Join Dan Sullivan for an in-depth discussion in this video Tips for using document databases for data science, part of Advanced NoSQL for Data Science.
- [Instructor] As we conclude our discussion…of document databases, I just want to share…a few tips for working with these systems…of data science projects.…Embedded documents and hierarchical structures…are quite useful for denormalizing.…They keep related information together…in a logical structure.…This is helpful when trying to understand the meaning…of data within a document.…We use embedded documents to avoid having to…join data when we query collections.…Unfortunately, embedded documents are not a good fit…with the tabular structures commonly used…in machine learning and statistics.…
For this reason, it's helpful to flatten your structures…when you're loading data for analysis.…Feel free to add new features to your collections.…We can sometimes improve the quality…of our machine learning models by feature engineering.…For example, we can create features…based on the combination of attributes,…such as age and location.…Another common practice is normalizing numeric values…in a range of zero to one.…There may be some value in adding features…
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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