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Lead Instructor(s)
Date(s)
Jul 31 - Aug 03, 2023
Registration Deadline
Location
Live Virtual
Course Length
2 days
Course Fee
$2,500
CEUs
1.4

Graph analytics provides a valuable tool for modeling complex relationships and analyzing information. In this course, designed for technical professionals who work with large quantities of data, you will enhance your ability to extract useful insights from large and structured data sets to inform business decisions, accelerate scientific discoveries, increase business revenue, improve quality of service, detect fraudulent behavior, and/or defend against security threats. 

This course may be taken individually or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence.

Course Overview

Graphs have long been a fundamental way to model relationships in data across industries as diverse as IT, finance, transportation, telecommunications, and cybersecurity. Today, they are increasingly used in machine learning pipelines—enabling clustering for classification tasks, improving recommendation systems, ranking search results, and more. But as the sheer quantity of collected data has grown, so has the complexity of mapping these connections.   

As a result, the efficient processing of large graphs has attracted significant attention, due to its applications in various domains, including social network analysis, epidemiology, computational biology, machine learning, and scientific simulations. Today, graphs have become extremely large and are evolving rapidly in real-time — which has made designing graph analytics a major challenge.

This accelerated course provides a comprehensive overview of critical topics in graph analytics, including applications of graphs, the structure of real-world graphs, fast graph algorithms, synthetic graph generation, performance optimizations, programming frameworks, and learning on graphs. The curriculum additionally covers software performance engineering concepts, such as parallelism, caching, and compression, in the context of graph processing, as well as different design choices that will enable you to use or design the appropriate graph solutions for your needs. 

Through tutorials, exercises, and demonstrations featuring state-of-the-art graph analytics tools, you will broaden your fundamental understanding of graph analytics, and master the techniques and tools that you need to efficiently solve large-scale graph problems in your organization.

Learning Outcomes

By participating in this course, you will:

  • Learn how to model structured data with graphs
  • Enhance your understanding of real-world graph properties and how to generate synthetic graphs
  • Master fundamental graph algorithms
  • Discuss parallelism and how it can be used to speed up graph processing
  • Examine performance characteristics of graph algorithms
  • Assess the state-of-the-art graph processing tools available today and learn to use certain graph software
  • Explore the pros and cons of different graph processing approaches
  • Acquire a new set of tools for improving the effectiveness and performance of machine learning pipelines

Course Outline

This course runs 10:00am-5:30pm EDT each day. 

Day 1    
10:00-11:00 am: Introduction to Graph Theory and Applications of Graphs
11:00-11:15 am: Break
11:15 am-12:00 pm: Structure of Real-World Graphs (Part 1)
12:00-1:00 pm: Lunch Break
1:00-1:45 pm: Structure of Real-World Graphs (Part 2)
1:45-2:00 pm: Break
2:00-3:30 pm: Graph Algorithms (Part 1)
3:30-4:00 pm: Break
4:00-5:00 pm: Graph Algorithms (Part 2)
5:00-5:30 pm: Q&A

Day 2
10:00-11:00 am: Demo and Exercises with Graph Processing Software (NetworkX)
11:00-11:30 am: Break
11:30 am -1:00 pm: Large-Scale Graph Processing Frameworks
1:00-2:00 pm: Lunch Break
2:00-3:30 pm: Machine Learning on Graphs
3:30-4:00 pm: Break
4:00-5:00 pm: Problem Clinic
5:00-5:30 pm: Q&A

Who Should Attend

This course is designed for scientists and engineers in industry or government who work with large-scale data. The strategies covered are applicable to a variety of fields, such as software/IT, finance, transportation, biotech, telecommunications, and cybersecurity.

Applicable roles include, but are not limited to:

  • Data scientists who want to improve their ability to extract actionable insights from large and structured data sets
  • Software engineers looking to develop fast graph software 
  • Project managers who wish to increase their efficiency in overseeing technical teams working on graph-related projects and graph software products 
  • Any technical professionals who want to use data to inform business decisions, accelerate discoveries, detect fraud, defend against security threats, increase revenue, or improve service quality 

Requirements

Participants should have general knowledge of computer science at an undergraduate level, as well as some programming experience—Python and C++ are preferred. The NetworkX Python package will be used for some demonstrations and exercises, and so your laptop or computer must be compatible with the software.