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Christine Alvarado,
Mia Minnes,
and
Leo Porter
In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks!
The opportunities for learning are practically endless in a social network. Who are the “influential” members of the network? What are the sub-communities in the network? Who is connected to whom, and by how many links? These are just some of the questions you can explore in this project.
Read more
In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks!
The opportunities for learning are practically endless in a social network. Who are the “influential” members of the network? What are the sub-communities in the network? Who is connected to whom, and by how many links? These are just some of the questions you can explore in this project.
Read more
In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks!
The opportunities for learning are practically endless in a social network. Who are the “influential” members of the network? What are the sub-communities in the network? Who is connected to whom, and by how many links? These are just some of the questions you can explore in this project.
We will provide you with a real-world data set and some infrastructure for getting started, as well as some warm up tasks and basic project requirements, but then it’ll be up to you where you want to take the project. If you’re running short on ideas, we’ll have several suggested directions that can help get your creativity and imagination going. Finally, to integrate the skills you acquired in course 4 (and to show off your project!) you will be asked to create a video showcase of your final product.
Register for this course and see more details by visiting:
OpenCourser.com/course/1vt0wd/capstone
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What's inside
Syllabus
Introduction and Warm up
Welcome to our capstone project! In the last four courses in this specialization you've learned many core data structures and algorithms, and applied them to three different real-world projects. In this capstone project you'll be doing a project very much like the projects from these other courses, only it will be almost entirely directed by you! In this first week you'll get warmed up by playing around with the data that will form the backbone of this project: social network data. Then you'll get back into writing code by implementing a couple of graph algorithms to answer questions about this data.
Read more
Syllabus
Introduction and Warm up
Welcome to our capstone project! In the last four courses in this specialization you've learned many core data structures and algorithms, and applied them to three different real-world projects. In this capstone project you'll be doing a project very much like the projects from these other courses, only it will be almost entirely directed by you! In this first week you'll get warmed up by playing around with the data that will form the backbone of this project: social network data. Then you'll get back into writing code by implementing a couple of graph algorithms to answer questions about this data.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops advanced data analysis and visualization skills for several real-world applications
Provides hands-on experience with a real-world data set of social network data
Suitable for learners with experience in data structures and algorithms who seek to apply their knowledge to a real-world project
Guided and independent project work allows learners to explore topics of their interest and develop their own solutions
Requires learners to demonstrate their understanding by creating a video showcase of their final product, developing their communication skills
Taught by experienced instructors who provide valuable insights and guidance throughout the course
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Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Capstone: applying data structures to network analysis
According to learners, this capstone project provides a valuable culmination of the preceding specialization courses, allowing students to apply skills in data structures and algorithms to analyze real-world social network data. Many appreciate the opportunity for a self-directed project, finding it a rewarding challenge that solidifies their understanding. However, the open-ended nature can also be a significant challenge for some who prefer more structure or guidance. Implementing complex algorithms and dealing with data preprocessing are noted as areas requiring careful attention. The peer review process is frequently mentioned as being of variable quality. Overall, it is considered a solid, though demanding, project that effectively ties together the specialization.
Requires creating a video showcase.
"The video presentation requirement was a bit unusual but a good way to demonstrate the project."
"I found the video format challenging compared to a written report."
"Presenting my work visually helped me organize my thoughts about the project."
Assumes mastery of prior courses.
"You absolutely need a solid foundation from the earlier specialization courses to succeed."
"Don't attempt this capstone unless you are comfortable with data structures and algorithms."
"This course is challenging and builds heavily on prerequisite knowledge."
Project is open-ended and independent.
"The self-directed nature of the project was perfect for me, allowing creative freedom."
"I struggled a bit with the lack of specific instructions, it felt too open-ended at times."
"Requires significant independent work and problem-solving."
Working with practical network datasets.
"Analyzing the provided social network data was very interesting and relevant."
"The dataset was good, although like real data, it required some cleaning."
"It was exciting to apply the learned techniques to a practical problem using real data."
Excellent practical application of skills.
"This capstone was a fantastic way to synthesize everything learned in the previous courses."
"I found the project extremely rewarding, truly putting the concepts into practice."
"Applying data structures and algorithms to real network data was a great experience."
Implementing algorithms can be difficult.
"Implementing some of the required graph algorithms was more difficult than anticipated."
"Debugging the code for the larger project took a significant amount of time."
"Successfully translating algorithm theory into working code is the main hurdle."
