The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
- NoSQL compared to traditional relational databases
- Performing common data science tasks
- Preparing data with document databases
- Manipulating data in NoSQL
- Preparing, exploring, extracting, and model building
- Working with document, wide-column, and graph databases
- Reviewing case studies using MongoDB, Cassandra, and Neo4j
Skill Level Advanced
- [Dan] Hi, I'm Dan Sullivan and in this course I'll be describing how to get the most out of NoSQL databases for data science. We'll be covering how NoSQL databases are different from traditional relational databases. We'll cover some the the key advantages and disadvantages of various types of NoSQL databases and then we'll take a dive into setting up and exploring three different kinds of NoSQL databases: MongoDB, a document database, Cassandra, a popular big data store, and Neo4j, a graph database, and along the way, we'll talk about all the various tools you can use to easily interact with these databases.
So, now is the time for NoSQL. Let's get started.
1. Why NoSQL?
Types of NoSQL databases2m 20s
2. Perform Common Data Science Tasks with NoSQL Databases
3. Document Databases for Data Science
4. Wide-Column Databases for Data Science
5. Graph Databases for Data Science
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