Big Data Engineering (2191)

Course Aim: Provide an opportunity to deep dive into Big Data application development. Students will learn about big data and data-intensive application management, design and processing concepts.
Main Topics: Data modeling on different NoSQL databases: key/value - column-family - document - graph-based stores - stream and real-time processing - big data architectures - distributed computing using Spark and/or Hadoop - Big Data ecosystems (Sqoop, Kafka, Hive,....etc) - big data examples in real world.

Data Science I: Fundamentals (1792)

Course Aim: Develop all aspects of the data science pipeline: data acquisition and cleaning, handling missing data, data storage, exploratory data analysis, visualization, feature engineering, modeling, interpretation, presentation in the context of real-world datasets. Fundamental considerations for data analysis are emphasized. The course also uses the Python data science ecosystem.
Main Topics: Data collection and management via data pipeline - summarizing and visualizing data - utilizing basic ideas of statistical inference - core data mining models and machine learning models with their required statistics concepts - design strategies of data science solutions while considering business problems - practice of data science pipeline via hands-on labs with data mining, statistics and different analytics software.

Architecture and Implementation of Database Management Systems (1789)

Course Aim: Emphasize the understanding of the fundamentals of relational systems including data models, database architectures and database manipulations. The course also provides an understanding of new developments and trends using a problem-based approach.
Main Topics: Goals and functions of DBMS - catalogs - physical storage techniques - query processing/optimization - transaction management - mechanisms for concurrency control - recovery - distribution - security - integrity - extended data types - triggers - and rules - architectural foundations including: Performance - availability - and reliability characteristics of hardware and operating systems that impact the design of a DBMS- memory management for multi-user systems - logging and crash recovery .

Introduction to Database Systems (1211)

Course Aim: Introduce the fundamental concepts of database systems, acquaint the students with the use of current relational database systems, and build a solid foundation for advanced studies in the database area.
Main Topics: Conceptual/logical data models (ER - relational - and others) - query languages (relational algebra - SQL - and others) - database design (Normalization and Functional Dependencies) - foundations of data manipulation/analysis.