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Danilo Lessa Bernardineli
In this 1-hour long project-based course, you are going to be able to perform centrality network analysis and visualization on educational datasets, to generate different kinds of random graphs which represents social networks, and to manipulate the graph and subgraph structures, allowing you to break and get insights on complex structures.
This guided project is for people who want to incorporate network data science skills into their technology portfolio. This is a topic of interest to researchers, marketers, consultants and practitioners associated with the knowledge areas of social science, marketing, social media, operational...
Read more
In this 1-hour long project-based course, you are going to be able to perform centrality network analysis and visualization on educational datasets, to generate different kinds of random graphs which represents social networks, and to manipulate the graph and subgraph structures, allowing you to break and get insights on complex structures.
This guided project is for people who want to incorporate network data science skills into their technology portfolio. This is a topic of interest to researchers, marketers, consultants and practitioners associated with the knowledge areas of social science, marketing, social media, operational...
Read more
In this 1-hour long project-based course, you are going to be able to perform centrality network analysis and visualization on educational datasets, to generate different kinds of random graphs which represents social networks, and to manipulate the graph and subgraph structures, allowing you to break and get insights on complex structures.
This guided project is for people who want to incorporate network data science skills into their technology portfolio. This is a topic of interest to researchers, marketers, consultants and practitioners associated with the knowledge areas of social science, marketing, social media, operational research and complexity science.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/2hfpvl/network
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for early and mid-career researchers seeking to add network science to their skill set
Useful to marketers and consultants who wish to understand customer behavior and make better-informed decisions
Applicable for those interested in leveraging network science to solve complex problems in operational research and complexity science
Relies on a slightly outdated version of Python (3.7), which may limit its applicability in certain industries
May require learners to have access to specific software and tools, which could pose financial barriers for some
Limited to learners based in the North America region, potentially excluding a wider audience
Save this course
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Reviews summary
Practice-oriented network science
This one-hour project-based course in Network Data Science with NetworkX and Python covers centrality network analysis, visualization on educational datasets, random graph generation for social networks, and graph and subgraph structure manipulation. Reviews are mixed with several negative remarks about video and audio quality.
Helpful content despite technical issues.
"The content is good..."
"The course content is interesting..."
Frequent technical issues.
"bug for task_6transcrips unusable due to a very atypical accent for an Italian !"
"The audience is quite poor."
"The caption is impossible to read."
"he keeps letting the pointer floating around the lines he is typing which is annoying"
"The instructor is not using the whole screen, making it very difficult to read the notebook's code."
Instructor's delivery needs improvement.
"Video quality is extremely poor."
"Perfect content but poor pronunciation."
"the instructor seem professional ,the quality of the sound and video is very poor"
"very bad pronounciation from the instructor with no valid subtitles"
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 Network Data Science with NetworkX and Python with these
activities:
Review network analysis concepts
Show steps
Refreshes your memory on concepts in graph theory, network analysis, and social network analysis, which will be essential for understanding the course material.
Browse courses on
Graph Theory
Show steps
-
Review your notes or textbooks on graph theory and network analysis
-
Solve practice problems on basic graph theory concepts, such as finding shortest paths and computing centrality measures
Join a study group for the course
Show steps
Collaborate with peers to discuss course material, exchange ideas, and improve understanding.
Show steps
-
Find or create a study group with compatible classmates
-
Schedule regular meetings to discuss course topics and assignments
Review centrality network analysis
Show steps
You should review how centrality network analysis is calculated before taking this course
Show steps
-
Revise the definitions of different centrality measures
-
Practice calculating centrality measures on sample graphs
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Organize and review course materials
Show steps
Enhance retention and comprehension by organizing and reviewing course notes, assignments, and other relevant materials.
Show steps
-
Create a dedicated space for course materials
-
Regularly review and summarize key concepts
Review central limit theorem
Show steps
Review the central limit theorem to strengthen foundational understanding and improve comprehension of the course material.
