At the end of the course, the student is able to apply the knowledge acquired in Languages and algorithms for Artificial Intelligence in order to carry out autonomously a project focusing on a topic agreed upon with the teacher. The contents of the project will be agreed with the teacher in charge of the course.

Python or R – which tool should you learn? If I got a penny each time I came across this question.. There is a widely held belief that mastering data science is about learning how to apply techniques in Python or R. Or any other tool. That tool has become the central point around which all other data science functions revolve. The assumption (or myth) is that being able to write code using existing libraries (numpy, scikit-learn, caret, etc.) should be enough to label yourself an expert.

Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems. Unlike these disciplines, however, data science education remains heavily focused on theory and methods, and practical coursework typically revolves around cleaned or simplified data sets that have little analog in professional applications. We believe that the environment in which new data scientists are trained should more accurately reflect that in which they will eventually practice and propose here a data science master's degree program that takes inspiration from the residency model used in medicine. Students in the suggested program would spend three years working on a practical problem with an industry, government, or nonprofit partner, supplemented with coursework in data science methods and theory. We also discuss how this program can also be implemented in shorter formats to augment existing professional masters programs in different disciplines. This approach to learning by doing is designed to fill gaps in our current approach to data science education and ensure that students develop the skills they need to practice data science in a professional context and under the many constraints imposed by that context.

AI Saturdays, also known as AI6, is a community-driven, non-profit movement established to offer education on artificial intelligence (AI) to the masses. Through structured study groups, lectures and project work, the organizers aim to teach everybody how they can use AI in their everyday lives. With the first chapter established in December 2017 in Singapore by Nurture.AI CEO Mr. Yap Jia Qing, followed by the second soon after in Kuala Lumpur, it could be said that the AI6 movement is in its infancy. Yet within a few months, the initiative has grown to include 103 chapters across six continents, including 47 in Asia. At time of writing, there are over 5,000 participants worldwide, from Kathmandu to California.