with the tools and skills we've built over the course. I encourage you to use the first lesson titled, Introduction to the Course Dataset, and the accompanying exercise as a way to gauge if you'll be overwhelmed by this course. If you find the exercise difficult, then you'll need to review the concepts and be careful to move through the lectures as outlined. I will be with you every step of the way. If you on the other hand found the exercise easy, please feel free to move forward through the course as quickly as you like. Practice is important, but if you're short on time, feel free to skip some of the exercises and treat the solution videos as worked examples. This course builds on itself, so you can't really skip around all that much. But if for example you've already used SQL regularly and you're mainly interested in understanding how to set up an AB test, then you might be able to get by just watching the solution videos until module four. Wherever you jump in an exercise, be ready for some data questions that don't have a right answer. In many instances, my solutions are not the only right answer. If your methodology differs, that's fine, as long as you understand which assumptions you've made and how they're differ from the ones I've made. I'm really excited to have you in my course, and I hope that these lectures and these exercises on the dataset I built can take you from just knowing how to write SQL to a place where you're confident in taking on a new data project. I want you to come out of this ready to answer big questions with data, and armed with strategies to get insight out of ambiguous questions. Let's get started.