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This course presents the database design process practiced when creating a
relational database; it also presents the relational database management
system’s architecture as well as the fundamental ACID properties of a
relational database management system. Extended entity-relationship models
will be generated and represented using the Unified Modeling Language (UML)
notation. Relational algebra and its relationship to the SQL language will
be presented. Advanced topics include triggers, stored procedures, indexing,
and fundamentals of transactions, concurrency and recovery. The course will
also include an introduction to NoSQL databases and provide students the
opportunity to compare SQL to NoSQL. Students will define a database project
that includes the design and implementation of a database as well as an
application for interacting with the database.
[+]CS5200 ┆ Database Management Systems
This course presents the database design process practiced when creating a
relational database; it also presents the relational database management
system’s architecture as well as the fundamental ACID properties of a
relational database management system. Extended entity-relationship models
will be generated and represented using the Unified Modeling Language (UML)
notation. Relational algebra and its relationship to the SQL language will
be presented. Advanced topics include triggers, stored procedures, indexing,
and fundamentals of transactions, concurrency and recovery. The course will
also include an introduction to NoSQL databases and provide students the
opportunity to compare SQL to NoSQL. Students will define a database project
that includes the design and implementation of a database as well as an
application for interacting with the database.
Introduces the fundamental techniques for data mining and
machine learning. Discusses several basic learning algorithms,
such as regression, kNN, decision trees, support vector machines,
and neural networks. Applies techniques to common types of data.
Implements data mining strategies following CRISP-DM.
Evaluates accuracy and fit of machine learning algorithms using
common validation strategies, including k-fold cross-validation.
Coding is done in R. Presumes knowledge of data collection and
shaping, plus statistics.
[+]DA5030 ┆ Introduction to Machine Learning & Data Mining
Introduces the fundamental techniques for data mining and
machine learning. Discusses several basic learning algorithms,
such as regression, kNN, decision trees, support vector machines,
and neural networks. Applies techniques to common types of data.
Implements data mining strategies following CRISP-DM.
Evaluates accuracy and fit of machine learning algorithms using
common validation strategies, including k-fold cross-validation.
Coding is done in R. Presumes knowledge of data collection and
shaping, plus statistics.
Introduces information science. Examines how information is used to solve
problems both for individuals and organizations and how information systems
interface with their users. Considers the technical, economic, social, and
ethical issues that arise when working with information. Discusses how to
collect, manage, classify, store, encode, transmit, retrieve, and evaluate
data and information with appropriate security and privacy. Storage models
include lists, tables, and trees (hierarchies). Examines applications of
information: visualization, presentation, categorization, decision making,
and predictive modeling. Introduces key concepts in probability. Explains
Bayesian analysis for information classification and modeling.
[+]IS2000 ┆ Principles of Information Science
Introduces information science. Examines how information is used to solve
problems both for individuals and organizations and how information systems
interface with their users. Considers the technical, economic, social, and
ethical issues that arise when working with information. Discusses how to
collect, manage, classify, store, encode, transmit, retrieve, and evaluate
data and information with appropriate security and privacy. Storage models
include lists, tables, and trees (hierarchies). Examines applications of
information: visualization, presentation, categorization, decision making,
and predictive modeling. Introduces key concepts in probability. Explains
Bayesian analysis for information classification and modeling.
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[+]TC0000 ┆ Template Course
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Aliquam eleifend mi in nulla. Facilisis volutpat est velit egestas.
Suscipit adipiscing bibendum est ultricies. Venenatis tellus in
metus vulputate eu scelerisque. Non odio euismod lacinia at quis
risus. Pretium lectus quam id leo. Commodo quis imperdiet massa
tincidunt. Netus et malesuada fames ac turpis egestas. Elementum
tempus egestas sed sed risus.