Viterbi School of Engineering
Informatics Program
Assistant Director: Winnie Callahan, Ed.D.
Faculty
Professors: Barry Boehm, Ph.D. (Computer Science, Industrial and Systems Engineering); Ramesh Govindan, Ph.D. (Computer Science, Electrical Engineering); Julia Higle, Ph.D. (Industrial and Systems Engineering); Carl Kesselman, Ph.D. (Industrial and Systems Engineering, Computer Science); Neno Medvidovic, Ph.D. (Computer Science); Shri Narayanan, Ph.D. (Electrical Engineering, Computer Science, Linguistics, Psychology); Viktor Prasanna, Ph.D. (Electrical Engineering); Suvrajeet Sen, Ph.D. (Industrial and Systems Engineering, Electrical Engineering, Computer Science); Cyrus Shahabi, Ph.D. (Computer Science); Gaurav Sukhatme, Ph.D. (Computer Science, Electrical Engineering); Milind Tambe, Ph.D. (Computer Science, Industrial and Systems Engineering); Priya Vashishta, Ph.D. (Physics, Computer Science, Biomedical Engineering)
Assistant Professor: Yan Liu, Ph.D. (Computer Science)
Research Professors: Herbert Schorr, Ph.D. (Computer Science); William Swartout, Ph.D. (Computer Science)
Research Associate Professor: Clifford Neuman, Ph.D. (Computer Science)
Professor of the Practice: Roger Schell, Ph.D.
Senior Lecturer: Blaine Burnham, Ph.D.
Lecturer: Lyndon Pierson
Master of Cyber Security
Program Director: Blaine Burnham, Ph.D.
The Master of Cyber Security (MCBS) is intended for graduate students who desire to: obtain jobs in which knowledge and skills for the creation and analysis of trustworthy systems and networks are required and continue an education path toward a doctorate degree with focus on information security. It is also for individuals who are in degree programs or job fields that have some responsibility with information security and who desire enhanced knowledge and skills.
Upon completion of this program, students will have learned the fundamental theory and practices for designing, engineering and operating high assurance secure information systems. They will be well versed in the challenges and problems of secure operating systems, secure aware applications, secure networking, use of cryptography and key management. They will understand how to develop and formally model a security policy, and how sound policy taxonomy drives technology decisions. Students will gain the knowledge and concepts necessary to administer environments that require high levels of information security. Students will understand the value of assets, the business model of threat, the distinct threat categories from user abuse to malicious subversion and mitigation strategy. They will understand that a foundation of sound principles critically influences why some information security plans succeed and why others fail. Students will have hands-on experience in situations that simulate real-world scenarios with all technical and theoretical situations through extensive laboratory work, which will be designed by current and former information security practitioners.
Requirements for completion (27 units minimum)
| Required courses (18 units) | Units | |
|---|---|---|
| INF 520 | Foundations of Information Security | 3 |
| INF 521 | Applications of Cryptography to Information Security Problems | 3 |
| INF 522 | Policy: The Foundation for Successful Information Assurance | 3 |
| INF 523 | Assurance in Cyberspace Applied to Information Security | 3 |
| INF 524 | Distributed Systems and Network Security | 3 |
| INF 527 | Secure Systems Engineering | 3 |
| Elective courses (choose three, 9-10 units) | units | |
|---|---|---|
| CSCI 530 | Security Systems | 4 |
| CSCI 531 | Applied Cryptography | 3 |
| INF 525 | Trusted System Design, Analysis and Development | 3 |
| INF 526 | Secure Systems Administration | 3 |
| INF 528 | Computer and Network Forensics | 3 |
Master of Science in Data Informatics
Program Director: Herb Schorr, Ph.D.
The social emergence of large data environments and infrastructures (Big Data) in diverse domains and uses has spawned a requirement for analysis of the information contained. Past experience has shown that extracting value from large information stores can often be difficult due to the intrinsic nature of data, and the limits on ability to intelligently mine the information to add value to the organization.
The USC Viterbi Master of Science in Data Informatics provides students with the knowledge and skill to: a) understand and contribute toward the significant technical challenges created by large data environments, including architecture, security, integrity, management, scalability, artificial intelligence topics, and distribution; b) understand the principles and application of informatics, and the goals of enterprise intelligence; and c) utilize technical/engineering skills coupled with informatics capabilities to provide enterprise-centric solutions to stakeholders. The degree features application of knowledge and skill in hands-on type experiences, with the goal of having students leave the program having “lived in the data.”
Students will understand the overall field of data analytics, the role of the analyst and/or data scientist, and the domains where informatics skills can be applied to critical organization missions. They will understand how data management, data visualization, data mining, and artificial intelligence techniques (specifically machine learning) are critical to the analysis process, and how these can be applied to real world challenges. Through an extensive elective track, they can find the specializations that will help them better prepare themselves for the area(s) of analytics in which they hope to contribute. Finally, students will participate in a unique professional practicum that will focus on real world challenges, brought in by external customers.
