Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