Feedback varies significantly.
"The quality of peer reviews was inconsistent; some were helpful, others not."
"I wish the peer grading process was more reliable and informative."
"Receiving feedback from peers is a good idea, but the execution felt hit-or-miss."
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 Capstone: Analyzing (Social) Network Data with these
activities:
NetworkX tutorial for Python
Show steps
Familiarize yourself with NetworkX, a powerful Python library for network analysis, to enhance your project implementation.
Browse courses on
NetworkX
Show steps
-
Install NetworkX library
-
Follow online tutorials or documentation
-
Experiment with NetworkX functions
Graph traversal practice
Show steps
Sharpen your graph traversal skills by practicing different algorithms on smaller datasets or coding challenges.
Browse courses on
Graph Traversal
Show steps
-
Implement breadth-first search (BFS) algorithm
-
Implement depth-first search (DFS) algorithm
-
Apply these algorithms to solve coding problems
Analyze Twitter network dataset
Show steps
Kickstart your project by analyzing a real-world Twitter network dataset, allowing you to apply the algorithms and concepts learned in class.
Browse courses on
Graph Algorithms
Show steps
-
Gather necessary resources (libraries, data)
-
Load and preprocess the Twitter network data
-
Identify and implement relevant graph algorithms
-
Interpret and visualize your results
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice graph algorithms
Show steps
Practice implementing graph algorithms to improve your understanding of how to analyze social networks.
Browse courses on
Graph Algorithms
Show steps
-
Implement a breadth-first search algorithm.
-
Implement a depth-first search algorithm.
Create a social network analysis report
Show steps
Develop your analytical skills and apply your knowledge of social network analysis to a real-world dataset.
Browse courses on
Social Network Analysis
Show steps
-
Gather data from a social network.
-
Clean and preprocess the data.
-
Run social network analysis algorithms.
-
Write a report summarizing your findings.
NetworkX tutorial for Python
Show steps
Familiarize yourself with NetworkX, a powerful Python library for network analysis, to enhance your project implementation.
Browse courses on
NetworkX
Show steps
Show steps
Show steps
- Install NetworkX library
- Follow online tutorials or documentation
- Experiment with NetworkX functions
Graph traversal practice
Show steps
Sharpen your graph traversal skills by practicing different algorithms on smaller datasets or coding challenges.
Browse courses on
Graph Traversal
Show steps
Show steps
Show steps
- Implement breadth-first search (BFS) algorithm
- Implement depth-first search (DFS) algorithm
- Apply these algorithms to solve coding problems
Analyze Twitter network dataset
Show steps
Kickstart your project by analyzing a real-world Twitter network dataset, allowing you to apply the algorithms and concepts learned in class.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Gather necessary resources (libraries, data)
- Load and preprocess the Twitter network data
- Identify and implement relevant graph algorithms
- Interpret and visualize your results
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice graph algorithms
Show steps
Practice implementing graph algorithms to improve your understanding of how to analyze social networks.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Implement a breadth-first search algorithm.
- Implement a depth-first search algorithm.
Create a social network analysis report
Show steps
Develop your analytical skills and apply your knowledge of social network analysis to a real-world dataset.
Browse courses on
Social Network Analysis
Show steps
Show steps
Show steps
- Gather data from a social network.
- Clean and preprocess the data.
- Run social network analysis algorithms.
- Write a report summarizing your findings.
Career center
Learners who complete Capstone: Analyzing (Social) Network Data will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, deploy, and maintain machine learning systems that solve real-world problems such as natural language processing, image recognition, and speech recognition. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Machine Learning Engineer because it teaches how to analyze and extract insights from social network data, which can be valuable for developing and improving machine learning models.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data to help businesses make better decisions. They use a variety of statistical and machine learning techniques to uncover patterns and trends in data. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Data Scientist because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications and systems. They use a variety of programming languages and tools to create applications that meet the needs of end users. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Software Engineer because it teaches how to design and implement data structures and algorithms, as well as how to analyze and interpret data.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. They use a variety of statistical and machine learning techniques to uncover patterns and trends in data. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Data Analyst because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Business Analyst
Business Analyst
Business Analysts help businesses understand and improve their business processes. They use a variety of data analysis and modeling techniques to identify problems and opportunities, and to develop recommendations for improvement. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Business Analyst because it teaches how to analyze and interpret data, as well as how to build and interpret machine learning models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze and interpret financial data. They use these models to make investment decisions and to develop trading strategies. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Quantitative Analyst because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve problems in a variety of industries, including manufacturing, logistics, and healthcare. They use these models to improve efficiency and productivity. This course "Capstone: Analyzing (Social) Network Data" may be useful for an Operations Research Analyst because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts help businesses manage their supply chains. They use a variety of data analysis and modeling techniques to identify problems and opportunities, and to develop recommendations for improvement. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Supply Chain Analyst because it teaches how to analyze and interpret data, as well as how to build and interpret machine learning models.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research to understand consumer behavior and trends. They use this research to help businesses develop new products and services, and to improve marketing campaigns. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Market Research Analyst because it teaches how to analyze and interpret data, as well as how to build and interpret machine learning models.
