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 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 its pandas library to manipulate data, which will help you refine your skills for exploring and analyzing data. Finally, you will be required to use the Folium library to great maps of geospatial data and to communicate your results and findings. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
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À propos de ce cours
Learner Career Outcomes
45%
41%
10%
Learner Career Outcomes
45%
41%
10%
100 % en ligne
Dates limites flexibles
Niveau intermédiaire
Approx. 14 heures pour terminer
Anglais
Programme du cours : ce que vous apprendrez dans ce cours
Introduction
In this module, you will learn about the scope of this capstone course and the context of the project that you will be working on. You will learn about different location data providers and what location data is normally composed of. Finally, you will be required to submit a link to a new repository on your Github account dedicated to this course.
Foursquare API
In this module, you will learn in details about Foursquare, which is the location data provider we will be using in this course, and its API. Essentially, you will learn how to create a Foursquare developer account, and use your credentials to search for nearby venues of a specific type, explore a particular venue, and search for trending venues around a location.
Neighborhood Segmentation and Clustering
In this module, you will learn about k-means clustering, which is a form of unsupervised learning. Then you will use clustering and the Foursquare API to segment and cluster the neighborhoods in the city of New York. Furthermore, you will learn how to scrape website and parse HTML code using the Python package Beautifulsoup, and convert data into a pandas dataframe.
The Battle of Neighborhoods
In this module, you will start working on the capstone project. You will clearly define a problem and discuss the data that you will be using to solve the problem.
Meilleurs avis pour Applied Data Science Capstone
Its was great experience in completing the project using all skills that we learned in the course, thanks to coursera and IBM for giving me an opportunity to update my selft and also to test my skills
This is a great course and lots of fun. But it is a big commitment and you need to take it seriously. To get the most out of this course you will need to spend at least 30 hours doing it.
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