Capstone: Analyzing (Social) Network Data from Coursera

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.

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.

Project Definition and Scope

Now that you're warmed up, it's time to get started planning for the bulk of your capstone project. This week you will identify several questions you'd like to answer about the social network data. For each of these questions, you'll research and evaluate data structures and algorithms that would be useful in implementing a solution. Defining the scope of your project and anticipating bottlenecks and tricky spots is tough but extremely valuable. You'll use asymptotic analysis to guide and refine your design.

Capstone Implementation: Mini-project

Now that you've identified the two problems you want to solve, this week you'll work to solve the easier of the two. This week you are predominately on your own to work independently. To solve the problem, you'll likely create small datasets for testing, research existing solutions to related problems, implement a solution, test your solution, and analyze the algorithmic runtime of the solution. You can optionally write-up a report of your work for peer-review feedback.

Capstone Implementation: Full project checkpoint

This week, you will work on your own on the larger problem you aim to solve. You'll have two weeks (this and the next) to solve the larger problem and submit a report for peer feedback. For this week, you should aim to create small test datasets, research exist solutions, and analyze the runtime of your potential solutions. You should also research datasets which might be particularly interesting for your problem.

Capstone Implementation: Full project final deadline

Now you get to finalize your project! This week, you will finish your solution to the larger problem and submit a report for peer feedback. This is also an opportunity for reflection about what went well and what went poorly in the process of completing the project. It is also an opportunity to reflect on how far your technical skills have advanced since the beginning of this specialization.

Capstone oral report

In this week, you get to present your project to the learner community! This will combine all the skills you've learned in the specialization: algorithm analysis, object oriented programming, design and use of data structures, and presenting your work with confidence. We look forward to seeing what you've created!

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Data structures for social networks

Learners say this course is "okay" and that it focuses mainly on graphs. They would like to learn more than one data structure.

Course focuses on graphs.

"I would like to learn something besides graphs which I don't find interesting."

Learners expected to learn more than one data structure.

"I thought this course was on data structures but it seems to be mostly on graphs which are only one data structure."

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

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Familiarize yourself with NetworkX, a powerful Python library for network analysis, to enhance your project implementation.

Browse courses on NetworkX

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Install NetworkX library
Follow online tutorials or documentation
Experiment with NetworkX functions

Graph traversal practice

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Sharpen your graph traversal skills by practicing different algorithms on smaller datasets or coding challenges.

Browse courses on Graph Traversal

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Implement breadth-first search (BFS) algorithm
Implement depth-first search (DFS) algorithm
Apply these algorithms to solve coding problems

Analyze Twitter network dataset

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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

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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

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Show all five activities

Practice graph algorithms

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Practice implementing graph algorithms to improve your understanding of how to analyze social networks.

Browse courses on Graph Algorithms

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Implement a breadth-first search algorithm.
Implement a depth-first search algorithm.

Create a social network analysis report

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Develop your analytical skills and apply your knowledge of social network analysis to a real-world dataset.

Browse courses on Social Network Analysis

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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:

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Supply Chain 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Computer Scientist

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.

See salaries and explore the career path for Market Research Analyst

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.

See salaries and explore the career path for Data Mining Engineer

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.

See salaries and explore the career path for Customer Relationship Management (CRM) Analyst