laurakarimova / massive_graph_management_and_analytics

This repository contains folders with laboratory work performed during the course at the University of Paris Saclay.

Massive graph management and analytics

This repository contains folders with laboratory work performed during the course at the University of Paris Saclay.

Table of Contents

• General Info
• Technologies Used
• Contact

General Information

The objectives of this course is to provide the student with knowledge about designing high-performance and scalable algorithms for massive graph analytics. The course focuses on modeling and querying massive graph data in a distributed environment, designing algorithms, complexity analysis and optimization, for massive data graph problem analytics. Upon successful completion of this course, the student is able to:

• model and query massive graph data in a distributed environment
• design and analyse efficient graph algorithms in real-world data-intensive applications;
• develop efficient applications using the best practices in a distributed environment (Spark, MapReduce, Neo4J, GraphX, etc.).

Technologies Used

• Python - version 3.8
• Anaconda - version 2020.11

Contact

Created by @LauraKarimova - feel free to contact me!