This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.
About this Course
- 5 stars72.05%
- 4 stars21.81%
- 3 stars4.41%
- 2 stars0.73%
- 1 star0.98%
TOP REVIEWS FROM BIG DATA, ARTIFICIAL INTELLIGENCE, AND ETHICS
Excellent course. Helps in developing a good base in artificial intelligence for beginners. The explanations and lectures are very clear and understandable. Won't bore the listeners.
I was expecting more about the ethics side of AI, from the critical point of view. Besides, some quiz questions were not clear enough. But anyway, I learnt a lot of things.
It was a great experience learning a new language ;) . This course gives a lot info regarding big data ,its applications and IBM (I loved nature learning processing ).
Excellent course. Professon Martin Hilbert has done an excellent jon in terms of content, presentation as well as explanation. Thank you.
About the Computational Social Science Specialization
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