Join Dan Sullivan for an in-depth discussion in this video Working with Jupyter, part of Advanced NoSQL for Data Science.
- [Instructor] Now if you've been continuing right along,…you probably have a screen that looks something like this.…These are log messages printed out by the mongod daemon.…If for any reason you've shut down the mongod daemon,…go right ahead and start it up again.…Open a terminal window and enter the command mongod…to start the command up.…And you should see something like this with the last message…indicating that the connection is now open.…I'm going to open a new shell and start the Jupyter Notebook,…just want to point out…that Jupyter's spelled with a y here.…When you enter that command a server will start…in the background and the Jupyter Notebook will open…in your default browser.…
The Jupyter Notebook is a lot like working…with a Python shell.…But it also has features of a document like tool.…In this case we see the contents of the default directory…from which I started the Jupyter Notebook.…I'm going to create a new directory, or a folder,…for working with our scripts.…By selecting new and folder, the Jupyter Notebook…
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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