Join Dan Sullivan for an in-depth discussion in this video Install Anaconda, part of Advanced NoSQL for Data Science.
- [Instructor] In this video, we're going…to download and install Anaconda Python.…Anaconda Python is an open-source distribution…that includes many libraries used in data science.…It also includes the Jupiter Notebook, which is a…widely used interactive environment for data science.…Now, I'm working on a Mac, so I'll…download the installer for OS X.…You can chose whichever is appropriate for you.…Now, there are two versions of Python,…Python Three and Python Two.…We're using Python Two, so we'll click…on the graphical installer for that.…
Now, we're using Python version two,…because many popular libraries…are still being rewritten for Python three,…so we want to make sure that we have full access to a lot…of the libraries that are out there available for us.…Now, I'm going to open the package…by first showing it in Finder.…I'm going to double-click on the Anaconda package,…and this will open up a Wizard to guide me…through the installation process.…Now, if you're on Windows, you'll probably see…something different, but it should be fairly similar.…
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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