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Graph Analytics for Big Data

Amarnath Gupta

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.

Read more

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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What's inside

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!
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
- Introduces key concepts and techniques in graph analytics, which is becoming increasingly important in various industries
- Led by experienced instructor Amarnath Gupta, who is well-regarded for their expertise in graph analytics
- Provides practical examples and hands-on exercises using the popular graph database Neo4j, enhancing the applicability of the concepts learned
- Incorporates industry-standard programming models and frameworks, such as GraphX and Giraph, for building scalable graph analytics applications
- Lacks up-to-date information on the latest advancements and trends in graph analytics
- May require students to have some prior experience with graph theory and data structures to fully benefit from the course

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Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Graph analytics for big data overview

According to learners, this course provides a positive overview of graph analytics concepts, offering a solid introduction to modeling and analyzing graph data. Many found the section on Neo4j and Cypher practical and valuable for hands-on experience. However, some students noted challenges with the later modules covering distributed platforms like GraphX and Giraph, finding them difficult to understand or set up. The course is generally seen as a good foundational step, best suited for those with some technical background, and covers a mix of theory and practical tools.
Best suited for those with technical skills.
"This course isn't for absolute beginners; some prior programming or data analysis experience is definitely recommended."
"I found it challenging at times, and my background in computer science was necessary."
"Having some familiarity with big data concepts helps in the later modules."
Provides a broad look at the field.
"It gives a good overview of different aspects of graph analytics, from concepts to tools."
"I feel like I have a much better understanding of the landscape of graph analytics now."
"The course covers a wide range of topics, which is great for getting started in the field."
The initial modules introduce concepts well.
"The course starts with a clear and helpful introduction to graph fundamentals and terminology."
"I found the initial explanation of graph concepts very easy to follow and understand."
"Great overview of what graphs are and how they can be applied to data problems."
Hands-on experience with Neo4j is valuable.
"The section on using Neo4j and the Cypher query language was incredibly useful and practical."
"Working through the Neo4j examples was the most helpful part for applying concepts."
"Learning Cypher gave me a concrete tool to start querying graph databases."
"I really appreciated the practical labs using Neo4j; it made the theory click."
Later distributed platforms were difficult.
"I struggled significantly with the later modules on GraphX and Giraph; they felt much harder than Neo4j."
"Setting up and understanding the examples for GraphX and Giraph was a major hurdle for me."
"The transition to distributed platforms in the final weeks felt abrupt and lacked depth compared to earlier sections."
"Could use more detailed guidance or troubleshooting help for the GraphX/Giraph labs."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Graph Analytics for Big Data with these activities:
Graph Databases: Concepts and Applications
Gain a foundational understanding of graph databases and their applications by reading and comprehending this book.
Show steps
  • Read the book
  • Take notes and highlight important concepts
  • Complete the exercises and assignments in the book
Graph Analytics Resources Collection
Expand your understanding of graph analytics by compiling a list of relevant resources, including articles, tutorials, and tools.
Browse courses on Graph Analytics
Show steps
  • Search for online resources related to graph analytics
  • Evaluate the resources and select those that are most relevant and informative
  • Organize the resources into a curated collection
Graph Analytics with NetworkX Tutorial
Enhance your practical skills in graph analytics by following a guided tutorial on using the NetworkX library.
Browse courses on Graph Analytics
Show steps
  • Install NetworkX
  • Follow the tutorial to learn how to create and manipulate graphs
  • Experiment with different graph algorithms and techniques
Five other activities
Expand to see all activities and additional details
Show all eight activities
Identify Graph Patterns Practice
Develop your ability to recognize and identify patterns in graphs by engaging in targeted practice drills.
Show steps
  • Find online resources or create your own graph pattern exercises
  • Practice identifying different types of graph patterns
  • Test your skills by completing practice quizzes or challenges
Drill Neo4j Queries
Help yourself solidify your understanding of Neo4j queries by practicing and experimenting with writing different queries.
Browse courses on Neo4j
Show steps
  • Create a sample graph in Neo4j
  • Practice writing Cypher queries to retrieve nodes and relationships from the graph
  • Experiment with different query operators and functions
Graph Analysis Discussion Group
Engage with peers to discuss and share knowledge about graph analysis techniques and best practices.
Show steps
  • Join or create a discussion group focused on graph analysis
  • Participate in discussions, ask questions, and share insights
  • Collaborate with others on graph analysis projects
Design a Graph Model for a Real-World Problem
Enhance your problem-solving skills by designing a graph model to represent a real-world problem, demonstrate how to store and retrieve data using the model.
Show steps
  • Identify a real-world problem that can be represented as a graph
  • Design a graph schema to represent the entities and relationships in the problem
  • Implement the graph model using a graph database
  • Store and query data using the graph model
  • Evaluate the performance and scalability of the graph model
Contribute to an Open-Source Graph Analytics Project
Enhance your practical skills and contribute to the community by actively participating in an open-source project in the field of graph analytics.
Browse courses on Open Source
Show steps
  • Identify an open-source graph analytics project that aligns with your interests
  • Review the project's documentation and codebase
  • Identify an area where you can contribute
  • Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements
  • Collaborate with other contributors and maintainers

