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Daniel Romero
This course will introduce the learner to network analysis through tutorials using 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.
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This course will introduce the learner to network analysis through tutorials using 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.
Read more
This course will introduce the learner to network analysis through tutorials using 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.
This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/wccs11/applied
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What's inside
Syllabus
Why Study Networks and Basics on NetworkX
Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company.
Read more
Syllabus
Why Study Networks and Basics on NetworkX
Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company.
Read more
Network Connectivity
In Module Two you'll learn how to analyze the connectivity of a network based on measures of distance, reachability, and redundancy of paths between nodes. In the assignment, you will practice using NetworkX to compute measures of connectivity of a network of email communication among the employees of a mid-size manufacturing company.
Influence Measures and Network Centralization
In Module Three, you'll explore ways of measuring the importance or centrality of a node in a network, using measures such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities. You'll learn about the assumptions each measure makes, the algorithms we can use to compute them, and the different functions available on NetworkX to measure centrality. In the assignment, you'll practice choosing the most appropriate centrality measure on a real-world setting.
Network Evolution
In Module Four, you'll explore the evolution of networks over time, including the different models that generate networks with realistic features, such as the Preferential Attachment Model and Small World Networks. You will also explore the link prediction problem, where you will learn useful features that can predict whether a pair of disconnected nodes will be connected in the future. In the assignment, you will be challenged to identify which model generated a given network. Additionally, you will have the opportunity to combine different concepts of the course by predicting the salary, position, and future connections of the employees of a company using their logs of email exchanges.
Good to know
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what to watch for , and
possible dealbreakers
Teaches network analysis skills using the NetworkX library, fostering knowledge in Python programming
Develops core concepts of network analysis, including connectivity, centrality, and network evolution
Introduces various models of network generation and link prediction, preparing learners for advanced network analysis
Emphasizes the real-world relevance of network analysis in fields such as social sciences and computer engineering
Provides practical assignments and hands-on exercises, solidifying understanding of network analysis concepts
Requires prior knowledge of Python and introductory data science concepts, limiting accessibility for complete beginners
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Reviews summary
Applied social network analysis with python
Learners say this course covers the basics of network analysis using the Python package networkx. The course is well-structured and engaging, with clear explanations and useful examples. The assignments are challenging but fair, and the quizzes test your understanding of the material. Daniel Romero, the excellent instructor, leaves you confident in your ability to apply network analysis in your work.
The assignments are interesting and challenging. They help you apply the concepts you learn in the lectures.
"The course provides a good overview of basic measures for network data. I took as prep for a harder course. I would recommend it."
"Really excellent course! Practical programming and network analysis tools along with just enough theory to keep you interested. "
"Excellent course to learn Network Analysis using Python. Thank you to the instructor and whole team behind making this course for providing such good content."
Daniel Romero is a great instructor. He is clear, engaging, and knowledgeable.
"The lecturer was the best of the lecturers in the specialisation."
"Excellent lecturer, very useful content, assignments were a good level of challenge, particularly the last one which brings it all to life."
"This course was fantastic, with a lot of information and tips important for me. The instructor is very focused and I have confidence on him."
Learners highly recommend this course and find the material interesting and relevant.
"This is one of the most usefull course ive taken."
"I enjoyed the whole specialization I am very grateful with Coursera and Michigan University."
"Great course. I enjoyed the whole specialization I am very grateful with Coursera and Michigan University."
Some materials are outdated, and students have to work around old or no longer used libraries.
"All assignments and lectures are outdated and will not work with current versions of Python. "
"I thought the content was interesting - but it is so stale, none of the packages work with conventional language semantics."
"Python and related libs are SUPER old. Some important codes used in this specialization were duplicated and you cannot get enough explanations even on Google."
The auto grader is frustrating and often gives unclear error messages.
"The course provides a good overview of basic measures for network data. I took as prep for a harder course. I would recommend it."
