À propos de ce cours : This capstone project course will give you a taste of what data scientists go through in real life when working with data. You will learn about why data cleaning and munging is an important part of data science and how it occupies more than 80% of a data scientist’s daily work. You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code. You will utilize python and pandas to manipulate data, which will help you help you refine your skills for analyzing data and creating interesting visuals. By the end of the first part of the course you would have compared different neighborhoods and shared your results. Data Scientists also need to be able to work with different kinds of machine learning techniques. In the second part of this course, you will build and apply a machine learning model using the geospatial data from the first part of the project as well as additional data sources, to investigate and attempt to predict neighborhood attributes using techniques like regression, decision trees, clustering, classification, etc. Storytelling and presentation is a very important part of a Data Scientist’s job. By the end of course you would have shared Jupyter notebooks of your implementation and written a detailed report describing your findings. You will also perform peer review of other’s projects.