Coursera

Graph Analytics for Big Data (Coursera)

Offered by University of California, San Diego,
Graph Analytics for Big Data (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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.

Course 5 of 6 in the Big Data Specialization.

Syllabus

WEEK 1
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.

WEEK 2
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!

WEEK 3
Graph Analytics

WEEK 4
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.

WEEK 5
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Code Free Data Science (Coursera) Coursera
University of California, San Diego

Code Free Data Science (Coursera)

Data Science

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data.

Nov 23rd 2026
4 Weeks
Machine Learning Big Data Data Science
Details
HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) (Coursera) Coursera
Columbia University

HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) (Coursera)

Health & Society Medicine & Pharmacology

HI-FIVE (Health Informatics For Innovation, Value & Enrichment) Training is an approximately 10-hour online course designed by Columbia University in 2016, with sponsorship from the Office of the National Coordinator for Health Information Technology (ONC). The training is role-based and uses case scenarios. No additional hardware or software are required for this course. Our nation’s healthcare system is changing at a rapid pace.

Nov 23rd 2026
4 Weeks
Health Innovation Healthcare
Details
Statistics and Data Analysis with Excel, Part 1 (Coursera) Coursera
University of Colorado Boulder

Statistics and Data Analysis with Excel, Part 1 (Coursera)

Statistics & Data Analysis Data Science

Designed for students with no prior statistics knowledge, this course will provide a foundation for further study in data science, data analytics, or machine learning. Topics include descriptive statistics, probability, and discrete and continuous probability distributions. Assignments are conducted in Microsoft Excel (Windows or Mac versions). Designed to be taken with the follow-up course, “Statistics and Data Analysis with Excel, Part 2.”

Nov 23rd 2026
5-12 Weeks
Statistics Probability Excel
Details
Combining and Analyzing Complex Data (Coursera) Coursera
University of Maryland, College Park

Combining and Analyzing Complex Data (Coursera)

Statistics & Data Analysis

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.

Nov 16th 2026
4 Weeks
Ethics Models Data Analysis
Details
Bioinformatic Methods II (Coursera) Coursera
University of Toronto

Bioinformatic Methods II (Coursera)

Sci: Biology & Life Sciences Health & Society

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Nov 23rd 2026
5-12 Weeks
Biology Bioinformatics Molecular Biology
Details
Case studies in business analytics with ACCENTURE (Coursera) Coursera
ESSEC Business School

Case studies in business analytics with ACCENTURE (Coursera)

Statistics & Data Analysis Data Science

This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors. The objective is to give you an helicopter overview on what's happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects.

Nov 16th 2026
3 Weeks
Healthcare Media Pharmaceutical
Details
Introduction and Programming with IoT Boards (Coursera) Coursera
Pohang University of Science and Technology - POSTECH

Introduction and Programming with IoT Boards (Coursera)

CS: Information & Technology CS: Programming

Internet of Things (IoT) is an emerging area of information and communications technology (ICT) involving many disciplines of computer science and engineering including sensors/actuators, communications networking, server platforms, data analytics and smart applications. IoT is considered to be an essential part of the 4th Industrial Revolution along with AI and Big Data. This course will be very useful to senior undergraduate and graduate students as well as engineers who are working in the industry.

Nov 23rd 2026
5-12 Weeks
Programming Big Data Syntax
Details
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

Statistics & Data Analysis Data Science

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Nov 16th 2026
4 Weeks
NoSQL Machine Learning Big Data
Details
Data Processing Using Python (Coursera) Coursera
Nanjing University

Data Processing Using Python (Coursera)

CS: Software Engineering Statistics & Data Analysis

This course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.

Nov 23rd 2026
5-12 Weeks
Python Data Structures Data Analysis
Details
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera) Coursera
Icahn School of Medicine at Mount Sinai

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera)

Health & Society Science

In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Nov 16th 2026
5-12 Weeks
Network Analysis Big Data
Details