Customer Relationship Management (CRM) Analyst
Customer Relationship Management (CRM) Analyst
CRM Analysts help businesses manage their customer relationships. They use a variety of data analysis and modeling techniques to identify problems and opportunities, and to develop recommendations for improvement. This course "Capstone: Analyzing (Social) Network Data" may be useful for a CRM Analyst because it teaches how to analyze and interpret data, as well as how to build and interpret machine learning models.
Risk Analyst
Risk Analyst
Risk Analysts help businesses identify and manage risks. They use a variety of data analysis and modeling techniques to assess the likelihood and impact of risks, and to develop recommendations for mitigation. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Risk Analyst because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess and manage risks. They work in a variety of industries, including insurance, finance, and healthcare. This course "Capstone: Analyzing (Social) Network Data" may be useful for an Actuary because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use these data to make inferences about populations and to develop models for making predictions. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Statistician because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Data Mining Engineer
Data Mining Engineer
Data Mining Engineers use data mining techniques to extract valuable information from large datasets. They work in a variety of industries, including retail, finance, and healthcare. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Data Mining Engineer because it provides hands-on experience with data analysis and visualization techniques, as well as an understanding of how to build and interpret machine learning models.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and implement computer systems and applications. They use a variety of programming languages and tools to create software that meets the needs of end users. This course "Capstone: Analyzing (Social) Network Data" may be useful for a Computer Scientist because it teaches how to design and implement data structures and algorithms, as well as how to analyze and interpret data.
For more career information including salaries, visit:
OpenCourser.com/course/1vt0wd/capstone
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
Capstone: Analyzing (Social) Network Data.
Covers the core principles and theories of social network analysis, including measures of centrality, graph algorithms, community detection, and network visualization techniques. It provides a comprehensive overview of the field and offers practical guidance on applying these methods to real-world problems.
Save
Focuses on social network analysis and mining from a computational perspective. It covers techniques for collecting, processing, and analyzing large-scale social network data, as well as methods for identifying patterns and trends within these networks.
Save
Introduces the history and key concepts of network analysis, with a focus on bibliographic networks and citation data. It provides insights into the structure and evolution of knowledge domains and offers practical techniques for mapping and analyzing these networks.
Provides a foundational understanding of graph theory, covering fundamental concepts such as connectivity, trees, cycles, and graph algorithms. Although it does not focus specifically on social networks, the knowledge of graph theory presented in this book is highly valuable for analyzing network data.
Offers an introduction to the field of complex networks, with a focus on mathematical and statistical techniques for analyzing network structure and dynamics. It provides a solid foundation for understanding the behavior of complex networks and their applications.
Save
Covers a wide range of data mining concepts and techniques, including data preprocessing, clustering, classification, and association analysis. Although it does not focus specifically on social networks, the techniques presented in this book are widely used for analyzing network data.
Save
Provides a practical introduction to data visualization techniques, with an emphasis on creating clear and effective visualizations for social network data. It covers topics such as graph visualization, node-link diagrams, and interactive visualizations.
Provides a comprehensive overview of sentiment analysis and opinion mining techniques. Although not directly focused on social network analysis, it covers techniques that are valuable for analyzing sentiments and opinions expressed within social networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1vt0wd/capstone
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Rating
4.7
Effort
17 hours to complete
Via
Coursera
Institution
University of California San Diego
Instructors
Christine Alvarado
Mia Minnes
Leo Porter
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Object Oriented Java Programming: Data Structures and Beyond. It's comprised of six courses:
- Data Structures and Performance
- Object Oriented Programming in Java
- Capstone: Analyzing (Social) Network Data (viewing)
- Mastering the Software Engineering Interview
- Advanced Data Structures in Java
- Viral Marketing and How to Craft Contagious Content
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