Join Dan Sullivan for an in-depth discussion in this video Performing data science tasks with NoSQL, part of Advanced NoSQL for Data Science.
As for working with data science applications,…it's important to keep in mind some of the common tasks…that we have to deal with,…and how that relates to NoSQL.…One of the most time consuming parts of data science…and other business intelligence activities…is data preparation.…Collecting data is the first step.…Often times we'll need to collect data…from multiple types of systems,…like application servers, databases,…lab files, sometimes even Excel spread sheets,…and once we've got those consolidated,…we often have to filter the data.…
This is especially true when we deal with streaming data…and large volume data, such as logs,…and then finally, the last big step…that we have to keep in mind…is we have to restructure this data…once we've collected it and filtered it.…Sometimes this means mapping it into tabular structures.…Sometimes it means mapping it into document structures.…The actual output of the restructured data…really depends on the application…that we're working toward.…Now it's also important to note…
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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