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Martin Hilbert

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.

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What's inside

Syllabus

Getting Started and Formalizing Networks
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.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces foundational concepts around networks and formalizes them
Provides hands-on labs to analyze networks using software
Suitable for individuals interested in understanding the structure and evolution of social networks
Taught by Professor Martin Hilbert, who is recognized for his research in social networks
Examines network evolution, enabling learners to understand how networks change over time
Covers theoretical predictions of networks, providing a mathematical foundation for understanding network dynamics

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Reviews summary

Introduction to social network analysis

According to learners, this course offers a solid conceptual introduction to Social Network Analysis, providing a good overview of fundamental ideas and terminology. Many found the software lab, specifically the introduction to Gephi, to be a useful and practical component, offering a first look at network visualization and analysis. However, some students felt the course leans too heavily on theory and lacks sufficient practical application or real-world examples beyond the lab (warning). There are mixed opinions on the clarity of lectures and whether the course provides immediately applicable skills (neutral). A few reviews suggest that having some prior background in statistics or mathematics might be helpful for certain sections (neutral). Overall, it seems to be a good starting point for understanding SNA concepts but may require supplementary learning for those seeking deep practical expertise.
May need background in stats/math.
"The math concepts are introduced quickly without much explanation."
"Requires some prior stats knowledge."
"Covers the essential graph theory and social science aspects of networks."
"I needed to review some basic math concepts for this."
Some find lectures clear, others unclear.
"Excellent introduction... The lectures were clear..."
"Mostly dry lectures presenting definitions."
"The theoretical parts are quite abstract and sometimes hard to connect..."
"The instructor is engaging."
Hands-on experience with analysis software.
"...the lab section using Gephi was incredibly helpful for practical application."
"The software part is a bit rushed, but useful."
"The lab was a highlight, although navigating the software initially had a learning curve."
"The visualization part was eye-opening."
Provides a good start to key SNA concepts.
"Excellent introduction to SNA concepts. The lectures were clear..."
"Perfect course to get started with Social Network Analysis. The modules are well-structured..."
"This course is a good theoretical introduction to the subject."
"It demystifies complex network ideas and makes them accessible."
More theory than hands-on application.
"Too theoretical for me. ... Didn't feel like I gained practical skills I can immediately apply."
"...it lacks practical examples beyond the single lab assignment."
"Could use more real-world case studies or more advanced techniques."
"Expected more depth."

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 Social Network Analysis with these activities:
Review course syllabus and content
Understand the course structure and key topics to succeed in this course.
Show steps
  • Download and read the course syllabus
  • Identify core learning objectives
  • Preview the course content by reading the first module
Analyze network structures using software
Develop proficiency in analyzing network structures and identifying patterns.
Show steps
  • Install the necessary network analysis software
  • Import and preprocess network data
  • Apply network analysis techniques to identify metrics and visualize results
Create a diagram of a real-world network
Enhance your understanding of network structures by creating visual representations.
Show steps
  • Choose a real-world social network (e.g., friendship network, collaboration network)
  • Collect data on the network's structure (e.g., nodes, edges, attributes)
  • Use a diagraming tool to create a visual representation of the network
  • Analyze and interpret the network diagram to identify patterns and insights
Four other activities
Expand to see all activities and additional details
Show all seven activities
Facilitate a discussion on a network analysis research paper
Deepen your understanding of network analysis by engaging in collaborative learning.
Browse courses on Network Analysis
Show steps
  • Select a research paper on a topic related to network analysis
  • Read and summarize the paper's main findings and methodologies
  • Prepare a presentation to guide a discussion among peers
  • Facilitate the discussion, encouraging active participation and critical thinking
Learn about network evolution and dynamics
Gain insights into the mechanisms that drive the evolution and behavior of networks.
Browse courses on Network Evolution
Show steps
  • Watch online tutorials or read articles on network evolution
  • Understand concepts such as network growth, network resilience, and network community dynamics
  • Explore examples and case studies of network evolution in various domains (e.g., social networks, biological networks)
Create a comprehensive study guide using course notes and materials
Organize and consolidate course materials to enhance memorization and retention.
Browse courses on Study Guide
Show steps
  • Gather all relevant course notes, slides, and assignments
  • Organize materials by topic and key concepts
  • Summarize and condense information into a coherent and structured format
  • Include visual aids, such as diagrams and charts, to enhance clarity
Participate in a social network analysis competition
Challenge yourself and demonstrate your skills in network analysis and problem-solving.
Show steps
  • Find a social network analysis competition or challenge
  • Form a team or work independently
  • Apply network analysis techniques to solve the competition's problem or task
  • Submit your results for evaluation

