Introduction to Network Analysis

Introduction to Network Analysis

This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.

This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.

$549 | Enroll Now
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Overview

This course, designed for managers in organizations that have or plan to have their own social networks, teaches a mix of quantitative and qualitative methods to describe, measure and analyze a social network environment. Students learn how to identify influential individuals, track the spread of information through networks, and how to use these techniques on real problems.

Learning Outcomes

Students who complete this course will learn how to:

  • Visualize networks of connections among entities or people
  • Measure attributes of networks 
  • Measure attributes of users and ties among them
  • Sample from networks that would be too large for analysis taken as a whole
  • Generate and study hypotheses about networks
  • Analyze propagation of things through networks

Who Should Take This Course

Marketing and IT managers, people who work in organizations with social media presences that they want to manage and analyze, and also people in organizations that have, or plan to have, their own social networks, who want to better understand the details of the environment they create.

Instructors

jennifer

Dr. Jennifer Golbeck

Dr. Jennifer Golbeck is an Associate Professor in the College of Information Studies at the University of Maryland, College Park, and the former director of its Human-Computer Interaction Lab.

Her research focuses on analyzing and computing with social media. This includes building models of social relationships, particularly trust, as well as user preferences and attributes, and using the results to design and build systems that improve the way people interact with information online. She is a Research Fellow of the Web Science Research Initiative and in 2006, she was selected as one of IEEE Intelligent Systems' Top Ten to Watch, a list of their top young AI researchers....

See Instructor Bio

Course Syllabus

Week 1

Network Analysis Basics

  • Basic Terminology
  • Metrics
  • Visualization

Week 2

The Social Network

  • Tie strength
  • Trust - User attributes and behavior

Week 3

Analytics

  • Modeling
  • Sampling
  • Content Analysis
  • Propagation

Week 4

Applications

  • Location
  • Filtering and recommender systems
  • Business use

Class Dates

2020

Sep 11, 2020 to Oct 9, 2020

2021

Mar 12, 2021 to Apr 9, 2021

Sep 10, 2021 to Oct 8, 2021

2022

Mar 11, 2022 to Apr 8, 2022

Sep 9, 2022 to Oct 7, 2022

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Prerequisites

There are no prerequisites and no particular background is required. Computer scientists or those with humanities backgrounds will be equally capable of doing the work.

What Our Students Say​

Jen is an amazing instructor. She is open to discussions, knowledgeable and willing to discuss topics. Her you tube videos added substantially to to the course and understanding the material

Vanessa Scherman
University of South Africa

The Video/ PPT lectures were excellent. Dr. Golbeck and the TA did a great job to ensure a very good environment to learn and obtain feedback. This course is a great addition to statistics.com

Satish Rao
IBM

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

In addition to assigned readings, this course also has supplemental video lectures and supplemental readings available online.

Course Text

The required text for this course is Analyzing the Social Web by Jennifer Golbeck. 

Please order a copy of your course textbook prior to course start date.

Software

The required software is Gephi (https://gephi.org/). Windows users may also want to get NodeXL (http://nodexl.codeplex.com/). Both are free.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Introduction to Network Analysis
$549 | Enroll Now
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