Browse courses on
Central Limit Theorem
Show steps
-
Re-read notes from previous courses
-
Practice solving problems related to the central limit theorem
Explore NetworkX tutorials
Show steps
This tutorial will provide a guided introduction to NetworkX, a widely used Python library for graph analysis
Browse courses on
NetworkX
Show steps
-
Follow the NetworkX tutorial on graph basics
-
Explore different NetworkX functions for graph manipulation and analysis
-
Apply NetworkX to solve a small network analysis problem
Generate and manipulate random graphs
Show steps
This activity will help you master random graph generation and manipulation techniques
Show steps
-
Use a Python library (e.g., NetworkX) to generate random graphs with different properties
-
Apply graph manipulation operations, such as adding/removing nodes and edges
Join a study group
Show steps
Collaborating with peers can enhance your understanding and provide support throughout the course
Show steps
-
Find fellow students interested in forming a study group
-
Establish a regular meeting schedule and set goals for each session
-
Review course materials, discuss concepts, and work on practice problems together
Follow a tutorial on graph theory
Show steps
Supplement your understanding of graph theory by following a guided tutorial, which will provide step-by-step instructions and examples.
Browse courses on
Graph Theory
Show steps
-
Search for reputable tutorials on graph theory
-
Choose a tutorial that aligns with your learning style and pace
-
Follow the tutorial thoroughly and take notes
Follow tutorials on network visualization
Show steps
Helps you gain practical experience in visualizing network data, which is a key skill for analyzing and communicating insights from network analysis.
Browse courses on
Network Visualization
Show steps
-
Find online tutorials or courses on network visualization, such as those offered by Gephi or NetworkX
-
Follow the tutorials and practice visualizing different types of network data
-
Experiment with different visualization techniques and explore their strengths and weaknesses
Solve practice problems on network analysis
Show steps
Enhance your problem-solving skills by working through practice problems that cover various network analysis techniques.
Browse courses on
Network Analysis
Show steps
-
Collect practice problems from textbooks or online resources
-
Allocate dedicated time for solving these problems
-
Review your solutions and learn from your mistakes
Discuss network analysis concepts with peers
Show steps
Engages you in active learning and helps you clarify your understanding of the course material through discussions with peers.
Browse courses on
Network Analysis
Show steps
-
Join an online forum or discussion group dedicated to network analysis
-
Participate in discussions, ask questions, and share your insights
-
Collaborate with other learners on projects or assignments
Read "Networks, Crowds, and Markets"
Show steps
Gain a deeper understanding of network science by reading this foundational text, which covers key concepts and applications in social networks, information diffusion, and economic markets.
View
Networks, Crowds, and Markets: Reasoning about...
on Amazon
Show steps
-
Acquire a copy of the book
-
Read the book thoroughly, taking notes and highlighting important passages
Create a visual representation of a real-world network
Show steps
This activity will allow you to apply your understanding of network analysis to real-world data
Browse courses on
Network Visualization
Show steps
-
Choose a real-world dataset that represents a network
-
Load the data into a Python environment and create a graph
-
Use NetworkX to calculate centrality measures and identify important nodes and edges
-
Create a visualization that effectively communicates the network structure and insights
Design a graph model for a social network
Show steps
Apply your knowledge by creating a graph model for a real-world social network, allowing you to visualize and analyze patterns.
Show steps
-
Identify the entities and relationships in the social network
-
Choose an appropriate graph data structure
-
Create a visual representation of the graph
-
Analyze the graph to identify patterns and insights
Solve network analysis problems
Show steps
Provides you with opportunities to apply your network analysis skills and deepen your understanding of the concepts.
Browse courses on
Network Analysis
Show steps
-
Find online problem sets or exercises on network analysis
-
Solve the problems and check your answers against provided solutions
-
Analyze the solutions and learn from your mistakes
Build a network visualization dashboard
Show steps
Develops your skills in presenting network analysis results in a clear and visually appealing way.
