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Graph Analytics for Big Data

This course is a part of Big Data, a 7-course Specialization series from Coursera.

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.
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Coursera

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University of California San Diego

Rating 3.8 based on 156 ratings
Length 6 weeks
Effort 4 Weeks, 3-5 hours/week
Starts Apr 22 (5 weeks ago)
Cost $89
From University of California San Diego via Coursera
Instructor Amarnath Gupta
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

graph analytics in 17 reviews

This module introduced a lot of technical knowledge and theory around Graph Analytics.

Some items are interesting, but the lectures seem goal-less: not enough structure to help understand graph analytics.

Good course overall for concepts of graph analytics.Too much time spent setting up and going through the hand-on exercises.

I like the content at a high level, and I have a better appreciation for the value of graph analytics.

Probably because I'm new to all the big data and graph analytics terminologies but the lectures in this chapter were quite difficult to follow in my opinion.

Good Course to get introduced to Graph Analytics , Neo4j, Cypher Graph Query Language , GraphX through Spark.

Graph Analytics should be its own 6 course specialization.

But really, to understand graph analytics one should be spending a great deal of time with the subject.

big data in 15 reviews

One sugestion is use more tools for graph analytics for Big Data in this course.

great course for those who want to learn graph analytics to use for the assessment of big data.

I learned a lot about graph techniques and tools for application to big data problems.

My problem with this course is the same that with the Machine learning course: it is too basic, and the big data aspects were more a demo than a real course.

not much explanation about the syntax of the commands given in excercises Great course to get familiar with graph data, graph big data, and query languages in graph data.

A good topic to continue building upon big data with Graphs, however Week 5 needs some more understandable examples and exercises.

This is one of the toughest and most interesting course at it deals with big data in graph database.

Thanks to this program, I was able to obtain a job as a data scientist in a big data environment.

very interesting in 8 reviews

Very interesting topic, but poor course.

g Very interesting course, it covered what i was expecting.

This course is very interesting and well done.

Neo4j is very interesting but it is poorly explained.

Very interesting class, large applications possibilities, unfortunately the hands on machine wont work properly The concepts are brilliantly taught but the technical part that was programming was rushed through and not explained step by step.

interesting practices with neo4j It was very interesting and helpful.

Very interesting though!

Very interesting and well explained, but little practical.

introduction to graph in 6 reviews

An introduction to graph theory, at best.

Got an amazing introduction to Graph Analytics in Big Data.

I suggest to rename this course to something like "Introduction to graph and network analysis with a big-data demo" not to mislead users.

It was perfect course This is a really good course as an introduction to Graph analytics as well as the types of analyses and techniques used in graph analytics.

This is probably out of scope for this course but would have been good to get a little more technical training - Thanks so much you are great people The neo4j parts were good, and nice introduction to graph terminology and concepts.

This course was excellent as an introduction to Graph Analytics and using Neo4j.

really enjoyed in 4 reviews

very good Really enjoyed the Neo4j module.

Really enjoyed the Neo4j and GraphX Hands On Displaying handson queries graphically would make it more appealing as it was in Neo4j Good approach for introducing to graph analytics All the complexity of the course is concentrated to the last week.

Really enjoyed hands-on exercises The professor is well spoken and the class started out very well.

I really enjoyed learning graph analytics, great course!

hands-on exercises in 4 reviews

Complex theory is also explained very quickly and disconnected from the hands-on exercises.

The hands-on exercises in Graphx introduced a lot of unfamiliar code and seemed very technical but I believe that it is needed.

Can be improved further by including rigorous Hands-on exercises Excellent course for beginners in graph analytics.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Customer Graph Digital Data Scientist $61k

Graph Database Developer $76k

Analytics/F&B Management $77k

Global Analytics $89k

Analytics Scientist $89k

Business Analytics & Advanced Analytics $90k

Client Analytics $91k

Analyst, Analytics $94k

Graph Database Engineer $103k

BI & Analytics $106k

Mobile Analytics $116k

Senior Analytics $198k

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Coursera

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University of California San Diego

Rating 3.8 based on 156 ratings
Length 6 weeks
Effort 4 Weeks, 3-5 hours/week
Starts Apr 22 (5 weeks ago)
Cost $89
From University of California San Diego via Coursera
Instructor Amarnath Gupta
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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