Advanced NoSQL for Data Science

Course details

Many organizations are turning to NoSQL databases to store large volumes of complex data, sparking an increased need for data scientists and analysts to understand non-relational data stores. If you're a data scientist or business analyst who needs to work with NoSQL, then this course is for you. Learn about the differences between relational and NoSQL databases, review types of NoSQL databases, and see how to perform common data science tasks, including data preparation, exploration, and building and applying models.

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.

Learning objectives
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

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Exercise files

1. Why NoSQL

04 - The limits of relational databases
05 - Types of NoSQL databases
06 - Advantages of NoSQL databases
07 - Performing data science tasks with NoSQL

2. Perform Common Data Science Tasks with NoSQL Databases

08 - Preparing data
09 - Exploring data
10 - Building models
11 - Applying models

3. Document Databases for Data Science

12 - Document data models
13 - JSON structures
14 - Prepare data with document databases
15 - Install Anaconda
16 - Install MongoDB
17 - Working with Jupyter
18 - Explore data with document databases
19 - Extract data with document databases
20 - Perform quality checks
21 - Index data with document databases
22 - Data frames in MongoDB
23 - Tips for using document databases for data science

4. Wide-Column Databases for Data Science

24 - Wide-column data models
25 - Prepare data with wide-column databases
26 - Install the Java Development Kit
27 - Install Cassandra
28 - Prepare data for Cassandra
29 - Load data into Cassandra
30 - Cassandra and Spark
31 - Tips for using wide-column databases for data science

5. Graph Databases for Data Science

32 - Graph data models
33 - Key graphi concepts
34 - Prepare data with graph databases
35 - Install Neo4j
36 - Explore data with graph databases
37 - Extract data with graph databases
38 - Tips for using graph databases for data science

Conclusion

39 - Next Steps