COURSES
Units: 4
Terms Offered: Fall
Fundamentals of data science: representation of data and knowledge, role of a data scientist, data storage/processing/analysis, machine learning, big data and data visualization. Recommended preparation: A basic understanding of engineering and/or technology is recommended.
Corequisite: ITP 115 or ITP 116
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
An introduction to the concepts of artificial intelligence (AI) and machine learning (ML); AI and ML applications; ethical considerations; intended for students without a programming or computer science background. Recommended Preparation: A basic knowledge of mathematics or statistics, cognitive psychology, economics, business, linguistics, communication and philosophy.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Introduction to the concepts behind and use of artificial intelligence for natural language processing in interactive artificial intelligence systems; intended for students without a programming or computer science background. Recommended Preparation: A basic knowledge of mathematics and statistics, cognitive psychology, economics, business, linguistics, communication and philosophy.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Foundational concepts of artificial intelligence (AI) for robotics, cyberphysical systems and automation; intended for students without a programming or computer science background. Recommended Preparation: Basic knowledge of mathematics or statistics, mechanical engineering, electrical engineering, cognitive psychology, economics, business, linguistics, communication and philosophy
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Data modeling, data storage, indexing, relational databases, key-value/document store, NoSQL, distributed file system, parallel computation and big-data analytics. Recommended Preparation: Programming experience (e.g., Python or Java)
Prerequisite: DSCI 250 & ITP 115
Available on-campus only.
Units: 4
Terms Offered: Fall
Foundational course focusing on the understanding, application and evaluation of machine learning and data mining approaches in data-intensive scenarios.
Prerequisite: DSCI 250 & MATH 208x
Available on-campus only.
Units: 4
Terms Offered: Spring
Basic concepts in information security and privacy; implications of security and privacy breaches; security and privacy policies, threats and protection mechanisms; security and privacy laws, regulations and ethics.
Available on-campus only.
Units: 4
Terms Offered: Fall
Design of systems for data visualization; user interface design for exploring and interacting with data.
Prerequisite: DSCI 250
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Programming in Python for retrieving, searching and analyzing data from the Web. Learning to manipulate large data sets.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall & Spring
Introduction to research methods and data analysis techniques for human subject research; experimental research design, correlational research, data analysis, ensuring validity and ethics.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall
Threats to information systems; technical and procedural approaches to threat mitigation; policy specification and foundations of policy for secure systems; mechanisms for building secure security services; risk management. Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems and/or mathematics.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall
Assurance that an information system will behave as expected; assurance approaches for fielding secure information systems; case studies. Recommended preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems and/or mathematics. Some background in computer security preferred.
Prerequisite: DSCI 519
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Spring
Analysis of computer security and why systems are not secure. Concepts and techniques applicable to the design of hardware and software for Trusted Systems.
Recommended Preparation: Background in computer security, computer architecture, operating systems, software development is preferred. Recommended previous course of study is DSCI 519.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall
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, data science and/or information security undergraduate program. It is also highly recommended that students have successfully completed coursework involving policy and network security.
Prerequisite: CSCI 530
Available on-campus only.
Units: 3
Terms Offered: Fall
The process of designing, developing and fielding secure information systems. Developing assurance evidence. Completion of a penetration analysis. Detecting architectural weaknesses. Case studies. Recommended preparation: Previous degree in computer science, mathematics, computer engineering or data science; moderate to intermediate understanding of the fundamentals of information assurance and distributed systems and network security. Knowledge and skill in programming.
Prerequisite: DSCI 525
Available on-campus only.
Units: 4
Terms Offered: Fall
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.
Available on-campus only.
Units: 4
Terms Offered: Spring
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.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall & Spring
Basic and advanced fairness concepts and methods; applications to societal data for studying fairness and bias; fairness and bias effects in learning algorithms.