Browse courses on
Network Visualization
Show steps
-
Choose a dataset and a specific research question or hypothesis to investigate
-
Extract the network data from the dataset and create a network graph
-
Use a visualization tool like Tableau or Power BI to create a dashboard that displays the network graph and relevant metrics
-
Write a brief report that summarizes your findings and insights
Contribute to open-source network analysis projects
Show steps
This activity will expose you to real-world network analysis applications and contribute to the community
Browse courses on
Collaborative Learning
Show steps
-
Identify open-source network analysis projects on platforms like GitHub
-
Review the project documentation and identify areas where you can contribute
-
Implement bug fixes, enhance existing features, or develop new functionalities
-
Submit pull requests and actively engage with the project community
Network analysis project
Show steps
Provides you with hands-on experience in applying network analysis techniques to solve a real-world problem, enabling you to synthesize and apply your learning.
Browse courses on
Network Analysis
Show steps
-
Identify a real-world problem or research question that can be addressed using network analysis
-
Collect and clean the necessary data to create a network graph
-
Apply network analysis techniques to analyze the graph
-
Draw conclusions and make recommendations based on your analysis
-
Write a report or present your findings
Mentoring and tutoring on network analysis
Show steps
Enhances your understanding of the course material by teaching it to others.
Browse courses on
Network Analysis
Show steps
-
Volunteer as a tutor or mentor for students taking similar network analysis courses
-
Prepare lessons and materials to help students understand the concepts
-
Answer students' questions and provide guidance
Review network analysis concepts
Show steps
Refreshes your memory on concepts in graph theory, network analysis, and social network analysis, which will be essential for understanding the course material.
Browse courses on
Graph Theory
Show steps
Show steps
Show steps
- Review your notes or textbooks on graph theory and network analysis
- Solve practice problems on basic graph theory concepts, such as finding shortest paths and computing centrality measures
Join a study group for the course
Show steps
Collaborate with peers to discuss course material, exchange ideas, and improve understanding.
Show steps
Show steps
Show steps
- Find or create a study group with compatible classmates
- Schedule regular meetings to discuss course topics and assignments
Review centrality network analysis
Show steps
You should review how centrality network analysis is calculated before taking this course
Show steps
Show steps
Show steps
- Revise the definitions of different centrality measures
- Practice calculating centrality measures on sample graphs
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Organize and review course materials
Show steps
Enhance retention and comprehension by organizing and reviewing course notes, assignments, and other relevant materials.
Show steps
Show steps
Show steps
- Create a dedicated space for course materials
- Regularly review and summarize key concepts
Review central limit theorem
Show steps
Review the central limit theorem to strengthen foundational understanding and improve comprehension of the course material.
Browse courses on
Central Limit Theorem
Show steps
Show steps
Show steps
- Re-read notes from previous courses
- Practice solving problems related to the central limit theorem
Explore NetworkX tutorials
Show steps
This tutorial will provide a guided introduction to NetworkX, a widely used Python library for graph analysis
Browse courses on
NetworkX
Show steps
Show steps
Show steps
- Follow the NetworkX tutorial on graph basics
- Explore different NetworkX functions for graph manipulation and analysis
- Apply NetworkX to solve a small network analysis problem
Generate and manipulate random graphs
Show steps
This activity will help you master random graph generation and manipulation techniques
Show steps
Show steps
Show steps
- Use a Python library (e.g., NetworkX) to generate random graphs with different properties
- Apply graph manipulation operations, such as adding/removing nodes and edges
Join a study group
Show steps
Collaborating with peers can enhance your understanding and provide support throughout the course
Show steps
Show steps
Show steps
- Find fellow students interested in forming a study group
- Establish a regular meeting schedule and set goals for each session
- Review course materials, discuss concepts, and work on practice problems together
Follow a tutorial on graph theory
Show steps
Supplement your understanding of graph theory by following a guided tutorial, which will provide step-by-step instructions and examples.
Browse courses on
Graph Theory
Show steps
Show steps
Show steps
- Search for reputable tutorials on graph theory
- Choose a tutorial that aligns with your learning style and pace
- Follow the tutorial thoroughly and take notes
Follow tutorials on network visualization
Show steps
Helps you gain practical experience in visualizing network data, which is a key skill for analyzing and communicating insights from network analysis.
