Join Dan Sullivan for an in-depth discussion in this video Exercise files, part of Advanced NoSQL for Data Science.
- [Instructor] If you have access to the exercise files for this course, you can follow along. The exercise files are organized into folders, one for each chapter that has an exercise file. Each chapter folder is further organized into videos. Each video that has an exercise file has a folder. Within that folder, you'll see different types of files. These include data files, which may be text files, json files, or other types, and then also several files of type ipynb.
These are Jupiter Notebooks and they won't work correctly until we install Jupiter Notebook software, which we will do during this 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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