Join Dan Sullivan for an in-depth discussion in this video Extract data with document databases, part of Advanced NoSQL for Data Science.
- [Instructor] Let's work with some data.…I've opened my browser to the University of California…at Irvine Machine Learning Repository.…And I'm looking at a dataset known as the adult data set.…I'm going to click on data folder…and I'll notice there are several files here.…There's one called adult data.…I'm going to click on that to show it.…And we can see that it's a combination…of categorical and numeric data.…And this is a file that's used…for testing machine algorithms that make prediction.…It's a good one for us to work with.…I'm just going to navigate back.…I'm going to right click on adult data, select Save Link As,…and give this file a name called income dot TXT.…
I'm going to save it into my no sequel for DS scripts.…I'm going to save it as a text file.…And I'll click Save, and that has been saved.…Now I'm going to close this and go back to my Jupyter notebook.…Now you'll notice that in the directory…I have these income dot text file, which I just downloaded.…I also have income underscore header dot text.…
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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