Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Explores Python modules, program hierarchy, module importing, and namespaces, including variables and scopes within functions.
Delves into advanced Spark optimization techniques, emphasizing data partitioning, shuffle operations, and memory management.
Explores coordination and scheduling in operating systems, covering lost wakeup problems, scheduling algorithms, and coordination primitives like sleep and wakeup.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
