Semester:
Spring
Offered:
2019
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and deep neural networks, statistical modeling, and prediction. Topics include big data and database management, multiple deep learning subjects such as CNNs, RNNs, autoencoders, and generative models as well as basic Bayesian methods, nonlinear statistical models and unsupervised learning.