"Lecture Videos are good but it seems 0 efforts were put in the assessments. The auto-grader is especially a pain and you end up spending LOT of time around trivial issues with the auto-grader."
"The professor was great! The way he explained everything was clear and understandable. The mentor, Uwe, in the course was also super helpful when other students or I needed assistance during assignments. The only reason why I gave this course 4 stars rather than a 5 was that there were many errors in the assignments and auto grader."
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 Applied Social Network Analysis in Python with these
activities:
Review Python Programming Fundamentals
Show steps
Refreshes your Python programming skills, ensuring that you have a strong foundation for the Python-based activities and assignments in this course.
Browse courses on
Python Programming
Show steps
-
Review the basics of Python syntax, data types, and control flow.
-
Practice writing and executing simple Python programs.
Compile Course Resources
Show steps
Organizes and consolidates your course materials, making it easier to access and review key concepts throughout the course.
Show steps
-
Gather all the lecture notes, slides, assignments, and other materials provided by the instructor.
-
Create a system for organizing and storing these materials, such as using folders or a digital notebook.
Review Elements of Graph Theory
Show steps
Refreshes and strengthens your understanding of the fundamentals and important concepts of graph theory, laying a stronger foundation for the network analysis topics of this course.
Show steps
-
Read each chapter and take notes on key concepts such as types of graphs, graph representations, and basic graph algorithms.
-
Attempt the end-of-chapter exercises to practice applying the concepts.
-
Create flashcards to help you memorize important definitions and theorems.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Find a Network Analysis Mentor
Show steps
Provides you with guidance and support from an experienced professional, enhancing your learning journey and career development.
Browse courses on
Network Analysis
Show steps
-
Identify potential mentors who are active in the field of network analysis.
-
Reach out to them via email or LinkedIn.
-
Explain your interest in network analysis and ask if they are willing to mentor you.
Network Analysis Workshop
Show steps
Provides exposure to advanced topics and best practices in network analysis, enabling you to stay abreast of the latest developments.
Browse courses on
Network Analysis
Show steps
-
Attend a workshop conducted by experts in the field.
-
Participate actively in discussions and ask questions.
-
Apply the knowledge gained to your own network analysis projects.
Volunteer in a Network Analysis Research Group
Show steps
Offers hands-on experience in applying network analysis techniques to various domains, broadening your perspective and practical skills.
Browse courses on
Network Analysis
Show steps
-
Find a research group at a university or organization that focuses on network analysis.
-
Contact the group leader and express your interest in volunteering.
-
Attend meetings and participate in discussions.
-
Assist with research projects and contribute to the group's knowledge base.
Network Analysis Tutorial
Show steps
Deepens your understanding of network analysis concepts and enhances your ability to explain them clearly.
Browse courses on
Network Analysis
Show steps
-
Choose a specific topic in network analysis that interests you.
-
Research the topic thoroughly and gather relevant information from credible sources.
-
Create a written or video tutorial explaining the topic in a concise and engaging manner.
-
Share your tutorial with others for feedback and improvement.
Review Python Programming Fundamentals
Show steps
Refreshes your Python programming skills, ensuring that you have a strong foundation for the Python-based activities and assignments in this course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review the basics of Python syntax, data types, and control flow.
- Practice writing and executing simple Python programs.
Compile Course Resources
Show steps
Organizes and consolidates your course materials, making it easier to access and review key concepts throughout the course.
Show steps
Show steps
Show steps
- Gather all the lecture notes, slides, assignments, and other materials provided by the instructor.
- Create a system for organizing and storing these materials, such as using folders or a digital notebook.
Review Elements of Graph Theory
Show steps
Refreshes and strengthens your understanding of the fundamentals and important concepts of graph theory, laying a stronger foundation for the network analysis topics of this course.
Show steps
Show steps
Show steps
- Read each chapter and take notes on key concepts such as types of graphs, graph representations, and basic graph algorithms.
