Join Dan Sullivan for an in-depth discussion in this video Perform quality checks, part of Advanced NoSQL for Data Science.
- [Narrator] Okay, we're starting where we left off,…I have loaded data from the income file…and what I'd like to do now…is just verify that we've loaded it correctly.…So, what I'd like to do is…maybe look up somebody who's age 39.…Because the first line of the data file…is about someone who is age 39,…so this should be fairly simple to find.…I'm going to define a variable called age 39…I'm going to go to my collection…I'm going to 'find_one',…then I'm going to specify…that the age is 39.…There.…Now that variable should have…data about that person.…
Now, I just need to execute.…Wait a minute, there's no value.…What went wrong?…Let's do a little debugging.…You might remember…I created a variable called 'row_dict' dictionary list.…That's a list,…and the first item in the list…will be the first row that we saw.…And since Python numbers from zero,…we use an index of zero to get the first item of the list.…And we see there is an object there,…the age is 39,…but you'll notice, it's not an integer, it's a string.…So, we need to change our…
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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