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University of California, San Diego

Graph Analytics for Big Data

University of California, San Diego via Coursera

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Overview

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Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

Syllabus

  • Welcome to Graph Analytics
    • Meet your instructor, Amarnath Gupta and learn about the course objectives.
  • Introduction to Graphs
    • Welcome! This week we will get a first exposure to graphs and their use in everyday life. By the end of the module you will be able to create a graph applying core mathematical properties of graphs, and identify the kinds of analysis questions one might be able to ask of such a graph. We hope the you will be inspired as to how graphical representations might enable you to answer new Big Data problems!
  • Graph Analytics
  • Graph Analytics Techniques
    • Welcome to the 4th module in the Graph Analytics course. Last week, we got a glimpse of a number of graph properties and why they are important. This week we will use those properties for analyzing graphs using a free and powerful graph analytics tool called Neo4j. We will demonstrate how to use Cypher, the query language of Neo4j, to perform a wide range of analyses on a variety of graph networks.
  • Computing Platforms for Graph Analytics
    • In the last two modules we have learned about graph analytics and graph data management. This week we will study how they come together. There are programming models and software frameworks created specifically for graph analytics. In this module we'll give an introductory tour of these models and frameworks. We will learn to implement what you learned in Week 2 and build on it using GraphX and Giraph.

Taught by

Amarnath Gupta

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Reviews

2.5 rating, based on 6 Class Central reviews

4.3 rating at Coursera based on 1260 ratings

Start your review of Graph Analytics for Big Data

  • Anonymous
    An introduction to graph theory, at best. But a very detailed and thorough introduction, at least. Don't expect to be a master in graph theory after taking this course. Graph theory is not an easy field and without discussing the mathematics, ther…
  • Profile image for Eric Gabriel Bellet Locker
    Eric Gabriel Bellet Locker
    The teacher and explanations is very good. One sugestion is use more tools for graph analytics for Big Data in this course.
  • Colin Khein
  • Pawel Krzysztofik
  • Anonymous
  • Profile image for Pavel Baryshnikov
    Pavel Baryshnikov
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