- Attempt the end-of-chapter exercises to practice applying the concepts.
- Create flashcards to help you memorize important definitions and theorems.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Find a Network Analysis Mentor
Show steps
Provides you with guidance and support from an experienced professional, enhancing your learning journey and career development.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Identify potential mentors who are active in the field of network analysis.
- Reach out to them via email or LinkedIn.
- Explain your interest in network analysis and ask if they are willing to mentor you.
Network Analysis Workshop
Show steps
Provides exposure to advanced topics and best practices in network analysis, enabling you to stay abreast of the latest developments.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Attend a workshop conducted by experts in the field.
- Participate actively in discussions and ask questions.
- Apply the knowledge gained to your own network analysis projects.
Volunteer in a Network Analysis Research Group
Show steps
Offers hands-on experience in applying network analysis techniques to various domains, broadening your perspective and practical skills.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Find a research group at a university or organization that focuses on network analysis.
- Contact the group leader and express your interest in volunteering.
- Attend meetings and participate in discussions.
- Assist with research projects and contribute to the group's knowledge base.
Network Analysis Tutorial
Show steps
Deepens your understanding of network analysis concepts and enhances your ability to explain them clearly.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Choose a specific topic in network analysis that interests you.
- Research the topic thoroughly and gather relevant information from credible sources.
- Create a written or video tutorial explaining the topic in a concise and engaging manner.
- Share your tutorial with others for feedback and improvement.
Career center
Learners who complete Applied Social Network Analysis in Python will develop knowledge and skills
that may be useful to these careers:
Market Researcher
Market Researcher
Market Researchers conduct research to understand market trends, customer behavior, and industry dynamics. Applied Social Network Analysis in Python offers valuable insights for aspiring Market Researchers seeking to analyze social networks for market research purposes. This course teaches techniques for measuring network influence, identifying key players, and predicting network evolution, which are essential skills for understanding the dynamics of consumer behavior and market trends in the digital age.
Network Engineer
Network Engineer
Network Engineers design, implement, and manage computer networks to ensure their reliability and efficiency. Applied Social Network Analysis in Python provides valuable insights for aspiring Network Engineers who wish to analyze network topologies and identify potential vulnerabilities. This course teaches techniques for measuring network connectivity, identifying key nodes, and predicting network evolution, which are essential skills for optimizing network performance, enhancing network security, and ensuring network resilience.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts examine healthcare data to improve patient outcomes and optimize healthcare delivery. Applied Social Network Analysis in Python provides valuable insights for aspiring Healthcare Analysts who aim to analyze healthcare networks and identify key influencers. This course teaches techniques for measuring network influence, identifying key players, and predicting network evolution, which are crucial for understanding the dynamics of healthcare networks, improving patient care, and optimizing healthcare resource allocation.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks to protect organizations from potential threats. Applied Social Network Analysis in Python offers valuable insights for aspiring Risk Analysts who wish to analyze social networks for risk assessment purposes. This course provides techniques for measuring network influence, identifying key players, and predicting network evolution, which are essential skills for understanding the dynamics of social networks, identifying potential threats, and optimizing risk management strategies.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing business processes and recommending solutions. Applied Social Network Analysis in Python can benefit aspiring Business Analysts who aim to understand the social dynamics within organizations and leverage network analysis to drive organizational improvements. This course provides skills in identifying key influencers, measuring network connectivity, and predicting network evolution, which are crucial for analyzing and optimizing organizational networks to enhance collaboration and productivity.
Customer Success Manager
Customer Success Manager
Customer Success Managers nurture relationships with customers to ensure their satisfaction and retention. Applied Social Network Analysis in Python offers valuable insights for aspiring Customer Success Managers who wish to analyze customer networks and identify key influencers. This course teaches techniques for measuring network influence, identifying key players, and predicting network evolution, which are essential skills for understanding customer behavior, optimizing customer engagement, and driving customer loyalty.