Browse courses on
Network Visualization
Show steps
Show steps
Show steps
- Find online tutorials or courses on network visualization, such as those offered by Gephi or NetworkX
- Follow the tutorials and practice visualizing different types of network data
- Experiment with different visualization techniques and explore their strengths and weaknesses
Solve practice problems on network analysis
Show steps
Enhance your problem-solving skills by working through practice problems that cover various network analysis techniques.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Collect practice problems from textbooks or online resources
- Allocate dedicated time for solving these problems
- Review your solutions and learn from your mistakes
Discuss network analysis concepts with peers
Show steps
Engages you in active learning and helps you clarify your understanding of the course material through discussions with peers.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Join an online forum or discussion group dedicated to network analysis
- Participate in discussions, ask questions, and share your insights
- Collaborate with other learners on projects or assignments
Read "Networks, Crowds, and Markets"
Show steps
Gain a deeper understanding of network science by reading this foundational text, which covers key concepts and applications in social networks, information diffusion, and economic markets.
View
Networks, Crowds, and Markets: Reasoning about...
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the book
- Read the book thoroughly, taking notes and highlighting important passages
Create a visual representation of a real-world network
Show steps
This activity will allow you to apply your understanding of network analysis to real-world data
Browse courses on
Network Visualization
Show steps
Show steps
Show steps
- Choose a real-world dataset that represents a network
- Load the data into a Python environment and create a graph
- Use NetworkX to calculate centrality measures and identify important nodes and edges
- Create a visualization that effectively communicates the network structure and insights
Design a graph model for a social network
Show steps
Apply your knowledge by creating a graph model for a real-world social network, allowing you to visualize and analyze patterns.
Show steps
Show steps
Show steps
- Identify the entities and relationships in the social network
- Choose an appropriate graph data structure
- Create a visual representation of the graph
- Analyze the graph to identify patterns and insights
Solve network analysis problems
Show steps
Provides you with opportunities to apply your network analysis skills and deepen your understanding of the concepts.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Find online problem sets or exercises on network analysis
- Solve the problems and check your answers against provided solutions
- Analyze the solutions and learn from your mistakes
Build a network visualization dashboard
Show steps
Develops your skills in presenting network analysis results in a clear and visually appealing way.
Browse courses on
Network Visualization
Show steps
Show steps
Show steps
- Choose a dataset and a specific research question or hypothesis to investigate
- Extract the network data from the dataset and create a network graph
- Use a visualization tool like Tableau or Power BI to create a dashboard that displays the network graph and relevant metrics
- Write a brief report that summarizes your findings and insights
Contribute to open-source network analysis projects
Show steps
This activity will expose you to real-world network analysis applications and contribute to the community
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Identify open-source network analysis projects on platforms like GitHub
- Review the project documentation and identify areas where you can contribute
- Implement bug fixes, enhance existing features, or develop new functionalities
- Submit pull requests and actively engage with the project community
Network analysis project
Show steps
Provides you with hands-on experience in applying network analysis techniques to solve a real-world problem, enabling you to synthesize and apply your learning.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Identify a real-world problem or research question that can be addressed using network analysis
- Collect and clean the necessary data to create a network graph
- Apply network analysis techniques to analyze the graph
- Draw conclusions and make recommendations based on your analysis
- Write a report or present your findings
Mentoring and tutoring on network analysis
Show steps
Enhances your understanding of the course material by teaching it to others.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Volunteer as a tutor or mentor for students taking similar network analysis courses
- Prepare lessons and materials to help students understand the concepts
- Answer students' questions and provide guidance
Career center
Learners who complete Network Data Science with NetworkX and Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their expertise in statistical modeling, machine learning, and data analysis to extract value from large datasets to help businesses make better decisions. This course enhances one's ability to process and analyze data. Learning centrality network analysis, generating random graphs representing social networks, and performing graph substructure manipulation helps build a strong foundation for a successful Data Scientist.
