Data Science II: Advanced

This course is a follow-up to INET 4061: Data Science Fundamentals. It covers the tools required to apply and implement data science techniques such as mathematical programming libraries, cloud resources, and big data databases. It also gives an overview of advanced data science methodologies such as deep learning, reinforcement learning, recommendation systems, and linear programming.

Sample course topics: Python and Spark, Common ML algorithms/workflow, operations, and platforms; cloud computing; big data database systems; neural networks; computer vision and natural language processing; recommender systems; reinforcement learning; optimization.

Prerequisites: Basic programming knowledge (Java, Python, R). Linear algebra and calculus strongly recommended (e.g., MATH 2243 and 2263). INET 4061 strongly recommended.