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In this course, you will :
- Learn the distinctions between relational and NoSQL databases, as well as the various types of NoSQL databases, and see how to perform common data science tasks such as data preparation, exploration, and model building and application.
- The course starts with an overview of NoSQL before delving into the specifics of document, wide-column, and graph databases.
- Learn the essentials of data preparation, exploration, and extraction for each NoSQL database type.
- Examine case studies that demonstrate how to use popular data science tools with various NoSQL databases, such as the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
Syllabus :
1. Why NoSQL?
- The limits of relational databases
- Types of NoSQL databases
- Advantages of NoSQL databases
- Performing data science tasks with NoSQL
2. Perform Common Data Science Tasks with NoSQL Databases
- Preparing data
- Exploring data
- Building models
- Applying models
3. Document Databases for Data Science
- Document data models
- JSON structures
- Prepare data with document databases
- Install Anaconda
- Install MongoDB
- Working with Jupyter
- Explore data with document databases
- Extract data with document databases
- Perform quality checks
- Index data with document databases
- Data frames in MongoDB
4. Wide-Column Databases for Data Science
- Wide-column data models
- Prepare data with wide-column databases
- Install the Java Development Kit
- Install Cassandra
- Prepare data for Cassandra
- Load data into Cassandra
- Cassandra and Spark
5. Graph Databases for Data Science
- Graph data models
- Key graphi concepts
- Prepare data with graph databases
- Install Neo4j
- Explore data with graph databases
- Extract data with graph databases
