Course

Goals

This course is the first course in big data for the ABC courses (AI model/algorithm, Big data, Computing) of the Graduate School of Data Science. The course covers the foundation of data management for data science and related fields. It covers the following topics:

– Theoretical background of data management, including data type, first-order logic, second-order logic, relational calculus and algebra, schema, and normalization.
– Relational database, including the ER model, transaction, concurrency control, logging, recovery, SQL, OLTP, query optimization.
– Distributed and federated database systems.
– Data analytics, including OLAP, column store, ETL, operational data stores, data warehouse, data lake, and in-memory databases.
– Physical design of databases with hands-on excises to implement database system functions, such as B-tree, using Postgres or MySQL.
– Data wrangling with hands-on exercises with NumPy, Pandas, and Python.