Career center

Learners who complete Social Network Analysis will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist analyzes big data using statistical and mathematical modeling to make improved and informed business decisions. Their main goal is to find insights and trends in data that an organization can use to increase its revenue, improve efficiency, and identify new opportunities. Increasingly, companies also want to use large amounts of data to better understand their customers. This course introduces the learner to the basic concepts of networks, including their structure and evolution in time. This course is highly recommended because it will help you to develop the skills needed to be successful in the fast-growing field of data science.
Operations Research Analyst
An Operations Research Analyst develops and uses mathematical models to investigate complex operational challenges and find optimal solutions. These analysts may work in a variety of industries and on a wide range of problems, from figuring out the most efficient way to deliver products to customers, to designing new scheduling systems for public transportation, to improving the efficiency of manufacturing processes. This course can help you develop the skills necessary to succeed as an Operations Research Analyst because it provides you with the foundation in network science that is needed to develop and use mathematical models to investigate complex operational challenges.
Quantitative Analyst
Quantitative Analysts develop and use mathematical and statistical models to help businesses make better decisions. They use their knowledge of mathematics, statistics, and computing to analyze data and create models that can be used to predict future trends and make recommendations for investment.
Market Researcher
A Market Researcher helps companies understand their target market and develop strategies to reach them. They conduct research to gather data about consumer behavior, preferences, and trends. This course can help you develop the skills necessary to succeed as a Market Researcher because it provides you with the foundation in network science that is needed to understand how consumers are connected to each other and how this affects their behavior.
Network Engineer
A Network Engineer builds and maintains computer networks. They design, install, and manage the hardware and software that allows computers to communicate with each other. This course can help you develop the skills necessary to succeed as a Network Engineer because it provides you with the foundation in network science that is needed to understand how networks are structured and how they function.
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make better decisions. They use their knowledge of data analysis techniques to identify trends and patterns in data. This course can help you develop the skills necessary to succeed as a Data Analyst because it provides you with the foundation in network science that is needed to understand how data is structured and how it can be used to make better decisions.
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course can help you develop the skills necessary to succeed as a Software Engineer because it provides you with the foundation in network science that is needed to understand how software applications are structured and how they interact with each other.
Project Manager
A Project Manager plans, executes, and closes projects. They work with stakeholders to define the project scope, develop a project plan, and manage the project budget. This course may be useful for Project Managers because it provides you with the foundation in network science that is needed to understand how projects are structured and how they can be managed more effectively.
Business Analyst
A Business Analyst gathers and analyzes business requirements to help businesses improve their processes and systems. They work with stakeholders to identify the needs of the business and develop solutions to meet those needs. This course may be useful for Business Analysts because it provides you with the foundation in network science that is needed to understand how businesses are structured and how they operate.
Consultant
A Consultant provides advice and guidance to businesses on a variety of topics. They use their knowledge and expertise to help businesses improve their operations, solve problems, and achieve their goals. This course may be useful for Consultants because it provides you with the foundation in network science that is needed to understand how businesses are structured and how they operate.
Human Resources Manager
A Human Resources Manager plans, directs, and coordinates the human resources activities of an organization. They work with employees to recruit, hire, train, and develop staff. They also develop and implement policies and procedures to ensure that the organization is in compliance with labor laws and regulations. This course may be useful for Human Resources Managers because it provides you with the foundation in network science that is needed to understand how organizations are structured and how they operate.
Marketing Manager
A Marketing Manager develops and implements marketing plans to promote products and services. They work with marketing teams to create and execute marketing campaigns, and they track the results of these campaigns to measure their effectiveness. This course may be useful for Marketing Managers because it provides you with the foundation in network science that is needed to understand how consumers are connected to each other and how this affects their behavior.
Sales Manager
A Sales Manager leads and manages a team of sales representatives. They develop and implement sales strategies, and they track the results of these strategies to measure their effectiveness. This course may be useful for Sales Managers because it provides you with the foundation in network science that is needed to understand how customers are connected to each other and how this affects their buying behavior.
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. They use their knowledge of financial markets and investment strategies to help clients make informed investment decisions. This course may be useful for Financial Analysts because it provides you with the foundation in network science that is needed to understand how financial markets are structured and how they operate.
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop and implement risk management strategies. This course may be useful for Actuaries because it provides you with the foundation in network science that is needed to understand how risk is distributed throughout a population.

Reading list

We've selected eight 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 Social Network Analysis.
Provides a comprehensive overview of social network theory. It covers a wide range of topics, including network structure, network dynamics, and network applications. It valuable resource for students and researchers interested in social network analysis.
Provides a comprehensive overview of social network analysis methods and applications. It covers a wide range of topics, including network structure, network dynamics, and network visualization. It valuable resource for students and researchers interested in social network analysis.
Provides an introduction to social network data analysis. It covers a wide range of topics, including data collection, data cleaning, and data analysis. It valuable resource for students and researchers interested in social network analysis.
Provides an introduction to social network analysis. It covers a wide range of topics, including network structure, network dynamics, and network applications. It valuable resource for students and researchers interested in social network analysis.
Provides a framework for social network analysis. It covers a wide range of topics, including network structure, network dynamics, and network applications. It valuable resource for students and researchers interested in social network analysis.
Provides an introduction to data science for social network analysis. It covers a wide range of topics, including data collection, data cleaning, and data analysis. It valuable resource for students and researchers interested in social network analysis.
Provides a user's guide to social networks. It covers a wide range of topics, including social network theory, social network analysis, and social network applications. It valuable resource for students and researchers interested in social network analysis.

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