USC Schedule of Classes

Foundational course focusing on the understanding, application, and evaluation of machine learning and data mining approaches in data intensive scenarios.

Programming in Python for retrieving, searching, and analyzing data from the Web. Programming in Java. Learning to manipulate large data sets.

Analysis of computer security and why systems are not secure. Concepts and techniques applicable to the design of hardware and software for Trusted Systems.

The administrator's role in information system testing, certification, accreditation, operation and defense from cyber attacks. Security assessment. Examination of system vulnerabilities. Policy development. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, informatics, and/or information security undergraduate program. Also, it is highly recommended that students have successfully completed course work involving policy and network security.

Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; File System Analysis; Investigative Techniques and Methodologies; Forensic Reports and Presentations.

Covers societal implications of information privacy and how to design systems to best preserve privacy. Recommended preparation: General familiarity with the use of common internet and mobile applications.

Introduction to data analysis techniques and associated computing concepts for non-programmers. Topics include foundations for data analysis, visualization, parallel processing, metadata, provenance, and data stewardship. Recommended preparation: Mathematics and logic undergraduate courses.

Fundamentals of big data informatics techniques. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools for storage/processing/analytics of large data set on clusters; in-data techniques. Recommended preparation: A basic understanding of engineering principles and programming language is desirable.

Function and design of modern storage systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm.

Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.

Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies.

Understand and apply user interface theory and techniques to design, build and test responsive applications that run on mobile devices and/or desktops.

The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services.

Foundations, techniques, and algorithms for building knowledge graphs and doing so at scale. Topics include information extraction, data alignment, entity linking, and the Semantic Web.

Function, design, and use of modern data management systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm.

Student teams working on external customer data analytic challenges; project/presentation based; real client data, and implementable solutions for delivery to actual stakeholders; capstone to degree.

Picture archive communication system (PACS) design and implementation; clinical PACS-based imaging informatics; telemedicine/teleradiology; image content indexing, image data mining; grid computing in large-scale imaging informatics; image-assisted diagnosis, surgery and therapy.

Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.