Social Media Manager
Social Media Manager
Social Media Managers develop and execute social media strategies to build and maintain an organization's online presence. Applied Social Network Analysis in Python can be highly beneficial for aspiring Social Media Managers who wish to gain a deeper understanding of social media networks and user interactions. The course provides insights into network connectivity, influence measures, and network evolution, enabling individuals to optimize their social media campaigns and effectively engage with target audiences on various platforms.
Data Scientist
Data Scientist
Data Scientists collect, analyze and interpret data to extract meaningful insights that drive decision-making. Applied Social Network Analysis in Python is a valuable course for aspiring Data Scientists who aim to specialize in analyzing social networks and relationships. This course provides a solid foundation in network analysis techniques, which are essential for understanding and extracting valuable information from complex social networks. By completing this course, individuals will gain the skills to identify patterns, measure influence, and predict the evolution of social networks, all of which are crucial skills for Data Scientists in various industries.
Public Relations Specialist
Public Relations Specialist
Public Relations Specialists manage the public image of organizations and build relationships with key stakeholders. Applied Social Network Analysis in Python is a valuable asset for aspiring Public Relations Specialists seeking to leverage social networks for effective public relations campaigns. The course provides techniques for analyzing network influence, measuring network connectivity, and predicting network evolution, enabling individuals to identify key influencers, track the spread of information, and optimize communication strategies to enhance their public relations efforts.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make informed decisions. Applied Social Network Analysis in Python can be beneficial for aspiring Statisticians who wish to specialize in analyzing social networks and relationships. This course provides a solid foundation in network analysis techniques, which are essential for understanding and extracting meaningful information from complex social networks. By completing this course, individuals will gain the skills to identify patterns, measure influence, and predict the evolution of social networks, all of which are crucial skills for Statisticians in various industries.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights that drive decision-making. Applied Social Network Analysis in Python can be highly beneficial for aspiring Data Analysts who wish to specialize in analyzing social networks and relationships. This course provides a solid foundation in network analysis techniques, which are essential for understanding and extracting valuable information from complex social networks. By completing this course, individuals will gain the skills to identify patterns, measure influence, and predict the evolution of social networks, all of which are crucial skills for Data Analysts in various industries.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex operational problems. Applied Social Network Analysis in Python can provide valuable insights for aspiring Operations Research Analysts who wish to apply network analysis techniques to optimize business processes and systems. This course teaches the fundamentals of network analysis, including concepts such as graph theory, network optimization algorithms, and simulation techniques, which can be applied to a wide range of operations research problems, such as supply chain management, logistics, and resource allocation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning models to solve complex problems. Applied Social Network Analysis in Python can be beneficial for aspiring Machine Learning Engineers who wish to incorporate network analysis techniques into their machine learning models. This course provides a foundation in network analysis concepts and algorithms, which can be leveraged to enhance the performance of machine learning models in applications such as community detection, link prediction, and network embedding.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers analyze financial data to identify investment opportunities and make informed decisions. Applied Social Network Analysis in Python can be beneficial for aspiring Quantitative Researchers who wish to incorporate network analysis techniques into their financial modeling and analysis. This course provides a foundation in network analysis concepts and algorithms, which can be leveraged to identify patterns and relationships in financial data, enhance risk assessment, and develop more sophisticated trading strategies.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Applied Social Network Analysis in Python can provide valuable insights for aspiring Software Engineers who wish to develop software applications that leverage network analysis techniques. This course teaches the fundamentals of network analysis, including concepts such as graph theory, network measures, and community detection algorithms, which can be applied to a wide range of software development scenarios, such as social network analysis, recommender systems, and fraud detection.
For more career information including salaries, visit:
OpenCourser.com/course/wccs11/applied
Reading list
We've selected 20 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
Applied Social Network Analysis in Python.
Provides a comprehensive overview of network science, covering the fundamental concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to gain a deeper understanding of network science.
