Graph Analytics for Big Data

Go to Course: https://www.coursera.org/learn/big-data-graph-analytics

Introduction

### Course Review: Graph Analytics for Big Data **Introduction** In our rapidly evolving digital landscape, the ability to analyze complex data networks is paramount. “Graph Analytics for Big Data” on Coursera offers an invaluable introduction to the field of graph analytics, tailored for individuals who wish to understand and leverage the intricate structures within their data. Whether you are a data scientist, business analyst, or a curious learner, this course provides the foundational knowledge you need to effectively model, store, retrieve, and analyze graph-structured data. **Overview of the Course** The course is expertly led by Amarnath Gupta, a seasoned instructor in the field. It is designed to equip students with a comprehensive understanding of graph analytics, addressing critical questions such as how data networks behave under different conditions and how to identify clusters that deeply interact within those networks. Upon completion, learners will be adept at transforming various problems into graph database structures and leveraging analytical techniques to extract meaningful insights. **Course Syllabus Breakdown** 1. **Welcome to Graph Analytics** - The course kicks off with an introduction from Amarnath Gupta, outlining the course objectives and setting the stage for the transformative journey ahead. 2. **Introduction to Graphs** - This module offers an engaging introduction to graphs and their relevance in everyday scenarios. Learners will gain hands-on experience in creating graphs, applying core mathematical properties. This foundational knowledge sparks inspiration on how graphical representations can be utilized to tackle Big Data challenges. 3. **Graph Analytics** - Building on the introduction, this module delves deeper into the key principles of graph analytics. Participants begin to grapple with various graph properties, exploring how these can be harnessed to extract insights from complex datasets. 4. **Graph Analytics Techniques** - Here, students are introduced to Neo4j, a leading graph analytics tool. The module emphasizes hands-on practice using Cypher, Neo4j's query language. This session is particularly enlightening, allowing learners to apply their grasp of graph properties in real-world scenarios, analyzing diverse graph networks effectively. 5. **Computing Platforms for Graph Analytics** - The final module integrates the previous lessons, exploring programming models and software frameworks tailored for graph analytics. Students will gain exposure to advanced systems like GraphX and Giraph, cementing their understanding of how to implement what they've previously learned. **Final Thoughts and Recommendation** “Graph Analytics for Big Data” serves as an exceptional primer for those interested in exploring the dynamic world of graph analytics. Its engaging content, hands-on approach, and clear instruction by Amarnath Gupta make it accessible for learners at various levels. The course not only equips you with theoretical understanding but also emphasizes practical skills using industry-standard tools like Neo4j, which is essential for any data professional. Whether you’re seeking to enhance your data analysis capabilities, pivot your career towards data science, or simply satisfy your curiosity about graph networks, this course is highly recommended. ### Who Should Enroll? - Data Analysts - Data Scientists - Business Analysts - Students of Mathematics and Computer Science - Anyone interested in the field of data analytics and its applications ### Conclusion In a world where data connectivity and relationships matter more than ever, mastering graph analytics is a leap forward. “Graph Analytics for Big Data” on Coursera is not just a course; it is a stepping stone towards mastering the skills needed to decode complex datasets and derive meaningful insights. Enroll today and embark on your journey into the fascinating realm of graph analytics!

Syllabus

Welcome to Graph Analytics

Meet your instructor, Amarnath Gupta and learn about the course objectives.

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!

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.

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.