Coursera

Performing Network, Path, and Text Analyses in SAS Visual Analytics (Coursera)

Offered by SAS,
Performing Network, Path, and Text Analyses in SAS Visual Analytics (Coursera)

In this course, you learn about the data structure needed for network, path, and text analytics and how to create network analysis, path analysis, and text analytics in SAS Visual Analytics. What tou will learn: To describe the data structure needed for network analysis, path analysis, and text analytics; How to create network analysis to analyze relationship between entities in SAS Visual Analytics; How to create path analysis to understand frequent paths in SAS Visual Analytics; How to create text analytics to analyze unstructured text in SAS Visual Analytics.

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

Course 4 of 5 in the SAS Visual Business Analytics Professional Certificate

Syllabus

WEEK 1
Course Overview
In this module, you learn about the business scenario that you will follow for this course and where the files are located in SAS Viya for Learners.
Performing Network Analysis
In this module, you learn more about network analysis in Visual Analytics.
Performing Path Analysis
In this module, you learn more about path analysis in Visual Analytics.
Performing Text Analytics
In this module, you learn more about text analytics in Visual Analytics.

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

Related Courses

The Structured Query Language (SQL) (Coursera) Coursera
University of Colorado Boulder

The Structured Query Language (SQL) (Coursera)

Data Science

In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database. Our scope includes not only the SELECT statement for retrieving data and creating analytical reports, but also includes the DDL ("Data Definition Language") and DML ("Data Manipulation Language") commands necessary to create and maintain database objects.

Jun 9th 2026
5-12 Weeks
Databases SQL Data Science
Details
Data Analysis: Statistical Modeling and Computation in Applications (edX) EdX
MIT,MITx

Data Analysis: Statistical Modeling and Computation in Applications (edX)

Statistics & Data Analysis

A hands-on introduction to the interplay between statistics and computation for the analysis of real data. -- Part of the MITx MicroMasters program in Statistics and Data Science. Data science requires multi-disciplinary skills ranging from mathematics, statistics, machine learning, problem solving to programming, visualization, and communication skills. In this course, learners will combine these foundational and practical skills with domain knowledge to ask and answer questions using real data.

May 13th 2024
13-24 Weeks
Machine Learning Applications Computation
Details
Introduction to Effective Network Analysis (FutureLearn) FutureLearn
EC-Council

Introduction to Effective Network Analysis (FutureLearn)

CS: Information & Technology

Learn network monitoring and analysis to ensure security and guard against social network cybercrime. Protect your organisation with effective network security. As cybercrime continues to grow, analysis of social networks has become a powerful tool in criminology and criminal investigations. This four-week course will give you a comprehensive introduction to network analysis and security, allowing you to provide effective digital security to your organisation.

Mar 21st 2022
3 Weeks
Security Cybercrime Social Networks
Details
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 8th 2026
4 Weeks
Statistics Probability Data Analysis
Details
Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

Data Science

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Jun 1st 2026
5-12 Weeks
Algorithms Machine Learning Clustering
Details
Introduction to Data Networks and the Internet - Master's (Coursera) Coursera
Illinois Tech

Introduction to Data Networks and the Internet - Master's (Coursera)

Security & Networking

This course covers current and evolving data network technologies, protocols, network components, and the networks that use them, focusing on communication to and from the Internet and Local area networks. Course content includes Internet architecture, organization, and protocols including Ethernet, 802.11, routing, switching, OSI and TCP models, DNS, SNMP, DHCP, and more.

Jun 8th 2026
5-12 Weeks
Network Networks Internet
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.

Jun 1st 2026
5-12 Weeks
Network Analysis Big Data
Details
Business Intelligence and Visual Analytics (Coursera) Coursera
University of California, Irvine

Business Intelligence and Visual Analytics (Coursera)

CS: Information & Technology

Building on “Data Warehousing and Business Intelligence,” this course focuses on data visualization and visual analytics. Starting with a thorough coverage of what data visualization is and what type of visualization is good for a given purpose, the course quickly dives into development of practical skills and knowledge about visual analytics by way of using one of the most popular visual analytics tools: SAS Viya, a cloud-based analytics platform. An overview of cloud architecture, automation, and machine learning is also provided.

Jun 1st 2026
4 Weeks
Machine Learning Business Intelligence Data Visualization
Details
Applied Social Network Analysis in Python (Coursera) Coursera
University of Michigan

Applied Social Network Analysis in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

Jun 8th 2026
4 Weeks
Python Networks Social Networks
Details