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# Graph Analytics for Big Data

6 Reviews 263 students interested
Part of the Big Data Specialization
• Provider Coursera
• Subject Big Data
• Cost Free Online Course (Audit)
• Session Upcoming
• Language English
• Certificate Paid Certificate Available
• Start Date
• Duration 5 weeks long

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## Overview

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

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.

Amarnath Gupta

## Reviews for Coursera's Graph Analytics for Big Data 2.5 Based on 6 reviews

• 5 star 17%
• 4 star 17%
• 3 star 17%
• 2 star 0%
• 1 stars 50%

Did you take this course? Share your experience with other students.

• 1
Anonymous
4.0 3 years ago
completed this course.
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, there's just so much this course can do.

But given the constraints, the course has successfully introduced all essential concepts in graph theory and analytics. That's the reason why I give 4 stars, for the thoroughness and the comprehensiveness of the introductory purpose.

- 1 star because there are too few practical assignments to be done in visualizing graphs and analyze them. Perhaps, a peer review assignments or project that puts together all the basic concepts into applications will be a great revision to the course.
3 people found
Eric L
5.0 3 years ago
by is taking this course right now, spending 20 hours a week on it and found the course difficulty to be medium.
The teacher and explanations is very good. One sugestion is use more tools for graph analytics for Big Data in this course.
0 person found
Colin K
1.0 3 years ago
by completed this course.
0 person found
Pawel K
3.0 2 years ago
by completed this course.
Anonymous
1.0 3 years ago
is taking this course right now.
1 person found
Pavel B
1.0 3 years ago
by is taking this course right now and found the course difficulty to be very easy.
1 person found
• 1

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