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⇱ Social Network Analysis | Coursera


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Social Network Analysis

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Gain insight into a topic and learn the fundamentals.
4.7

246 reviews

Beginner level
No prior experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.7

246 reviews

Beginner level
No prior experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace

What you'll learn

  • Define networks and discover the languages networks use.

  • Analyze a social network through data wrangling and visualizing a network.

  • Discuss what mechanisms generate networks.

  • Examine social networks analysis using case studies.

Details to know

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Assessments

5 assignments¹

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Taught in English
94%
Most learners liked this course

Build your subject-matter expertise

This course is part of the Computational Social Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

This course is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself). While such network structures always existed, computational social science has helped to reveal and to study them more systematically. In the first part of the course we focus on network structure. This looks as static snapshots of networks, which can be intricate and reveal important aspects of social systems. In our hands-on lab, you will also visualize and analyze a network with a software yourself, which will help to appreciate the complexity social networks can take on. During the second part of the course, we will look at how networks evolve in time. We ask how we can predict what kind of network will form and if and how we could influence network dynamics.

In this module, you will be introduced to the concept of networks. You will be able to define networks and identify how data is transformed and analyzed in a network. You will able be able to discuss how to formalize networks.

What's included

8 videos2 readings1 assignment1 discussion prompt

8 videosTotal 67 minutes
  • What is this Specialization About? (intro to 5-course Specialization)18 minutes
  • Course Introduction5 minutes
  • Social Equals Network5 minutes
  • Nodes12 minutes
  • Links9 minutes
  • Nodes and/or Links6 minutes
  • Strength of Ties8 minutes
  • Formalizing Networks4 minutes
2 readingsTotal 20 minutes
  • About UCCSS10 minutes
  • A Note From UC Davis10 minutes
1 assignmentTotal 30 minutes
  • Module 1 Quiz30 minutes
1 discussion promptTotal 10 minutes
  • Learning Goals10 minutes

In this module, you will be able to discuss the structure of networks and be able to explain how a person can be the center of one. You will be able to discover the different types of language that networks use and be able to identify the three types of network measurements.

What's included

12 videos1 reading1 assignment

12 videosTotal 74 minutes
  • Module Introduction1 minute
  • Network Jargon8 minutes
  • Degrees5 minutes
  • Roaming the Network12 minutes
  • Communities6 minutes
  • Triangles5 minutes
  • Network Centrality (Part 1)10 minutes
  • Network Centrality (Part 2)8 minutes
  • Community Detection6 minutes
  • Eigenvector Centrality6 minutes
  • Three Kinds of Measures2 minutes
  • Network Analysis Software5 minutes
1 readingTotal 10 minutes
  • Optional/Complementary10 minutes
1 assignmentTotal 30 minutes
  • Module 2 Quiz30 minutes

In this module, you will begin with a social network analysis lab activity. You will be able to do data wrangling of databases and visualize a network. You will be able to analyze a social network and also be able to examine other social network analysis through case studies.

What's included

9 videos4 readings1 assignment1 peer review

9 videosTotal 73 minutes
  • Module Introduction2 minutes
  • Data Wrangling12 minutes
  • Network Measures (Part 1)15 minutes
  • Network Measures (Part 2)9 minutes
  • Influentials6 minutes
  • Who's Influential?8 minutes
  • Twitter Cascades8 minutes
  • Base Rate4 minutes
  • Modeling Influentials7 minutes
4 readingsTotal 40 minutes
  • Social Network Analysis - Getting Started10 minutes
  • Social Network Analysis Lab Tutorial10 minutes
  • Welcome to Peer Review Assignments!10 minutes
  • Optional/Complementary10 minutes
1 assignmentTotal 20 minutes
  • Module 3 Quiz20 minutes
1 peer reviewTotal 60 minutes
  • Social Network Analysis Lab60 minutes

In this module, you will be able to identify the different types of social networks. You will be able to discuss what mechanisms generates these different types of networks and you will be able to explain how networks move from being static to dynamic.

What's included

8 videos1 assignment

8 videosTotal 57 minutes
  • How do Networks Evolve?1 minute
  • Network Dynamics7 minutes
  • Network Hypotheses5 minutes
  • Random Graphs7 minutes
  • Tipping Points8 minutes
  • Scale-Free Networks13 minutes
  • Hybrid Models6 minutes
  • Small World Networks10 minutes
1 assignmentTotal 30 minutes
  • Module 4 Quiz30 minutes

In this module, you will be able to examine theoretical predictions of networks. You will be able to calculate basic math problems and be able to discuss how to make networks more efficient and stable.

What's included

9 videos1 assignment1 discussion prompt

9 videosTotal 49 minutes
  • Module Introduction2 minutes
  • Growing Efficient Networks (Part 1)10 minutes
  • Growing Efficient Networks (Part 2)8 minutes
  • Growing Stable Networks9 minutes
  • Efficiency & Stability3 minutes
  • Diffusion of Network6 minutes
  • Diffusion Patterns7 minutes
  • Computing Networks3 minutes
  • Course Summary1 minute
1 assignmentTotal 30 minutes
  • Module 5 Quiz30 minutes
1 discussion promptTotal 10 minutes
  • Self-Reflection10 minutes

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Instructor

Instructor ratings
4.8 (65 ratings)
University of California, Davis
10 Courses98,607 learners

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Showing 3 of 246

MD
·

Reviewed on Aug 21, 2020

Very useful and wonderful course to enhance my knowledge. Looking forward more to learn. Thank you.

