CONGRATULATIONS! Not only did you accomplish to finish our intellectual tour de force, but, by now, you also already have all required skills to execute a comprehensive multi-method workflow of computational social science. We will put these skills to work in this final integrative lab, where we are bringing it all together. We scrape data from a social media site (drawing on the skills obtained in the 1st course of this specialization). We then analyze the collected data by visualizing the resulting networks (building on the skills obtained in the 3rd course). We analyze some key aspects of it in depth, using machine learning powered natural language processing (putting to work the insights obtained during the 2nd course). Finally, we use a computer simulation model to explore possible generative mechanism and scrutinize aspects that we did not find in our empirical reality, but that help us to improve this aspect of society (drawing on the skills obtained during the 4th course of this specialization). The result is the first glimpse at a new way of doing social science in a digital age: computational social science. Congratulations! Having done all of this yourself, you can consider yourself a fledgling computational social scientist!
Overview
Syllabus
- Getting Started and Milestone 1
- For this milestone, you will again web scrape videos from two YouTube channels. You will be assigned two channels to scrape. In contrast to the previous version of this exercise, you will NOT scrape the featured videos of the specified news channel, but the search results of the name of the news channel in combination with your name.
- Milestone 2: Social Network Analysis
- In this milestone, you will analyze a social network with help of the software Gephi.
- Milestone 3: Natural Language Processing
- In this milestone of our Integrative Lab, you will select two of the key videos identified with help of our SNA, and analyze the sentiment and emotions contained in the comment sections of the videos. We use NLP from IBM Watson for this.
- Milestone 4: Agent-Based Computer Simulations
- In this milestone, you will take all the data you created in the previous milestones and use a two-step flow model and discover how ideas can diffuse into society. Through this exercise you will grow your own artificial society from the bottom-up.
Taught by
Martin Hilbert
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Reviews
4.7 rating, based on 32 Class Central reviews
4.6 rating at Coursera based on 37 ratings
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I think this course was amazing due to different things: Methodology: it was very interesting for me to work under the proposed scientific method. I also enjoyed learning how Computational Social Science defines a methodology for creating new knowl…
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I'm excited to go through the course! It was an amazing adventure to the world of CSS, machine learning, scientific approaches - induction and deduction, agent based modelling and web scraping. I was able to apply the knowledge I got in this course…
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You start from scratch and suddenly, boom, you learn a bunch of super interesting techniques for exploring new questions in social science. Worth the investment.
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This was a very helpful lab and course I think I will be using tools like Gephi to my work as it can help with the networking analysis of how our business operates. I did fine all the practical labs more useful and easy to understand than the methodologies and theory aspect but that's just me.
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This final project portion was definitely my favorite course on coursera. With everything that we have learned throughout the course, I was able to use literally every aspect from web-scraping, social networks, and simulations to produce something that I will be able to use outside of school and beyond. It truly is the introduction to computational social science and overall very easy to follow!
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Great way to bring together all of the previous courses into one interactive lab involving analyzing social networks and building my own, personal artificial society. This project is not intimidating in the slightest, it is actually quite fun. I had never previously coded before, and the coding was easy to follow and replicate. Overall, I am very pleased that I took this course!
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It was the repeat exercise of previous assignments. I was waiting for more points about how I can use all I have learned in a social science research altogether.
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This course is considered to be the final project to apply all of the previous concepts learned from the previous courses in the Computational Social Science class in Coursera. This is a final test to use all of the skills learned and see how you can apply it in a real life situation.
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Cici QIUThis final four capstone projects wrap up all the information we learnt from the previous courses. The fourth project, the ABM modeling, is the most interestingly designed one, since it deals with the classical "two-step flow" model in communication studies and make it easy to understand. More importantly, it makes it open to more discussion and explorations.
Looking forward to courses on experiments! -
I found the final project to be a good way to integrate all of the course concepts we covered into one cohesive assignment.
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Really enjoyed running through the exercises for each course and seeing how the workflow works together. I do wish there was a way the final exercises in agent based modeling would somehow tie into the data collected for the previous weeks' exercises.
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The project was a success integrating all the projects which was previously in silos and helped me understand computational social science better. No words are enough to express my gratitude to you Prof Martin Hilbert and your team!
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Great beginners learning course, great way to bring all modules learned thru out the course together. Excellent hands-on.
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This is an excellent course for beginners. Learned alot about the Big Data, Social Network Analysis.
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i think the window that is opened by technology into the social science aspect is a defining moment for humans, at least now we can imagine how things might connect or work by the dynamic nature of the human relationship . it is an eye opening for none specialized person like me , i can imagine the enthusiasm of social scientist by these new methods and tools.
final note - despite the complexity of the subject - your made it exciting and draw me to finish it early. thank you for all your efforts .
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Thank you so much for offering this course. It was a good wrap-up for the specialization. I also got a good feel of what else needs to be done and where to head to next. It took longer than expected for me to finish the specialization, because of having a baby in the middle of covid but nonetheless I'm really greatful that you made the courses available online and extended the deadlines. I got a good introduction into computational social sciences and will continue to learn.
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The courses were excellent with great introductory and sufficient in-depth content to understand computational social science. In particular, the course on computer simulation was simple to grasp with a clear and concise explanation. At the same time, the evaluations of using online data were insightful that gave a critical perspective on using digital data.
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The work in this course is intense, many topics are explored and in all there is a practical component that makes you contextualize the theory that you have just learned, it has a few touches of humor so as not to make it more dynamic. Thank you very much Martin Hilbert for this opportunity
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In the final capstone project, please create an upload button to submit our videos like in previous courses. And enable copy-paste formatting.
Or are you doing this on purpose??
Otherwise it was a perfect course to get an overview of big data, AI, and computer simulation. Thank you. -
This was an excellent course. The practical nature of this final course in the series enabled me to feel like I could 'put it all together'. I enjoyed using all of the same tools again for a new project and believe it helped me solidify my learning. Highly recommend this course
