Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.
Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
