Data Science Solutions: Machine Learning. Python. Google Cloud. AWS. Neo4j. Kaggle. by Manav Sehgal

Author:Manav Sehgal [Sehgal, Manav]
Language: eng
Format: epub
Publisher: Startupsci
Published: 2017-02-06T08:00:00+00:00

Data Science Graph

In this chapter we will make our Data Science Graph way more useful. We will refactor our graph through the seven stages of data science solutions workflow - Question, Acquiring, Wrangle, Analyze, Model, Visualize, and Supply. By the end of this chapter you will end up creating a live knowledge graph for your data science projects. A knowledge graph which you will grow with every project you complete. A graph you can query to discover datasets used within your projects and the decisions you took along your data science workflow.

Coding Goals

You can code along instructions in this chapter. The code, import data, and Data Science Graph database in Neo4j is available for download at a dedicated GitHub repository.

https://github.com/Startupsci/data-science-graph-code

You can clone this database in a local folder and start your Neo4j community server and Browser on this folder. Login is neo4j and password is demo for this database.

The Python notebooks used in this chapter are available at a dedicated GitHub repository.

https://github.com/Startupsci/data-science-notebooks/

Learning Goals

Learning goals for this chapter will cover advanced topics for using your graph database across the data science solutions workflow.

Learn about graph refactoring.

Ask questions about database information.

Refactor the entire graph database using four Cypher statements.

Create question and acquiring workflows.

Perform data wrangling workflows using Cypher.

Analyze and visualize workflows using Cypher.

Python Neo4j integration for model and supply workflows.

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