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.
Этот курс входит в специализацию ''Специализация Большие данные'
Graph Analytics for Big Data
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Об этом курсе
Недавно просмотрено: 23,737
Приобретаемые навыки
Graph TheoryNeo4jAnalyticsGraph Database
Программа курса: что вы изучите
Неделя
14 минуты на завершение
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 видео ((всего 4 мин.))
1 видео
Неделя
23 ч. на завершение
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!
8 видео ((всего 38 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
8 видео
What is a Graph?7мин
Why Graphs?2мин
Why Graphs? Example 2: Biological Networks3мин
Why Graphs? Example 3: Human Information Network Analytics3мин
Why Graphs? Example 4: Smart Cities2мин
The Purpose of Analytics1мин
What are the impact of Big Data's V's on Graphs?12мин
2 материала для самостоятельного изучения
What to learn in this module10мин
Download Slides for this Module10мин
1 практическое упражнение
Introduction to Graphs24мин
Неделя
33 ч. на завершение
Graph Analytics
17 видео ((всего 81 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
17 видео
Path Analytics6мин
Applying Dijkstra's Algorithm5мин
Inclusion and Exclusion Constraints1мин
Connectivity Analytics3мин
Disconnecting a Graph1мин
Connectedness: Indegree and Outdegree3мин
Community Analytics and Local Properties7мин
Global Property: Modularity7мин
Centrality Analytics8мин
Optional Lecture 1: Bi-directional Dijkstra Algorithm2мин
Optional Lecture 2: Goal-directed Dijkstra Algorithm2мин
Optional Lecture 3: Power Law Graphs2мин
Optional Lecture 4: Measuring Graph Evolution3мин
Optional Lecture 5: Eigenvector Centrality6мин
Optional Lecture 6: Key Player Problems2мин
3 материала для самостоятельного изучения
What to learn in this module10мин
If this module takes a little longer... that's OK!10мин
Download All Slides for Module 310мин
2 практического упражнения
Graph Analytics Applications34мин
Connectivity, Community, and Centrality Analytics38мин
Неделя
44 ч. на завершение
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.
10 видео ((всего 78 мин.)), 12 материалов для самостоятельного изучения, 2 тестов
10 видео
Hands-On: Modifying a Graph With Neo4j7мин
Hands-On: Importing Data Into Neo4j13мин
Hands-On: Basic Queries in Neo4j With Cypher - Part 16мин
Hands-On: Basic Queries in Neo4j With Cypher - Part 26мин
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18мин
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211мин
Hands-On: Connectivity Analytics in Neo4j With Cypher9мин
12 материала для самостоятельного изучения
About the Supplementary Resources10мин
Downloading, Installing, and Running Neo4j - Supplementary Resources10мин
Getting Started With Neo4j - Supplementary Resources10мин
Adding to and Modifying a Graph - Supplementary Resources10мин
Download datasets used in this Graph Analytics with Neo4j10мин
Importing Data Into Neo4j - Supplementary Resources10мин
FAQ10мин
Basic Queries in Neo4j With Cypher - Supplementary Resources10мин
Path Analytics in Neo4j With Cypher - Supplementary Resources10мин
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10мин
Assignment: Practicing Graph Analytics in Neo4j With Cypher10мин
Download All Neo4j Supplementary Resources (PDFs)10мин
2 практического упражнения
Quiz: Graph Analytics With Neo4j18мин
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18мин
50%
начал новую карьеру, пройдя эти курсы
30%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие отзывы о курсе Graph Analytics for Big Data
Got an amazing introduction to Graph Analytics in Big Data. Technical issues with Neo4J made this course a little more challenging than necessary. But the introduction to Spark GraphX was invaluable.
This course was excellent as an introduction to Graph Analytics and using Neo4j. Not only did I learn a lot, I've been given tasks related to what I've learned in this course after finishing it.
Преподаватели
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
О Калифорнийский университет в Сан-Диего
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Специализация Большие данные: общие сведения
Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
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Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.
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