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배울 내용

  • 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.

공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
완료하는 데 약 11시간 필요
영어
자막: 영어

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캘리포니아 대학교 데이비스 캠퍼스

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 3시간 필요

Getting Started and Big Data Opportunities

완료하는 데 3시간 필요
10개 동영상 (총 94분), 3 개의 읽기 자료, 1 개의 테스트
10개의 동영상
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
3개의 읽기 자료
About UCCSS10m
A Note From UC Davis10m
Optional/Complementary10m
1개 연습문제
Module 1 Quiz30m
2

2

완료하는 데 3시간 필요

Big Data Limitations

완료하는 데 3시간 필요
8개 동영상 (총 52분), 1 개의 읽기 자료, 3 개의 테스트
8개의 동영상
Big Data Limitations2m
Footprint ≠ Representativeness10m
Data ≠ Reality6m
Meaning ≠ Meaningful4m
Discrimination ≠ Personalization8m
Correlation ≠ Causation6m
Past ≠ Future10m
1개의 읽기 자료
Welcome to Peer Review Assignments!10m
2개 연습문제
Natural Language Processing (NLP) Assignment Task5m
Module 2 Quiz30m
3

3

완료하는 데 3시간 필요

Artificial Intelligence

완료하는 데 3시간 필요
15개 동영상 (총 105분), 1 개의 읽기 자료, 1 개의 테스트
15개의 동영상
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
1개의 읽기 자료
Optional/Complementary10m
1개 연습문제
Module 3 Quiz30m
4

4

완료하는 데 2시간 필요

Research Ethics

완료하는 데 2시간 필요
13개 동영상 (총 105분), 1 개의 읽기 자료, 1 개의 테스트
13개의 동영상
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
1개의 읽기 자료
Slaughterbots10m
1개 연습문제
Module 4 Quiz30m

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BIG DATA, ARTIFICIAL INTELLIGENCE, AND ETHICS의 최상위 리뷰

모든 리뷰 보기

Computational Social Science 특화 과정 정보

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

자주 묻는 질문

  • 강의 및 과제 이용 권한은 등록 유형에 따라 다릅니다. 청강 모드로 강좌를 수강하면 대부분의 강좌 자료를 무료로 볼 수 있습니다. 채점된 과제를 이용하고 수료증을 받으려면 청강 도중 또는 이후에 수료증 경험을 구매해야 합니다. 청강 옵션이 표시되지 않는 경우:

    • 강좌에서 청강 옵션을 제공하지 않을 수 있습니다. 대신 무료 평가판을 사용하거나 재정 지원을 신청할 수 있습니다.
  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 구독하는 경우, 취소해도 요금이 청구되지 않는 7일간의 무료 평가판을 이용할 수 있습니다. 해당 기간이 지난 후에는 환불이 되지 않지만, 언제든 구독을 취소할 수 있습니다. 전체 환불 정책 보기.

  • 예, Coursera에서는 수업료를 낼 수 없는 학습자를 위해 재정 지원을 제공합니다. 왼쪽에 있는 등록 버튼 아래 재정 지원 링크를 클릭하면 지원할 수 있습니다. 신청서를 작성하라는 메시지가 표시되며 승인되면 알림을 받습니다. 성취 프로젝트를 포함하여 전문 분야의 각 강좌에서 이 단계를 완료해야 합니다. 자세히 알아보기.

  • 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.

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