Courses
Units: 4
Terms Offered: Fall & Spring
Programming in Python for retrieving, searching and analyzing data from the Web. Learning to manipulate large data sets.
Duplicates credit in former INF 510
Instruction Mode: Lecture, Lab
Grading Option: Letter
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.
Duplicates credit in former INF 519
Instruction Mode: Lecture
Grading Option: Letter
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
Duplicates credit in former INF 523
Instruction Mode: Lecture
Grading Option: Letter
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: Prior degree in computer science, mathematics, computer engineering, or informatics; advanced knowledge of computer architecture, operating systems, and communications networks will be valuable.
Prerequisite: DSCI 519
Duplicates credit in former INF 525
Instruction Mode: Lecture
Grading Option: Letter
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
Duplicates Credit in former INF 526
Instruction Mode: Lecture, Lab
Grading Option: Letter
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
Duplicates credit in former INF 527
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 528
Instruction Mode: Lecture
Grading Option: Letter
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. Recommended preparation: General familiarity with the use of common Internet and mobile applications.
Duplicates credit in former INF 529
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 549
Instruction Mode: Lecture
Grading Option: Letter
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. Recommended preparation: A basic understanding of engineering principles and programming language is desirable.
Duplicates credit in former INF 550
Instruction Mode: Lecture
Grading Option: Letter
Units: 4
Terms Offered: Fall & Summer
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: DSCI 550 taken previously or concurrently; understanding of networks and databases; experience with probability, statistics, and programming.
Duplicates credit in former INF 551, former INF 559, DSCI 559
Instruction Mode: Lecture
Grading Option: Letter
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. Recommended preparation: Knowledge of statistics and linear algebra; programming experience.
Prerequisite: DSCI 550 and DSCI 551 taken previously or concurrently
Duplicates credit in former INF 552
Instruction Mode: Lecture
Grading Option: Letter
Units: 4
Terms Offered: Fall, Spring & Summer
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies. Recommended preparation: DSCI 550, DSCI 551 and DSCI 552; knowledge of probability, linear algebra, basic programming and machine learning.
Duplicates credit in former INF 553
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 554
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 555
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 556
Instruction Mode: Lecture
Grading Option: Letter
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 INF 551 or CSCI 585, and DSCI 552 or INF 552 or CSCI 567
Duplicates credit in former INF 558
Instruction Mode: Lecture
Grading Option: Letter
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 datacenters; relational databases; the map-reduce paradigm. Recommended preparation: Understanding of engineering principles, basic programming skills, familiarity with Python
Duplicates credit in DSCI 551, former INF 559 and former INF 551
Instruction Mode: Lecture
Grading Option: Letter
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.
Duplicates credit in former INF 560
Instruction Mode: Lecture
Grading Option: Letter
Units: 3
Theory and methods of data analytics emphasizing engineering applications: multivariate statistics, supervised learning, classification, smoothing and kernel methods, support vector machines, discrimination analysis, unsupervised learning.
Units: 3
Medical imaging quality, compression, data standards, workflow analysis and protocols, broadband networks, image security, fault tolerance, picture archive communication system (PACS), image database and backup.
To enroll register in BME 527.
Units: 3
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.
To enroll register in BME 528.
Units: 4
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.
Duplicates credit in former INF 564
Instruction Mode: Lecture
Grading Option: Letter
Units: 3
Terms Offered: Spring
Modeling behavior and understanding network structures using graph theory and game theory. Using massive data to analyze group behavior. Recommended preparation: Minimum one year of calculus and background in matrix operations.
Duplicates credit in former INF 570
Instruction Mode: Lecture
Grading Option: Letter
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.
Instruction Mode: Lecture
Grading Option: Credit/No Credit
Units: 1 - 8
Terms Offered: Fall & Spring
Course content to be selected each semester from recent developments in Data Science.
Instruction Mode: Lecture
Grading Option: Letter