Join Dan Sullivan for an in-depth discussion in this video Exploring data, part of Advanced NoSQL for Data Science.
- [Instructor] When starting any kind of analytics project,…it can help to get a feel for your data.…Here are three techniques…to help you better understand your data…before you begin detailed analysis.…Descriptive statistics are widely used…to understand the range of values…that a numeric attribute can take on.…So for example, a bank's customers might range in age…from a minimum of 20 to a maximum of 95.…Well that's good to know, but it doesn't tell us much…about how the data's spread out between those endpoints.…Now, the Average, which is also called the Mean,…gives us the midpoint value of all ages…while the Median gives us the age at which…there are just as many younger customers…as they are older customers.…
Another useful statistic is called the Standard Deviation.…It's a measure of how spread out the data is.…When most of the data tends to the middle…and drops off equally above and below the average,…then the data has a bell curve shape.…You might also hear this called the normal distribution.…We need to be careful when we're using…
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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