Join Dan Sullivan for an in-depth discussion in this video What you should know, part of Advanced NoSQL for Data Science.
- [Instructor] Before we get started, I want to briefly describe some assumptions about background knowledge. For this course, I assume you have some familiarity with relational databases. As I introduce different types of NoSQL databases, I will compare some of their features to the features of traditional relational databases. If, at any time, you would like a refresher on relational databases, I suggest you take a closer look at the Relational Database Fundamentals course. I also assume you're familiar with the command line. Finally, we will be working with Python, so it helps to have some experience with scripting or programming.
You don't need much experience, but if at any time, you'd like more details on Python, I suggest you view the Up and Running with Python course.
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
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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