Acerca de este Curso

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Nivel principiante
Aprox. 11 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Qué aprenderás

  • Define and discuss big data opportunities and limitations.

  • Work with IBM Watson and analyze a personality through Natural Language Programming (NLP).

  • Examine how AI is used through case studies.

  • Examine and discuss the roles ethics play in AI and big data.

Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
Aprox. 11 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

Logotipo de Universidad de California, Davis

Universidad de California, Davis

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

3 horas para completar

Getting Started and Big Data Opportunities

3 horas para completar
10 videos (Total 94 minutos), 3 lecturas, 1 cuestionario
10 videos
Course Introduction6m
Big Data Overview2m
What is "Big Data"?14m
Digital Footprint5m
Political Data-fusion and No-Sampling (Part 1)14m
Political Data-fusion and No-Sampling (Part 2)3m
Real-time11m
Machine Learning5m
Machine Learning Recommender Systems11m
3 lecturas
About UCCSS10m
A Note From UC Davis10m
Optional/Complementary10m
1 ejercicio de práctica
Module 1 Quiz30m
Semana
2

Semana 2

3 horas para completar

Big Data Limitations

3 horas para completar
8 videos (Total 52 minutos), 1 lectura, 3 cuestionarios
8 videos
Big Data Limitations2m
Footprint ≠ Representativeness10m
Data ≠ Reality6m
Meaning ≠ Meaningful4m
Discrimination ≠ Personalization8m
Correlation ≠ Causation6m
Past ≠ Future10m
1 lectura
Welcome to Peer Review Assignments!10m
2 ejercicios de práctica
Natural Language Processing (NLP) Assignment Task5m
Module 2 Quiz30m
Semana
3

Semana 3

3 horas para completar

Artificial Intelligence

3 horas para completar
15 videos (Total 105 minutos), 1 lectura, 1 cuestionario
15 videos
A Short History of AI9m
State of the Art5m
The Most Intelligent Gamer4m
Search and Robotics7m
Vision and Machine Learning6m
AI Challenges3m
Moral Frames7m
Predictions From Morals6m
Moral Brain Signatures6m
Computational fMRI11m
(A Personal) History of Dialogue Systems6m
The Art of Dialogue10m
Making Conversations10m
AI Telling Stories7m
1 lectura
Optional/Complementary10m
1 ejercicio de práctica
Module 3 Quiz30m
Semana
4

Semana 4

2 horas para completar

Research Ethics

2 horas para completar
13 videos (Total 105 minutos), 1 lectura, 1 cuestionario
13 videos
Origins: Unethical Medical Research8m
Unethical Social Research10m
Taking Responsibility12m
The Common Rule8m
Ethical Computational Social Science10m
Concerns of an AI Pioneer5m
Walker on Ethics10m
Shelton on Ethics7m
Language Acquisition (Complementary)6m
Modeling Framework (Complementary)9m
Computational Model (Complementary)6m
Lessons Learned (Complementary)6m
1 lectura
Slaughterbots10m
1 ejercicio de práctica
Module 4 Quiz30m

Revisiones

Principales revisiones sobre BIG DATA, ARTIFICIAL INTELLIGENCE, AND ETHICS

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Acerca de Programa especializado: Computational Social Science

For more information please view the Computational Social Science Trailer Digital technology has not only revolutionized society, but also the way we can study it. Currently, this is taken advantage of by the most valuable companies in Silicon Valley, the most powerful governmental agencies, and the most influential social movements. What they have in common is that they use computational tools to understand, and ultimately influence human behavior and social dynamics. An increasing part of human interaction leaves a massive digital footprint behind. Studying it allows us to gain unprecedented insights into what society is and how it works, including its intricate social networks that had long been obscure. Computational power allows us to detect hidden patterns through analytical tools like machine learning and to natural language processing. Finally, computer simulations enable us to explore hypothetical situations that may not even exist in reality, but that we would like to exist: a better world. This specialization serves as a multidisciplinary, multi-perspective, and multi-method guide on how to better understand society and human behavior with modern research tools. This specialization gives you easy access to some of the exciting new possibilities of how to study society and human behavior. It is the first online specialization collectively taught by Professors from all 10 University of California campuses....
Computational Social Science

Preguntas Frecuentes

  • El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:

    • es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
    • Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

  • Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.

  • Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.

  • 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."
  • 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.

  • Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.

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