Workshops and Short-Duration Courses
Augmented Cities C-Tech Summer Camp
The Program in Augmented Cities C-Tech Summer Camp is a summer camp hosted by NOVA Cidade – Urban Analytics Lab, where you will have an hands-on learning experience in augmented reality and participate in the MIT Portugal C-Tech project (Climate Driven Technologies for Low Carbon Cities) challenge to develop a real smart sustainable city AR application in a team project using Unity and Hololens 2.
Summer Course: Workshop - Mixed-Methods Research
The objective of this workshop is to help participants acquire a greater appreciation for theory development, to understand challenges in developing theory, and to discuss approaches to developing good theory. These particular skills will be contextualized within a framework of developing and sustaining a viable research program in the social and behavioral sciences.
Summer Course: Publishing in Top Tier Journals
The objective of the course is to use experiential learning to help Ph.D. students and researchers maximize their publication potential. In this course, we will discuss the major factors involved in developing high-quality research and understand how to increase the chances that the research will be published in top-tier journals (focus on ABS4/FT journals).
Summer Course: Theory testing with structural equation modelling
The main goal of this course is to provide professionals, researchers and Master or Doctorate students with the modelling and data analysis tools necessary to test theories in social sciences.
Summer Course: MLOps
This course is a practical approach to modern software development (focused on agility, customer satisfaction, and repeatability) and machine learning topics, trying to shine a software engineering focus on the solution of problems using machine learning, in such a way that the final solution can be easily deployed in current cloud environments. It focuses not on specific tools, but on giving the student elements of the state of the art so that they can take their own decisions depending on the specific problem. The course will be entirely practical, and by the end of the course students will be able to create reproducible data science pipelines as well as write reports that will pass the publication filter.