Big Data, Artificial Intelligence, and Ethics

• Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?

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  • Der Kurs bietet möglicherweise keine Gastoption an. Sie können stattdessen eine kostenlose Testversion ausprobieren oder finanzielle Unterstützung beantragen.
  • Der Kurs kann stattdessen "Vollständiger Kurs ohne Zertifikat" anbieten. Mit dieser Option können Sie alle Kursmaterialien einsehen, die erforderlichen Aufgaben einreichen und eine Endnote erhalten. Dies bedeutet auch, dass Sie kein Zertifikat erwerben können.

• Was bekomme ich, wenn ich diese Spezialisierung abonniere?

  Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.

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• What do students say after completion?

  These are some of the reflections shared by students who have worked through the content of the Specialization on Computational Social Science:

  • "Highly enjoyable and most importantly, giving me exceptionally important skills to fulfill my job requirements at a new position in Munich. You may be interested to know the impact of your course on salary and in my case, the knowledge and certification gained adds about another Euro 20.000 on the annual salary (taking it to about Euro 120.000 p.a.)."
  • "My overall impression of this was: I can't wait to use this for other stuff!!"
  • "Best course I have taken. I wish more online courses structured like this would be offered."
  • "The fact that these tools are so easily usable and attainable is incredible in my mind. Not only do we have access to them like we have access to things like Facebook and Twitter, but they're FREE."
  • "I absolutely think that these tools could be used in my future jobs, or even as a personal reflection. If you scrape and analyze the comments/reactions that your business gets on Youtube, Twitter, Instagram, etc., what does their language use say about how they interact with your brand — or what your brand brings out in them?"
  • "Wow, this is cool and fun stuff. Even though I may not pursue anything social-science related in the near future, it is still nice to learn and get to experience all of these tools that computational social science offers and benefits in all kinds of careers and fields of study."
  • "I particularly enjoyed the web-scraping for some reason. It feels very advanced although its very easy. ...It seems to be a very fast and efficient way of grabbing data."
  • "I enjoyed playing around with machine learning! ...It was also amazing to me how quickly it was able to grasp and learn our input in seconds. It makes me wonder how much more technology will advance in these next few years... It's scary but fascinating."

  • "The most interesting aspect was the fact that these tools are all free and online. In the past, only researchers at well-funded universities had access to programs like the ones we used in all of our labs. But now, even someone without much technical knowledge on complex software can use these tools."
  • "I am so surprised that these tools are available to anyone through a simple download, and even more so that they are very user friendly and easy to learn how to navigate. I plan on starting a clothing line company in the future and I think it will be really helpful for me to be able to analyze so much online data."
  • "As an Environmental Policy Analysis and Planning major, I was fascinated to learn that there is a feasible way to simulate policy implementation and impact multiple times within a short span of time."
  • "UCCSS has allowed me to feel more confident in my abilities with a computer and to better understand companies like Facebook or Twitter. ...these tools really are powerful but also dangerous. ...It allows powerful individuals to manipulate ideas."
  • "Throughout the course, the content was challenging, but when it was finally applied to the labs at the end of each module, it was really rewarding to see everything play out. It was even more rewarding when it made sense too! ... I'm really glad I took this course! It was definitely a challenge, but I'm glad I got to experience and learn about so many topics I never knew even existed."
  • "It was fun seeing the results of the code that I made, and I never thought that I would be doing something like this in my life. The results also showed me what the society would look like.... Social network analysis and web scraping could be the tools that I use in my future job as all the internship that I'm looking now all related to social media or digital media."
  • "My career aspiration is to be a digital marketing expert. These computational tools have enormous implications for the field."
  • "I really really loved that this class let me learn hands-on and gave me experience with tools that have real world application and combine STEM & social science. I think that a lot of these tools are useful far beyond homework activities."
  • "I did my MA in Social Work in India. I am trying to make a come-back in my field after a long career break. I had been hearing Big Data and Data Science everywhere and wondered if there is a link between these and Social Sciences. This specialization gave me needed answers and is helping me to gain very useful skills... Thank you so much for bringing this specialization. You are a very good instructor and made these courses are a smooth sail."

• Since this Specialization is a collective effort from all UC campuses, who teaches it?

  This Specialization on Computational Social Science is the result of a collective effort with contributions from Professors from all 10 campuses of the University of California. It is coordinated by Martin Hilbert, from UC Davis, and counts with lectures from:

  1) UC Berkeley: Joshua Blumenstock, Prof. iSchool; Stuart Russell, Professor of Computer Science and Engineering.

  2) UC Davis: Martin Hilbert, Prof., Dpt. of Communication & Seth Frey, Prof., Dpt. of Communication & Cynthia Gates, Director of the IRB.

  3) UC Irvine: Lisa Pearl, Prof. Cognitive Sciences.

  4) UC Los Angeles: PJ Lamberson, Assistant Prof. Communication Studies.

  5) UC Merced: Paul Smaldino, Prof. Cognitive and Information Sciences.

  6) UC Riverside: Christian Shelton, Prof. Computer Science.

  7) UC San Diego: James Fowler, Prof. Global Public Health and Political Science.

  8) UC San Francisco: Maria Glymour, Associate Prof. School of Medicine, Social Epidemiology & Biostatistics.

  9) UC Santa Barbara: René Weber, Prof. Dpt. of Communication & Media Neuroscience Lab (with Frederic Hopp).

  10) UC Santa Cruz: Marilyn Walker, Prof. Computer Science, Director, Computational Media.

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