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Justin Flett
NetworkX is a widely-used data science and machine learning software library. This course will teach you the basics of implementing network analysis using NetworkX, including visualization, link prediction, and collaborative filtering systems.
Read more
NetworkX is a widely-used data science and machine learning software library. This course will teach you the basics of implementing network analysis using NetworkX, including visualization, link prediction, and collaborative filtering systems.
Read more
NetworkX is a widely-used data science and machine learning software library. This course will teach you the basics of implementing network analysis using NetworkX, including visualization, link prediction, and collaborative filtering systems.
Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Mining Data from Networks, you will learn foundational knowledge of solving real world data science problems. First, you will learn the basics of implementing network analysis including understanding and visualizing network data. Next, you will discover how to define and identify important network data using NetworkX and Python. Finally, you will explore understanding and implementing link prediction and collaborative filtering systems. When you’re finished with this course, you will have the skills and knowledge of NetworkX needed to solve data science and machine learning problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/mp9bzh/mining
What's inside
Syllabus
Course Overview
Understanding Networks
Visualizing Network Data
Defining and Identifying Network Data
Read more
Syllabus
Course Overview
Understanding Networks
Visualizing Network Data
Defining and Identifying Network Data
Read more
Implementing Link Prediction Models for Network Analysis
Implementing Collaborative Filtering Models for Network Analysis
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches network analysis using NetworkX, which is standard in industry
Develops foundational knowledge for solving real-world data science problems
Strengthens existing foundation for intermediate learners
Taught by Justin Flett, who is recognized for their work in network analysis
Save this course
Save Mining Data from Networks to your list so you can find it easily later:
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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 Mining Data from Networks with these
activities:
Review concepts from a related course
Show steps
Start the course with a solid understanding of the essential principles of network analysis, data science, and machine learning.
Browse courses on
Network Analysis
Show steps
-
Review key concepts from a previous course or online resource.
-
Complete practice problems or exercises to test your understanding.
Review Basic Python Programming Concepts
Show steps
Brush up on basic Python programming concepts to ensure a solid foundation for implementing network analysis algorithms.
Browse courses on
Python Programming
Show steps
-
Review variables, data types, and control flow.
-
Practice writing simple Python scripts.
Organize and review course materials
Show steps
Stay organized and ensure you have a comprehensive understanding of course materials.
Show steps
-
Gather all course materials, including lecture notes, assignments, and readings.
-
Review and organize the materials logically.
-
Create a study schedule and plan for regular review of the materials.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Seek guidance from an experienced network analyst
Show steps
Connect with a mentor who can provide valuable insights, support, and advice specific to your learning goals.
Show steps
-
Identify potential mentors through professional networks, conferences, or referral services.
-
Reach out to mentors and request guidance and support.
-
Meet with your mentor regularly to discuss your progress and seek advice.
Follow Tutorials on Collaborative Filtering Systems
Show steps
Explore tutorials on collaborative filtering systems to enhance your understanding of their application in network analysis.
Browse courses on
Network Analysis
Show steps
-
Find tutorials on collaborative filtering systems.
-
Follow the tutorials to learn the concepts and techniques.
-
Implement a simple collaborative filtering system using NetworkX.
Participate in a NetworkX study group
Show steps
Collaborate with peers to discuss course concepts, share perspectives, and enhance your learning.
Show steps
-
Find a study group or create your own with classmates.
-
Meet regularly to review course material, work on assignments, and discuss ideas.
-
Take turns leading discussions and presenting different topics.
Create a Visual Representation of a Network Dataset
Show steps
Create a visual representation of a network dataset to improve your comprehension of network structures and patterns.
Browse courses on
Network Visualization
Show steps
-
Choose a network dataset.
-
Load the dataset into NetworkX.
-
Use NetworkX to create a visual representation of the network.
-
Analyze the visual representation to identify patterns and insights.
Participate in a Network Analysis Study Group
Show steps
Join a study group to discuss concepts, share insights, and reinforce your understanding of network analysis.
Browse courses on
Network Analysis
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course materials and work on projects.
-
Share your knowledge and insights with other group members.
Complete NetworkX exercises
Show steps
Reinforce your understanding of NetworkX by working through exercises and coding challenges.
Show steps
-
Find online or textbook exercises that cover NetworkX concepts.
-
Implement the code solutions and test your results.
-
Review your solutions and identify areas for improvement.
Practice Implementing Link Prediction Models
Show steps
Practice implementing link prediction models to reinforce your understanding of the concepts covered in the course.
Browse courses on
Network Analysis
Show steps
-
Review the concept of link prediction.
-
Choose a dataset and load it into NetworkX.
-
Implement a link prediction model using NetworkX.
-
Evaluate the performance of your model.
