Graduate Certificate in Digital Curation

Data Warehousing and Analytics In the Cloud will utilize concepts, frameworks, and best practices for designing a cloud-based data warehousing solution and explore how to use analytical tools to perform analysis on your data. In the first half of the course, I will provide an overview of the field of Cloud Computing, its main concepts, and students will get hands-on experience through projects which utilize cloud computing platforms. In the second half of the course, we will examine the construction of a cloud-based data warehouse system and explore how the Cloud opens up data analytics to huge volumes of data.

Most of the web data today consists of unstructured text. Of course, the fact that this data exists is irrelevant, unless it is made available such that users can quickly find information that is relevant for their needs. This course will cover the fundamental knowledge necessary to build such systems, such as web crawling, index construction and compression, boolean, vector-based, and probabilistic retrieval models, text classification and clustering, link analysis algorithms such as PageRank, and computational advertising. The students will also complete one programming project, in which they will construct one complex application that combines multiple algorithms into a system that solves real-world problems.  Graduate level requirements include implementing more complex, state-of-the-art algorithms for the programming project, which might require additional reading of research articles. Written assignments will have additional questions for graduate students.

This course covers theory, methods, and techniques widely used to design and develop a relational database system and students will develop a broad understanding of modern database management systems. Applications of fundamental database principles in a stand-alone database environment using MS Access and Windows are emphasized. Applications in an Internet environment will be discussed using MySQL in the Linux platform. Graduate-level requirements include a group project consisting of seven sections: Database Design; Implementation (Tables); Forms; Data Retrieval (Queries/Reports); Project Presentation; Project Report; and, Peer Evaluation.

This course provides an overview of modern database systems at the time. Both relational databases (SQL) and a few non-relational databases (NoSQL) are covered, including topics on data warehouses. The focus of the course is on the practical skills of the design and implementation of data storage and access for data and information sciences. Topics covered include ER-diagrams, database normalization, data modeling in NoSQL databases, SQL and other query languages, and data warehousing. The course will selectively cover one or two types of NoSQL databases, for example, document-oriented, key-value pair, column-oriented, or graph databases. Database platforms used in this course could change with time, some examples include MySQL, PostgreSQL, Apache HBASE, Apache Cassandra, MongoDB, and Neo4J.

Organizing information in electronic formats requires standard machine-readable languages. This course covers recent standards including XML (eXtensible Markup Language) and related technologies (XPath and XSLT) which are used widely in current information organization systems. Building on a sounding understanding of XML technologies, the course also introduces students to newer standards that support the development of the Semantic Web. These standards include RDF (Resource Description Framework), RDFS (RDF Schema), and OWL (Web Ontology Language) and their application under the Linked Data paradigm. While the application of many specific XML schemas used in libraries and other information setting such as science and business will be used to provide the context for various topics, the main focus of the course is on understanding the concepts of XML and Semantic Web technologies and on applying practical skills in various settings, including but not limiting to libraries. The course is heavy with hands-on assignments and requires students complete a final group project.

This three-credit course is one of six required for completion of the Certificate in Digital Information Management (DigIn). This course will provide an in-depth look at the processes involved in building and managing digital collections and institutional repositories. The course will have a strong hands-on component in which students will apply advanced resource description methods to a collection, and then build a prototype repository along with a basic access system. Students will also analyze and discuss case examples of digital collections, focusing on technology management issues and organizational strategies for building different types of collections.

Provides an introduction to the preservation of library materials, including an overview of physical and chemical deterioration in various forms of media, and exploration of the body of knowledge related to ameliorating these problems.

Information Resource Development. Principles of identifying, selecting, acquiring, managing, and evaluating information resources for libraries, information centers, and other information-based settings.

The owners of intellectual property have moral and legal rights that constrain how information can be disseminated. Those working in libraries, archives, or other information services should understand when and why these constraints apply to advocate for a robust and equitable cultural and intellectual commons. Digital content creators should also be aware of the licenses, networks, and creative practices at play in the circulation and reuse of audio, video, photographic and multimodal works. In this course, we will discuss the various legal aspects of copyright  (e.g., fair use, the first-sale doctrine) in the context of information services and content creation as well as the ways intellectual property rights affect information services and shape media reuse in the 21st century.