Covers model database resolution, hydrology, GIS, connections, transitions, and validation using various datasets.
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.
Introduces the relational model, SQL, keys, integrity constraints, ER translation, weak entities, ISA hierarchies, and SQL vs. noSQL.
Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Introduces the Entity-Relationship model for designing databases with a focus on conceptual and relational aspects.
Covers simulation, modeling, acceleration profiles, natural frequencies, rigidity calculations, and anti-resonance solutions for multi-axes robots.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
