このコースについて

90,105 最近の表示
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約11時間で修了
英語
字幕:英語

学習内容

  • 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.

共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約11時間で修了
英語
字幕:英語

提供:

カリフォルニア大学デービス校(University of California, Davis) ロゴ

カリフォルニア大学デービス校(University of California, Davis)

シラバス - 本コースの学習内容

1

1

3時間で修了

Getting Started and Big Data Opportunities

3時間で修了
10件のビデオ (合計94分), 3 readings, 1 quiz
10件のビデオ
Course Introduction6 分
Big Data Overview2 分
What is "Big Data"?14 分
Digital Footprint5 分
Political Data-fusion and No-Sampling (Part 1)14 分
Political Data-fusion and No-Sampling (Part 2)3 分
Real-time11 分
Machine Learning5 分
Machine Learning Recommender Systems11 分
3件の学習用教材
About UCCSS10 分
A Note From UC Davis10 分
Optional/Complementary10 分
1の練習問題
Module 1 Quiz30 分
2

2

3時間で修了

Big Data Limitations

3時間で修了
8件のビデオ (合計52分), 1 reading, 3 quizzes
8件のビデオ
Big Data Limitations2 分
Footprint ≠ Representativeness10 分
Data ≠ Reality6 分
Meaning ≠ Meaningful4 分
Discrimination ≠ Personalization8 分
Correlation ≠ Causation6 分
Past ≠ Future10 分
1件の学習用教材
Welcome to Peer Review Assignments!10 分
2の練習問題
Natural Language Processing (NLP) Assignment Task5 分
Module 2 Quiz30 分
3

3

3時間で修了

Artificial Intelligence

3時間で修了
15件のビデオ (合計105分), 1 reading, 1 quiz
15件のビデオ
A Short History of AI9 分
State of the Art5 分
The Most Intelligent Gamer4 分
Search and Robotics7 分
Vision and Machine Learning6 分
AI Challenges3 分
Moral Frames7 分
Predictions From Morals6 分
Moral Brain Signatures6 分
Computational fMRI11 分
(A Personal) History of Dialogue Systems6 分
The Art of Dialogue10 分
Making Conversations10 分
AI Telling Stories7 分
1件の学習用教材
Optional/Complementary10 分
1の練習問題
Module 3 Quiz30 分
4

4

2時間で修了

Research Ethics

2時間で修了
13件のビデオ (合計105分), 1 reading, 1 quiz
13件のビデオ
Origins: Unethical Medical Research8 分
Unethical Social Research10 分
Taking Responsibility12 分
The Common Rule8 分
Ethical Computational Social Science10 分
Concerns of an AI Pioneer5 分
Walker on Ethics10 分
Shelton on Ethics7 分
Language Acquisition (Complementary)6 分
Modeling Framework (Complementary)9 分
Computational Model (Complementary)6 分
Lessons Learned (Complementary)6 分
1件の学習用教材
Slaughterbots10 分
1の練習問題
Module 4 Quiz30 分

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Computational Social Science専門講座について

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

よくある質問

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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  • サブスクライブすると、7日間の無料トライアルを体験できます。この期間中は解約金なしでキャンセルできます。それ以降、払い戻しはありませんが、サブスクリプションをいつでもキャンセルできます。返金ポリシーをすべて表示します

  • はい。受講料の支払いが難しい受講生に、Coursera(コーセラ)は学資援助を提供しています。左側の[登録]ボタンの下にある[学資援助]のリンクをクリックして申請してください。申請書の入力を促すメッセージが表示され、承認されると通知が届きます。キャップストーンプロジェクトを含む専門講座の各コースでこのステップを完了する必要があります。詳細

  • 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.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。