Provides a comprehensive overview of social network analysis methods and applications. It covers the basics of network analysis, as well as more advanced topics such as community detection, link prediction, and network evolution. This book would be a valuable resource for students and researchers interested in learning more about social network analysis.
Save
Provides a comprehensive overview of network analysis methods, covering topics such as network data collection, network visualization, and network analysis techniques. It valuable resource for students and researchers interested in analyzing networks.
Provides a theoretical foundation for network analysis, covering topics such as random graphs, percolation theory, and network models. It valuable resource for anyone who wants to learn more about the mathematical underpinnings of network science.
Provides a comprehensive overview of complex networks, covering the fundamental concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to gain a deeper understanding of complex networks.
Provides a comprehensive overview of network models from a statistical perspective, covering topics such as network data representation, network analysis algorithms, and applications in various fields. It valuable resource for students and researchers interested in applying statistical methods to network analysis.
Save
Provides a comprehensive overview of data structures and algorithms for network analysis, covering topics such as graph representation, graph algorithms, and network optimization. It valuable resource for students and researchers interested in the implementation and optimization of network analysis algorithms.
Provides a comprehensive overview of social network analysis and mining techniques, covering topics such as network data representation, network analysis algorithms, and applications in various social network domains. It valuable resource for students and researchers interested in analyzing social networks.
Save
Provides a comprehensive overview of network science, covering topics such as network structure, dynamics, and applications in various fields. It valuable resource for students and researchers interested in getting started with network analysis.
Provides a concise introduction to networks, covering the basic concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to gain a basic understanding of networks.
Provides a comprehensive handbook of social network analysis, covering the basic concepts, methods, and applications of the field. It valuable resource for students, researchers, and practitioners who want to learn how to use social network analysis to study social phenomena.
Provides a comprehensive overview of the structure and dynamics of networks. It covers topics such as network formation, network evolution, and network resilience. It valuable resource for anyone who wants to learn more about the dynamics of complex systems.
Save
Provides a comprehensive introduction to graph algorithms.
Provides a theoretical framework for understanding the behavior of networks, crowds, and markets. It covers topics such as network formation, network dynamics, and network games. It valuable resource for anyone who wants to learn more about the theoretical foundations of network science.
Provides a comprehensive introduction to the social implications of network.
Provides a comprehensive overview of the statistical mechanics of complex networks, covering the fundamental concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to gain a deeper understanding of the statistical mechanics of complex networks.
Provides a comprehensive introduction to social network data analysis.
Provides a comprehensive overview of networks in social science, covering the basic concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to learn how to use networks to study social phenomena.
Provides a comprehensive handbook of network analysis, covering the basic concepts, methods, and applications of the field. It valuable resource for students, researchers, and practitioners who want to learn how to use network analysis to study social phenomena.
Provides a comprehensive overview of networks, covering the basic concepts, theories, and applications of the field. It valuable resource for students, researchers, and practitioners who want to gain a deeper understanding of networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wccs11/applied
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Applied Machine Learning in Python
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructor
Daniel Romero
Language
English
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This course belongs to a
collection
called
Applied Data Science with Python. It's comprised of five courses:
- Applied Text Mining in Python
- Introduction to Data Science in Python
- Applied Social Network Analysis in Python (viewing)
- Applied Machine Learning in Python
- Applied Plotting, Charting & Data Representation in Python
Good to know
Teaches network analysis skills using the NetworkX library, fostering knowledge in Python programming
Develops core concepts of network analysis, including connectivity, centrality, and network evolution
Introduces various models of network generation and link prediction, preparing learners for advanced network analysis
Emphasizes the real-world relevance of network analysis in fields such as social sciences and computer engineering
Provides practical assignments and hands-on exercises, solidifying understanding of network analysis concepts
Requires prior knowledge of Python and introductory data science concepts, limiting accessibility for complete beginners
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