Career center

Learners who complete Graph Analytics for Big Data will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their knowledge of data analysis tools and techniques to extract meaningful insights from data. They may work with structured or unstructured data, and they may use a variety of statistical and machine learning techniques to identify trends and patterns. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Data Analyst. This course will teach you how to model data into a graph database, perform analytical tasks over the graph in a scalable manner, and apply these techniques to understand the significance of your data sets for your own projects.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists use their knowledge of data science techniques to solve business problems. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Data Scientist. This course will teach you how to apply graph analytics techniques to identify patterns and trends in data, which can help you solve business problems more effectively.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning techniques to develop and deploy machine learning models. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Machine Learning Engineer. This course will teach you how to use graph analytics techniques to identify patterns and trends in data, which can help you develop more accurate and effective machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work with a variety of programming languages and technologies. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Software Engineer. This course will teach you how to use graph analytics techniques to design and develop more efficient and scalable software applications.
See salaries and explore the career path for Software Engineer
Database Administrator
Database Administrators design, implement, and maintain databases. They may work with a variety of database management systems. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Database Administrator. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable databases.
See salaries and explore the career path for Database Administrator
Business Analyst
Business Analysts use their knowledge of business and data analysis techniques to identify and solve business problems. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Business Analyst. This course will teach you how to use graph analytics techniques to identify patterns and trends in data, which can help you solve business problems more effectively.
See salaries and explore the career path for Business Analyst
Data Architect
Data Architects design and implement data management solutions. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Data Architect. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable data management solutions.
See salaries and explore the career path for Data Architect
Information Architect
Information Architects design and implement information systems. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Information Architect. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable information systems.
See salaries and explore the career path for Information Architect
Knowledge Engineer
Knowledge Engineers design and implement knowledge management systems. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Knowledge Engineer. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable knowledge management systems.
See salaries and explore the career path for Knowledge Engineer
Ontologist
Ontologists develop and maintain ontologies. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Ontologist. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable ontologies.
See salaries and explore the career path for Ontologist
Taxonomist
Taxonomists develop and maintain taxonomies. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Taxonomist. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable taxonomies.
See salaries and explore the career path for Taxonomist
Data Governance Analyst
Data Governance Analysts develop and implement data governance policies and procedures. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Data Governance Analyst. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable data governance policies and procedures.
See salaries and explore the career path for Data Governance Analyst
Metadata Manager
Metadata Managers develop and maintain metadata repositories. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course can help you develop the skills you need to be a successful Metadata Manager. This course will teach you how to use graph analytics techniques to design and implement more efficient and scalable metadata repositories.
See salaries and explore the career path for Metadata Manager
Librarian
Librarians organize and manage information resources. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course may be useful for you if you are interested in becoming a Librarian. This course will teach you how to use graph analytics techniques to organize and manage information resources more efficiently and effectively.
See salaries and explore the career path for Librarian
Archivist
Archivists preserve and manage historical records. They may work with a variety of data sources, including structured and unstructured data. The Graph Analytics for Big Data course may be useful for you if you are interested in becoming an Archivist. This course will teach you how to use graph analytics techniques to preserve and manage historical records more efficiently and effectively.
See salaries and explore the career path for Archivist

