Taught In:
Professional training
Levels:
Level of expertise and familiarity the programming experience, Python and C++ are preferred. The Network-X Python package will be used for some demonstrations and exercises, and so your PC or Mac must be compatible with the software.
Participants:
With the requirements in the “Level” section, means participants should have general knowledge of Computer Science at an undergraduate level.
This course will be useful to scientists, and engineers in industry or legal administrative 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. But not limited to data scientist, software engineers, project managers, and some 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.
Course Overview:
The type of course, whether it’s a foundational understanding of the material, the hottest trends, and developments in the field, or suggested practical applications for industry.
Analytics of graph 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, learner 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 defend against security threats.
We do know that 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.
That indicate 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.
Methods:
The course is taught from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers. An interactions by demonstrations, experiments and simulations itself.
Outcomes:
The fundamentals of algorithms diagram and machine learning of data modeling and analytics of software, finances, transportation, telecommunication, biotech, and cyber security.
- Awareness on modeling structure data with graphs.
- Enhance learner understanding of real-world graph properties and how to generate synthetic graphs
- Master the fundamental graph algorithms
- Sharing understanding of 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
- Find the meaning of 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
Topic Outline:
Lec 01 – Introduction to Graph Theory and Applications of Graphs
Lec 02 – Structure of Real-World Graphs
Lec 03 – Graph Algorithms
Lec 04 – Demo and Exercises with Software of Graph Processing
Lec 05 – Large-Scale Graph Processing Frameworks
Lec 06 – Machine Learning on Graphs
Lec 07 – Case Problem Clinic