- [Instructor] We're starting off…where we left off in the last video.…What I'd now like to do is load some data using json.…So, the first thing I'm going to do…is import a json library.…Now, I'm going to use the with open command…to open my json file, which is called companies.json,…and I'm going to refer to that file as f_in for file input,…and I'm going to use the json load command…to read that file in.…Now, I want to save the contents of the file…into a variable, so I'm going to use the variable companies.…
Now, let's double check on the type of companies.…It's a list, that's what I expected.…So let's take a look at the first element in that list.…Okay, it's got information about a company, as we'd expect.…Now, that certainly looks like a dictionary,…but just to make sure, I'm going to execute the type command…on the first element and the companies list.…Yup, it's a dictionary.…Now, the next thing I want to do…is execute a very simple insert statement…to insert one company record,…and we'll use the information we just saw…
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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