Join Dan Sullivan for an in-depth discussion in this video Explore data with graph databases, part of Advanced NoSQL for Data Science.
- [Instructor] Now in this video, we're going to do…a little work with the graph database Neo4j…using Python.…So I've opened a terminal window here.…I'm just going to demonstrate here that I am…in my home directory, and I'm going to cd…to my working directory which is NoSQLDS_Scripts.…And I'll just list a couple of files here.…These are two data files that we'll work with…and I'll get to those in a moment.…There's just one other thing I need to do…before we start work with the jupyter notebook.…And that is to install a Python driver.…And I'm going to use the command pip install…and the Python driver we're going to install…is called py2neo.…
So that's been successfully installed.…I'm going to clear my screen.…I'm going to enter my command, jupyter notebook.…Now, I'm going to increase the size of the fonts…just so it's easier to read on various platforms.…Now, I've opened the jupyter notebook,…and we'll see that the two data files are here…in my directory.…I'm going to start a new notebook,…so I'm going to mouseover to the new button,…
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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