Announcing Data Science with Neo4j And Applied Graph Algorithms Online Training Courses

Hot on the heels of the recently revamped Intro to Neo4j and Neo4j Administration training courses, we’re happy to announce the launch of two new Neo4j Online courses: Data Science with Neo4j and Applied Graph Algorithms.

These free Neo4j training courses introduce you to using Neo4j as part of your Data Science and Machine Learning workflows.

You’ll learn how to do this with the help of the citation dataset, a dataset that contains papers, authors and citations from DBLP – a computer science bibliography website, and the Yelp public dataset, with data on businesses and user reviews.

Why did you create these new training courses?

Over the last 12-18 months, we’ve had more and more people ask us how graphs can improve their data science and machine learning workflows, so we wanted to create courses that satisfied this need, both for data scientists and application developers who want to take advantage of this functionality.

Who are these courses for?

We’ve designed the Data Science course for data scientists and data analysts, but application developers may also find it interesting, and having some Python experience will be helpful. For the Applied Graph Algorithms course, we’ve focused on showing how application developers can use graph algorithms like PageRank, community detection, and similarity metrics to enhance their applications.

What will users learn?

  • Data Science Course
    • Query a database for its schema
    • Return and chart the number of node labels and relationship types using matplotlib
    • Build and plot a histogram of papers and their citations using pandas and matplotlib
    • Build a mini recommendation engine with Cypher queries to:
        • find potential collaborators for an author
        • find relevant papers about a topic for an author
    • Describe what link prediction is
    • Use the link prediction functions in Neo4j
    • Understand the challenges when building machine learning models on graph data
    • Build a link prediction classifier using scikit-learn with features derived from the Neo4j Graph Algorithms library
  • Applied Graph Algorithms Course
    • How to use Neo4j Graph Algorithms
    • Learn about the Overlap Similarity algorithm and how to use it in Neo4j to build a hierarchy of categories.
    • Enhance our business reviews application using Overlap Similarity to improve business search.
    • Learn about Similarity algorithms in Neo4j.
    • Use Pearson Similarity to improve search result ordering in our business reviews application.
    • Learn about PageRank and Personalized PageRank.
    • Use Personalized PageRank to find more relevant business reviews for users.
    • Learn about Community Detection and the Label Propagation algorithm.
    • Use Jaccard Similarity and Label Propagation to build a photo based business recommendation feature.

How long will it take to complete the courses?

Each course contains between 2 ½ – 3 hours worth of material, with exercises to test your learning at the end of each section.


Sounds interesting. How do I get started?

You can find the courses on the Graph Academy home page, or click one of the buttons below to get started right away:

Register for our free online training classes, Data Science with Neo4j and Applied Graph Algorithms and you’ll add the power of graphs to your data science toolbox.