Explore advanced NetworkX tutorials
Show steps
Expand your knowledge and learn advanced techniques by following online tutorials or attending workshops.
Show steps
-
Identify tutorials or workshops that cover advanced NetworkX concepts or applications.
-
Follow the tutorials or attend the workshops to learn new techniques.
-
Apply the new techniques to your own projects or assignments.
Develop a network analysis project
Show steps
Deepen your understanding by applying NetworkX to a real-world problem and creating a visual representation.
Show steps
-
Identify a suitable dataset and research problem.
-
Design and implement a network analysis solution using NetworkX.
-
Visualize your results using a network visualization library.
-
Write a report or presentation to document your project.
Contribute to the NetworkX open-source project
Show steps
Deepen your understanding of NetworkX and contribute to the community by participating in open-source development.
Show steps
-
Identify a contribution opportunity within the NetworkX project.
-
Fork the project, make your changes, and submit a pull request.
-
Collaborate with the community to review and refine your contribution.
Participate in a network analysis competition
Show steps
Challenge yourself and showcase your skills by participating in a competition focused on network analysis.
Show steps
-
Research and identify suitable competitions.
-
Form a team or work individually on a project.
-
Develop and implement a solution that addresses the competition's problem statement.
-
Submit your solution and prepare for the evaluation process.
Review concepts from a related course
Show steps
Start the course with a solid understanding of the essential principles of network analysis, data science, and machine learning.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Review key concepts from a previous course or online resource.
- Complete practice problems or exercises to test your understanding.
Review Basic Python Programming Concepts
Show steps
Brush up on basic Python programming concepts to ensure a solid foundation for implementing network analysis algorithms.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review variables, data types, and control flow.
- Practice writing simple Python scripts.
Organize and review course materials
Show steps
Stay organized and ensure you have a comprehensive understanding of course materials.
Show steps
Show steps
Show steps
- Gather all course materials, including lecture notes, assignments, and readings.
- Review and organize the materials logically.
- Create a study schedule and plan for regular review of the materials.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Seek guidance from an experienced network analyst
Show steps
Connect with a mentor who can provide valuable insights, support, and advice specific to your learning goals.
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks, conferences, or referral services.
- Reach out to mentors and request guidance and support.
- Meet with your mentor regularly to discuss your progress and seek advice.
Follow Tutorials on Collaborative Filtering Systems
Show steps
Explore tutorials on collaborative filtering systems to enhance your understanding of their application in network analysis.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Find tutorials on collaborative filtering systems.
- Follow the tutorials to learn the concepts and techniques.
- Implement a simple collaborative filtering system using NetworkX.
Participate in a NetworkX study group
Show steps
Collaborate with peers to discuss course concepts, share perspectives, and enhance your learning.
Show steps
Show steps
Show steps
- Find a study group or create your own with classmates.
- Meet regularly to review course material, work on assignments, and discuss ideas.
- Take turns leading discussions and presenting different topics.
Create a Visual Representation of a Network Dataset
Show steps
Create a visual representation of a network dataset to improve your comprehension of network structures and patterns.
Browse courses on
Network Visualization
Show steps
Show steps
Show steps
- Choose a network dataset.
- Load the dataset into NetworkX.
- Use NetworkX to create a visual representation of the network.
- Analyze the visual representation to identify patterns and insights.
Participate in a Network Analysis Study Group
Show steps
Join a study group to discuss concepts, share insights, and reinforce your understanding of network analysis.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course materials and work on projects.
- Share your knowledge and insights with other group members.
Complete NetworkX exercises
Show steps
Reinforce your understanding of NetworkX by working through exercises and coding challenges.
Show steps
Show steps
Show steps
- Find online or textbook exercises that cover NetworkX concepts.
- Implement the code solutions and test your results.
- Review your solutions and identify areas for improvement.
Practice Implementing Link Prediction Models
Show steps
Practice implementing link prediction models to reinforce your understanding of the concepts covered in the course.
Browse courses on
Network Analysis
Show steps
Show steps
Show steps
- Review the concept of link prediction.
- Choose a dataset and load it into NetworkX.
- Implement a link prediction model using NetworkX.
- Evaluate the performance of your model.
Explore advanced NetworkX tutorials
Show steps
Expand your knowledge and learn advanced techniques by following online tutorials or attending workshops.
Show steps
Show steps
Show steps
- Identify tutorials or workshops that cover advanced NetworkX concepts or applications.
- Follow the tutorials or attend the workshops to learn new techniques.
- Apply the new techniques to your own projects or assignments.
Develop a network analysis project
Show steps
Deepen your understanding by applying NetworkX to a real-world problem and creating a visual representation.
Show steps
Show steps
Show steps
- Identify a suitable dataset and research problem.
- Design and implement a network analysis solution using NetworkX.
- Visualize your results using a network visualization library.
