Basic Python/Statistics: Fairly fast pace so having at least some prior knowledge will definetly help to give you an easier start. However some students in my class did not have prior knowledge but still managed to do it (with a little additional work). Numpy/Pandas: Very insightful and slower pace compared to the previous block. Machine Learning: Great blance between basic concepts, pitfalls and practical application. Neural Networks/Computer Vision: Insightful and a comprehensive overview of developments in the field (Grad-CAM, Bayesian NN, Transfer Learning, Augmentation etc.). However, I personally did not enjoy tensorflow2 all that much and switched to PyTorch after the class. Neural Networks/NLP: Good overview of a complex field. Final Project: Propulsion receives many real-world projects from different companies which is great for students looking to find a job. However, you might be assigned to a project that you're not super interested in (even though they obviously try to cater to your interests). If you happen to have any data to work on that really interests you, you can also work on one of your own projects during this time. I brought data that required a lot of domain knowledge in medicine, and while I therefore wasn't able to receive the helpful feedback from the instructors that other students got, working on a project during a short time frame, with a non-medical but very tech-savy classmate and then presenting it to a non-technical audience was a really important experience. Overall, I highly recommend it! Always 1 instructor + 2 teching assistants in the room and a lot of smart/driven students from different backgrounds. | 