AW
·

Reviewed on Apr 10, 2020

A great crack course on SNA. It might be a bit difficult for newcomers, but you are making the right choice.

VP
·

Reviewed on Jul 31, 2020

Its a basic course which covers the breadth of SNA in a superficial manner.

Frequently asked questions

These are some reflections shared by students who have worked through the content of the Specialization on Computational Social Science:

  • "Highly enjoyable and most importantly, giving me exceptionally important skills to fulfill my job requirements at a new position in Munich. You may be interested to know the impact of your course on salary and in my case, the knowledge and certification gained adds about another Euro 20.000 on the annual salary (taking it to about Euro 120.000 p.a.)."

  • "My overall impression of this was: I can't wait to use this for other stuff!!"

  • "I absolutely think that these tools could be used in my future jobs, or even as a personal reflection. If you scrape and analyze the comments/reactions that your business gets on Youtube, Twitter, Instagram, etc., what does their language use say about how they interact with your brand — or what your brand brings out in them?"

  • "Wow, this is cool and fun stuff. Even though I may not pursue anything social-science related in the near future, it is still nice to learn and get to experience all of these tools that computational social science offers and benefits in all kinds of careers and fields of study."

  • "I particularly enjoyed the web-scraping for some reason. It feels very advanced although its very easy. ...It seems to be a very fast and efficient way of grabbing data."

  • "I enjoyed playing around with machine learning! ...It was also amazing to me how quickly it was able to grasp and learn our input in seconds. It makes me wonder how much more technology will advance in these next few years... It's scary but fascinating."

  • "The fact that these tools are so easily usable and attainable is incredible in my mind. Not only do we have access to them like we have access to things like Facebook and Twitter, but they're FREE."

  • "The most interesting aspect was the fact that these tools are all free and online. In the past, only researchers at well-funded universities had access to programs like the ones we used in all of our labs. But now, even someone without much technical knowledge on complex software can use these tools."

  • "I am so surprised that these tools are available to anyone through a simple download, and even more so that they are very user friendly and easy to learn how to navigate. I plan on starting a clothing line company in the future and I think it will be really helpful for me to be able to analyze so much online data."

  • "As an Environmental Policy Analysis and Planning major, I was fascinated to learn that there is a feasible way to simulate policy implementation and impact multiple times within a short span of time."

  • "UCCSS has allowed me to feel more confident in my abilities with a computer and to better understand companies like Facebook or Twitter. ...these tools really are powerful but also dangerous. ...It allows powerful individuals to manipulate ideas."

  • "Throughout the course, the content was challenging, but when it was finally applied to the labs at the end of each module, it was really rewarding to see everything play out. It was even more rewarding when it made sense too! ... I'm really glad I took this course! It was definitely a challenge, but I'm glad I got to experience and learn about so many topics I never knew even existed."

  • "It was fun seeing the results of the code that I made, and I never thought that I would be doing something like this in my life. The results also showed me what the society would look like.... Social network analysis and web scraping could be the tools that I use in my future job as all the internship that I'm looking now all related to social media or digital media."

  • "My career aspiration is to be a digital marketing expert. These computational tools have enormous implications for the field."

  • "I really really loved that this class let me learn hands-on and gave me experience with tools that have real world application and combine STEM & social science. I think that a lot of these tools are useful far beyond homework activities."

  • "Best course I have taken. I wish more online courses structured like this would be offered."

This Specialization on Computational Social Science is the result of a collective effort with contributions from Professors from all 10 campuses of the University of California. It is coordinated by Martin Hilbert, from UC Davis, and counts with lectures from:

1) UC Berkeley: Joshua Blumenstock, Prof. iSchool; Stuart Russell, Professor of Computer Science and Engineering.

2) UC Davis: Martin Hilbert, Prof., Dpt. of Communication & Seth Frey, Prof., Dpt. of Communication & Cynthia Gates, Director of the IRB.

3) UC Irvine: Lisa Pearl, Prof. Cognitive Sciences.

4) UC Los Angeles: PJ Lamberson, Assistant Prof. Communication Studies.

5) UC Merced: Paul Smaldino, Prof. Cognitive and Information Sciences.

6) UC Riverside: Christian Shelton, Prof. Computer Science.

7) UC San Diego: James Fowler, Prof. Global Public Health and Political Science.

8) UC San Francisco: Maria Glymour, Associate Prof. School of Medicine, Social Epidemiology & Biostatistics.

9) UC Santa Barbara: René Weber, Prof. Dpt. of Communication & Media Neuroscience Lab (with Frederic Hopp).

10) UC Santa Cruz: Marilyn Walker, Prof. Computer Science, Director, Computational Media.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.