- Write a report or presentation to document your project.
Contribute to the NetworkX open-source project
Show steps
Deepen your understanding of NetworkX and contribute to the community by participating in open-source development.
Show steps
Show steps
Show steps
- Identify a contribution opportunity within the NetworkX project.
- Fork the project, make your changes, and submit a pull request.
- Collaborate with the community to review and refine your contribution.
Participate in a network analysis competition
Show steps
Challenge yourself and showcase your skills by participating in a competition focused on network analysis.
Show steps
Show steps
Show steps
- Research and identify suitable competitions.
- Form a team or work individually on a project.
- Develop and implement a solution that addresses the competition's problem statement.
- Submit your solution and prepare for the evaluation process.
Career center
Learners who complete Mining Data from Networks will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They use tools like NetworkX to improve the accuracy and performance of these models. The skills you learn in this course will be helpful for building and deploying machine learning models.
Network Engineer
Network Engineer
Network Engineers design, build, and maintain computer networks. NetworkX can help you better understand the structure and function of networks. This ability may be helpful for designing and managing networks.
Data Scientist
Data Scientist
Data Scientists analyze data using advanced techniques and approaches. They build models and use tools to uncover trends or relationships. With the help of NetworkX, you will learn to analyze different kinds of data. You may find NetworkX helpful for understanding and visualizing data as well as implementing link prediction and collaborative filtering systems. This may make you more competitive in roles related to Data Science.
Software Engineer
Software Engineer
Software Engineers design and develop software systems. They can use NetworkX to develop applications that analyze and visualize data. This course can provide you with the foundation necessary to implement NetworkX in your own software development projects.
Data Analyst
Data Analyst
Data Analysts translate complex data into insights. They use data visualization to communicate these insights to stakeholders. NetworkX can make it easier to visualize and understand data. This course may be useful for expanding your skills as a Data Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical tools to solve problems in business and industry. NetworkX can be used to optimize resource allocation and scheduling. This course may be helpful for understanding how to use NetworkX to solve real-world problems.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use NetworkX to visualize and model data. This course may be useful for building a foundation in data analysis and visualization.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems at scale. They use NetworkX to model and manage data flow. This course may be useful for understanding how to use NetworkX to design and build data systems.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They use NetworkX to visualize and analyze data. This course may be useful for understanding how to use NetworkX to identify and solve business problems.
Product Manager
Product Manager
Product Managers design and manage products. They use NetworkX to understand and visualize user behavior. This course may be useful for understanding how to use NetworkX to design and manage products.
Data Architect
Data Architect
Data Architects design and build data architectures. They use NetworkX to model and manage data flow. This course may be helpful for understanding how to use NetworkX to design and build data architectures.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They use NetworkX to model and manage cloud resources. This course may be useful for understanding how to use NetworkX to design and manage cloud computing systems.
Security Analyst
Security Analyst
Security Analysts use data to identify and mitigate security threats. They use NetworkX to visualize and analyze data. This course may be useful for understanding how to use NetworkX to identify and mitigate security threats.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They use NetworkX to visualize and analyze data. This course may be useful for understanding how to use NetworkX to make investment decisions.
Marketer
Marketer
Marketers use data to understand and target customers. They use NetworkX to visualize and analyze data. This course may be useful for understanding how to use NetworkX to understand and target customers.
For more career information including salaries, visit:
OpenCourser.com/course/mp9bzh/mining
Reading list
We've selected six 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
Mining Data from Networks.
Provides comprehensive coverage of network analysis techniques and algorithms, using Python for implementation. It serves as a valuable resource for readers interested in gaining proficiency in network analysis.
Serves as a comprehensive handbook for social network analysis, providing a detailed overview of the field. It covers a wide range of topics, making it an excellent reference for researchers and practitioners.
Offers a detailed and rigorous introduction to network theory, covering fundamental concepts and advanced topics. It provides a thorough understanding of the mathematical and theoretical foundations of network analysis.
Offers a comprehensive guide to social network analysis, covering both the theoretical foundations and practical applications. It's an excellent choice for readers seeking a deep understanding of network analysis in social science contexts.
Save
Offers an accessible introduction to network science, exploring the fundamental concepts and techniques. It provides a broad overview of the field and is مناسب for readers new to network analysis.
Save
Provides a comprehensive overview of data mining techniques, including network analysis. It offers a practical approach to data mining, making it suitable for readers interested in applying these techniques to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mp9bzh/mining
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Time
107 hours
Level
Advanced
Via
Pluralsight
Instructor
Justin Flett
Language
English
Good to know
Teaches network analysis using NetworkX, which is standard in industry
Develops foundational knowledge for solving real-world data science problems
Strengthens existing foundation for intermediate learners
Taught by Justin Flett, who is recognized for their work in network analysis
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