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Graph Analytics for Big Data.
Cover image
Graph Databases
Save
Offers a deep dive into graph databases, providing theoretical grounding and practical advice. Useful for gaining a broader understanding of the theory and applications of graph databases.
Graph Databases: New Opportunities for Connected...
Paperback
$$
Graph Databases
Paperback
$$
Graph Databases: New Opportunities for Connected...
Kindle Edition
$$
Cover image
The Analytics Revolution
Save
Explores the fundamentals of graph analytics, focusing on practical applications and case studies. Offers foundational knowledge for understanding the concepts and techniques covered in the course.
The Analytics Revolution: How to Improve Your...
Kindle Edition
$$
Cover image
Modern Machine Learning Techniques and Their...
Save
Offers a deep dive into graph-based semi-supervised learning methods. Useful for understanding the advanced techniques used in graph analytics for data classification and prediction.
Modern Machine Learning Techniques and Their...
Hardcover
$$$
Cover image
Data Mining
Save
Offers a comprehensive overview of data mining concepts and techniques. Provides a foundation for understanding the data mining techniques used in graph analytics.
Data Mining: Concepts and Techniques (The Morgan...
Paperback
$$$
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Cover image
Social Network Analysis
Save
Provides a comprehensive overview of network analysis methods and their applications in various fields. Serves as a reference guide for understanding the theoretical underpinnings and methodologies used in graph analytics.
Social Network Analysis: Methods and Applications...
Paperback
$$
Social Network Analysis: Methods and Applications...
Kindle Edition
$$
Cover image
Visual Thinking for Information Design
Save
Provides a comprehensive overview of information visualization techniques and principles. Useful for understanding the visualization techniques used in graph analytics to represent and interpret data.
Visual Thinking for Information Design
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
Offers a comprehensive introduction to machine learning, including supervised and unsupervised learning techniques. Provides foundational knowledge for understanding the machine learning algorithms used in graph analytics.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
An Introduction to Data Structures and Algorithms
Save
Provides a foundational understanding of graph theory concepts and algorithms. Useful for gaining a deeper understanding of the mathematical underpinnings of graph analytics.
An Introduction to Data Structures and Algorithms
Hardcover
$$
An Introduction to Data Structures and Algorithms...
Kindle Edition
$$
Cover image
Data Science for Business
Save
Provides a general overview of data science concepts and techniques, including data mining. Offers insights into the broader context of data analytics and its applications in various business domains.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning techniques and architectures. Useful for understanding the potential applications of deep learning in graph analytics.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Hadoop: The Definitive Guide
Save
Offers a comprehensive guide to Hadoop, a widely used framework for big data processing. Provides background on the Hadoop ecosystem and its applicability to graph analytics.
Hadoop: The Definitive Guide: Storage and Analysis...
Paperback
$$
Hadoop: The Definitive Guide
Kindle Edition
$$
Hadoop: The Definitive Guide
Paperback
$$
Hadoop: The Definitive Guide
Paperback
$$
Cover image
Natural Language Processing with Python
Save
Offers a practical guide to natural language processing (NLP) techniques using Python. Can be useful for understanding the NLP techniques used in graph analytics for text data.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
(中文) Python Natural Language Processing(Chinese Edition)
Paperback
$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$

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Course image
Effort
4 Weeks, 3-5 hours/week
Via
Coursera
Institution
University of California San Diego
Instructor
Amarnath Gupta
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Big Data. It's comprised of seven courses:
  1. Graph Analytics for Big Data (viewing)
  2. Big Data - Capstone Project
  3. Introduction to Big Data
  4. Machine Learning With Big Data
  5. Big Data Integration and Processing
  6. Big Data Modeling and Management Systems
  7. Programming for Data Science
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
- Introduces key concepts and techniques in graph analytics, which is becoming increasingly important in various industries
- Led by experienced instructor Amarnath Gupta, who is well-regarded for their expertise in graph analytics
- Provides practical examples and hands-on exercises using the popular graph database Neo4j, enhancing the applicability of the concepts learned
- Incorporates industry-standard programming models and frameworks, such as GraphX and Giraph, for building scalable graph analytics applications
- Lacks up-to-date information on the latest advancements and trends in graph analytics
- May require students to have some prior experience with graph theory and data structures to fully benefit from the course
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