Hello, everyone and welcome to the Applied Data Science Specialization Capstone. I'm Alex Aklson, a data scientist at IBM and I'll be your course instructor. All right, let me start by congratulating you on making it all the way to the capstone. By now, you must have completed the Python for data science course, the data visualization with Python course, and finally, the data analysis with Python course. Now, although these three courses later foundations for this capstone, however, there are other secondary skills that you will learn in the first three modules that are necessary for you to complete this course. Okay, let me introduce the contacts of the capstone project through a scenario. Say this is you, and you live on the West side of the City of Toronto in Canada. You love your neighborhood mainly because of all the great amenities and other types of venues that exist in the neighborhood. Such as gourmet fast food joints, pharmacies, parks, grad schools, and so on. Now say you receive a job offer from a great company on the other side of the city with great career prospects. However, given the far distance from your current place, you unfortunately must move if you decide to accept the offer. Wouldn't it be great if you're able to determine neighborhoods on the other side of the city? There are exactly the same as your current neighborhood, and if not, perhaps similar neighborhoods that are at least closer to your new job. So in this capstone project, I hope to equip you with the necessary skills to do so. And not only that, but at the end, you will have the opportunity to be creative and come up with your own idea or problem to solve using location data. For example, you can choose to compare different neighborhoods in terms of a service. Search for potential explanation of why a neighborhood is popular. The cause of complaints in another neighborhood, or anything else related to neighborhoods. Hence the name of the capstone project will be the battle of the neighborhoods. So what you will learn to do is, given a city like the City of Toronto, you will segment it into different neighborhoods using the geographical coordinates of the center of each neighborhood. And then, using a combination of location data and machine learning, you will group the neighborhoods into clusters like this. So in summary, this capstone course will consist of five modules. In the first three modules, I hope to equip you with all the additional skills you need so that in the remaining two modules, you will be able to work on something exciting of your own creativity. You will be required to leverage location data to solve a problem or to get deeper insights into a neighborhood's reputation. So let's get started.