Requirements for completion (27 units minimum)
| Required courses (18 units) | Units | |
|---|---|---|
| INF 550 | Overview of Data Informatics in Large Data Environments | 3 |
| INF 551 | Foundations of Data Management | 3 |
| INF 552 | Machine Learning for Data Informatics | 3 |
| INF 553 | Foundations and Applications of Data Mining | 3 |
| INF 555 | User Interface Design, Implementation, and Testing | 3 |
| INF 560 | Data Informatics Professional Practicum | 3 |
| Elective courses (choose three, 9-10 units) | units | |
|---|---|---|
| CSCI 686 | Advanced Big Data Analytics | 3 |
| CSCI 567 | Machine Learning | 3 |
| CSCI 548 | Information Integration on the Web | 3 |
| CSCI 561 | Foundations of Artificial Intelligence | 3 |
| CSCI 585 | Database Systems | 3 |
| CSCI 530 | Security Systems | 4 |
| CSCI 485 | File and Database Management | 3 |
| CSCI 572 | Information Retrieval and Web Search Engines | 3 |
| INF 520 | Foundations of Information Security | 3 |
| INF 522 | Policy: The Foundation of a Successful Information Assurance Program | 3 |
Courses of Instruction
INFORMATICS PROGRAM (INF)
The terms indicated are expected but are not guaranteed. For the courses offered during any given term, consult the Schedule of Classes.
INF 520 Foundations of Information Security (3) Threats to information systems; technical and procedural approaches to threat mitigation; secure system design and development; mechanisms for building secure security services; risk management. Recommended preparation: Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
INF 521 Application of Cryptography to Information Security Problems (3) Application of cryptography and cryptanalysis for information assurance in secure information systems. Classical and modern cryptography. Developing management solutions. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; understanding of number theory and programming background are helpful.
INF 522 Policy: The Foundation for Successful Information Assurance (3) Policy as the basis for all successful information system protection measures. Historical foundations of policy and transition to the digital age. Detecting policy errors, omissions and flaws. Recommended preparation: Background in computer security, or a strong willingness to learn. Recommended previous courses of study include degrees in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
INF 523 Assurance in Cyberspace Applied to Information Security (3) Assurance as the basis for believing an information system will behave as expected. Approaches to assurance for fielding secure information systems that are fit for purpose. Recommended preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics. Some background in computer security preferred.
INF 524 Distributed Systems and Network Security (3) Fundamentals of information security in the context of distributed systems and networks. Threat examination and application of security measures, including firewalls and intrusion detection systems. Recommended preparation: Prior degree in computer science, mathematics, computer engineering, or informatics. It is recommended that students have a working understanding of communication networks and computer architecture, and some programming facility.
INF 525 Trusted System Design, Analysis and Development (3) 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: Prior degree in computer science, mathematics, computer engineering, or informatics; advanced knowledge of computer architecture, operating systems, and communications networks will be valuable.
INF 526 Secure Systems Administration (3) 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.
INF 527 Secure Systems Engineering (3) 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 informatics; moderate to intermediate understanding of the fundamentals of information assurance, and distributed systems and network security. Knowledge and skill in programming.
INF 528 Computer and Network Forensics (3) Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; cryptography; types of attacks; steganography; network forensics and surveillance. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; a working understanding of number theory and some programming knowledge will be helpful.
INF 550 Overview of Data Informatics in Large Data Environments (3, FaSp) 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: Basic understanding of engineering and/or technology principles; basic programming skills; background in probability, statistics, linear algebra and machine learning.
INF 551 Foundations of Data Management (3, FaSp) 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. Recommended preparation: INF 550 taken previously or concurrently; understanding of operating systems, networks, and databases; experience with probability, statistics, and programming.
INF 552 Machine Learning for Data Informatics (3, FaSp) Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces. Recommended preparation: INF 550 and INF 551 taken previously or concurrently; knowledge of statistics and linear algebra; programming experience.
INF 553 Foundations and Applications of Data Mining (3, FaSp) Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies. Recommended preparation: INF 550, INF 551 and INF 552. Knowledge of probability, linear algebra, basic programming, and machine learning.
INF 554 Information Visualization (3, FaSp) 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.
INF 555 User Interface Design, Implementation, and Testing (3, FaSp) 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, data mining, and data visualization.
INF 556 User Experience Design and Strategy (3, FaSp) The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services. Open only to Data Informatics majors. Recommended preparation: Basic familiarity with web development and/or graphic design using a digital layout tool.
INF 560 Data Informatics Professional Practicum (3, FaSp) 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.
INF 590 Directed Research (1-12, FaSpSm) 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.