Recommended Preparation: Knowledge of Python, linear algebra, probability, and statistics. Familiarity with artificial intelligence and machine learning.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Privacy concerns in healthcare; current law and regulations; existing and emerging technologies shaped by ethics, privacy considerations and medical implications; special attention given to genomic data. Recommended Preparation: Prior experience with information security, public policy, and legal frameworks is not required for this course. Basic understanding of engineering and/or technology principles.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
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.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
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.
Prerequisite: DSCI 517 or DSCI 564
Recommended Preparation: A basic understanding of computing principles at the level of DSCI 549 and programming at the level of DSCI 510.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall & Spring
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.
Prerequisite: DSCI 517 or DSCI 564
Recommended Preparation: A solid background of computing principles at the level of DSCI 550 and programming at the level of DSCI 510.
Available on-campus and online through DEN@Viterbi.
Units: 4
Terms Offered: Fall, Spring & Summer
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
Available on-campus only.
Units: 4
Terms Offered: Fall, Spring & Summer
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on System Building with Spark. Case studies.
Prerequisite: (DSCI 551 or CSCI 585) and (DSCI 552 or CSCI 567)
Recommended Preparation: Probability on the level of EE 364, linear algebra on the level of EE 141, and essential programming on the level of DSCI 510
Available on-campus only.
Units: 4
Terms Offered: Fall
Graphical depictions of data for communication, analysis and decision support. Cognitive processing and perception of visual data and visualizations. Designing effective visualizations. Implementing interactive visualizations.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Understand and apply user interface theory and techniques to design, build and test responsive applications that run on mobile devices and/or desktops. Recommended preparation: Knowledge of data management, machine learning, datamining and data visualization.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services. Recommended preparation: Basic familiarity with web development and/or graphic design using a digital layout tool.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
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. Recommended preparation: DSCI 553 and experience programming in Python.
Prerequisite: DSCI 551 or CSCI 585, and DSCI 552 or CSCI 567
Available on-campus only.
Units: 3
Terms Offered: Spring
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. Recommended preparation: Understanding of engineering principles, basic programming skills, familiarity with Python.
Available on-campus only. Crosslisted as ISE 559.
Units: 4
Terms Offered: Spring
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. Recommended preparation: Knowledge of data management, machine learning, data mining and data visualization.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
Analytics for supervised and unsupervised statistical learning. Generalized linear models, discriminant analysis, support vector machines. Nonparametric classification, trees, ensemble methods, k-nearest neighbors. Principal components, clustering.
Available on-campus only. Crosslisted as ISE 529.
Units: 4
Terms Offered: Fall
Introduce basic concepts of Medical Imaging Informatics with an introduction to clinical information systems (eg, PACS, RIS, EMR) related to the imaging workflow in a clinical healthcare enterprise.
Available on-campus only. To enroll, register in BME 527.
Units: 4
Terms Offered: Spring
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.
Available on-campus only. To enroll, register in BME 528.
Units: 4
Terms Offered: Spring
Fundamental concepts in probability and statistics from a data science perspective; rigorous probabilistic reasoning and problem-solving; statistical methods used in data science. Recommended preparation: Multivariate calculus, linear algebra, linear system theory.
Available on-campus only.
Units: 4
Terms Offered: Fall & Spring
An introduction to core deep learning algorithms combined with practical experience in building and applying deep learning networks.
Available on-campus only.
Units: 3
Terms Offered: Spring
Modeling behavior and understanding network structures using graph theory and game theory. Using massive data to analyze group behavior.
Available on-campus only.
Units: 1 - 12
Terms Offered: Fall, Spring & Summer
Research leading to the master’s degree; maximum units which may be applied to the degree to be determined by the department.
Available on-campus only.
Units: 1 - 8
Terms Offered: Fall & Spring
Course content to be selected each semester from recent developments in Data Science.
Available on-campus only.
For descriptions of TAC courses, visit the USC Viterbi Technology and Applied Computing Program website.
For descriptions of CSCI courses, visit the USC Viterbi Department of Computer Science website.