Data Analyst
Data Analyst
Data Analysts use their programming skills and knowledge of data analysis techniques to transform raw data into actionable insights. This course can enhance one's ability to analyze data, perform complex visualizations, and interpret the significance of the results. Learning about centrality network analysis, generating random graphs to represent social networks, and manipulating graph substructures provides valuable skills.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex problems in various industries. This course enhances one's analytical skills, particularly in centrality network analysis and graph manipulation, which are applicable to solving optimization and resource allocation problems.
Social Scientist
Social Scientist
Social Scientists conduct research on human behavior and social phenomena. This course enhances the analytical skills needed for research, particularly in centrality network analysis and graph manipulation. These skills can help analyze social networks, model social interactions, and gain insights into social dynamics.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their expertise in data science, machine learning, and software engineering to develop and deploy machine learning models. This course can help build a foundation in data analysis techniques, particularly in centrality network analysis and graph manipulation, which are valuable for developing more accurate and efficient models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical modeling to assess risk and make investment decisions in the financial industry. This course enhances one's understanding of centrality in network analysis, generating random graphs for social networks, and manipulating graph structures, which are applicable to modeling financial networks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course strengthens one's skills in data analysis, graph manipulation, and network visualization, which may be relevant for developing certain software applications.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to identify opportunities, solve problems, and improve business processes. This course strengthens one's analytical skills, particularly in network analysis. Expertise in centrality network analysis and graph manipulation can help identify patterns and make more informed business decisions.
Market Researcher
Market Researcher
Market Researchers conduct studies to understand consumer behavior and market trends. This course enhances one's analytical skills, particularly in centrality network analysis and graph manipulation, which can be used to analyze market networks, identify customer segments, and predict consumer behavior.
Social Media Manager
Social Media Manager
Social Media Managers use their knowledge of social media platforms and marketing techniques to create and execute social media campaigns. This course enhances one's understanding of network analysis and graph visualization, which provides valuable insights into social media networks, user behavior, and viral trends.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts protect computer networks and systems from unauthorized access, use, disclosure, disruption, modification, or destruction.
User Experience (UX) Researcher
User Experience (UX) Researcher
User Experience (UX) Researchers study how users interact with products and services to improve their usability and experience.
Web Developer
Web Developer
Web Developers design and develop websites and web applications.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure and processes to manage and analyze data.
IT Manager
IT Manager
IT Managers oversee the planning, implementation, and maintenance of information technology systems within an organization.
For more career information including salaries, visit:
OpenCourser.com/course/2hfpvl/network
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
Network Data Science with NetworkX and Python.
Save
Provides a comprehensive overview of network data science, including both theoretical foundations and practical applications. It would be a valuable resource for students and practitioners who want to learn more about this field.
Classic in the field of social network analysis. It provides a comprehensive overview of the theoretical and methodological foundations of the field, as well as a variety of case studies.
Save
Provides a comprehensive overview of network analysis. It covers a variety of topics, including the theoretical foundations of network analysis, as well as a variety of practical applications.
Provides an overview of network data analytics. It covers a variety of topics, including the collection and analysis of network data, as well as a variety of case studies.
Provides an introduction to network analysis for the social sciences. It covers a variety of topics, including the basic concepts of network analysis, as well as a variety of applications in the social sciences.
Provides an introduction to network analysis using the R programming language. It covers a variety of topics, including the basics of network analysis, as well as a variety of case studies.
Provides an introduction to network analysis for public health. It covers a variety of topics, including the basic concepts of network analysis, as well as a variety of applications in public health.
Provides an introduction to network analysis for business. It covers a variety of topics, including the basic concepts of network analysis, as well as a variety of applications in business.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2hfpvl/network
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Danilo Lessa Bernardineli
Good to know
Appropriate for early and mid-career researchers seeking to add network science to their skill set
Useful to marketers and consultants who wish to understand customer behavior and make better-informed decisions
Applicable for those interested in leveraging network science to solve complex problems in operational research and complexity science
Relies on a slightly outdated version of Python (3.7), which may limit its applicability in certain industries
May require learners to have access to specific software and tools, which could pose financial barriers for some
Limited to learners based in the North America region, potentially excluding a wider audience
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