Computer Vision Curriculum Design for the Data Science Degree

The Universidad de la República in Uruguay was launching a new Data Science degree and needed a rigorous, industry-aligned Computer Vision course. The faculty required expert support to design a curriculum that balanced academic depth with practical, real-world applications — covering classical image processing through modern deep learning approaches — and to create comprehensive teaching materials that could be delivered by university instructors.

We designed the full Computer Vision syllabus for the Data Science degree, structured as a progressive journey from fundamentals to advanced topics. The curriculum covers image representation and preprocessing, feature extraction, convolutional neural networks (CNNs), object detection (YOLO, SSD), semantic and instance segmentation, generative models (GANs, diffusion), and vision transformers (ViT). All content was developed with hands-on labs using real-world datasets and industry-standard tools. We also supervised the syllabus implementation, reviewing teaching materials, advising on assessment strategies, and ensuring alignment with current industry practices.