How to Read Course Descriptions
The bolded first line begins with a capitalized abbreviation that designates the subject area followed by the course number and title. The unit value is also displayed.
CR/NC: Indicates a course is offered on a Credit/No Credit grading basis only.
GE Area: Indicates the General Education (GE) Area for which the course may fulfill a requirement. See the course description for details.
USCP: Indicates that credit in the course satisfies the U.S. Cultural Pluralism requirement.
GWR: Indicates the course will satisfy the Graduation Writing Requirement, if the student earns a grade of C or better AND receives certification of proficiency in writing based on a 500-word in-class essay.
Prerequisite: Coursework to be completed and/or requirements to be met before taking the course
Corequisite: Course or courses that must be taken in a previous term or in the same term
Concurrent: Course or courses that must be taken in the same term
Recommended: Course with supporting content that is recommended, but is not required to be taken in a previous term or in the same term
The course description summarizes the purpose and key topical areas of the course, and includes special requirements if they exist. It indicates the mode of instruction, such as lecture and/or laboratory; if no mode is indicated, the course is supervised independent study. If a course can be taken more than once for credit, the description will indicate that either major credit or total credit is limited to a specified number of units. Some course descriptions end with information about whether the course was "formerly" another course or whether the course is cross-listed. A cross-listed course is the same course offered within multiple subject areas, MCRO/WVIT 301 Wine Microbiology for example.
DATA 301. Introduction to Data Science. 4 units
Introduction to the field of data science and the workflow of a data scientist. Types of data (tabular, textual, sparse, structured, temporal, geospatial), basic data management and manipulation, simple summaries, and visualization. 3 lectures, 1 laboratory.
DATA 401. Data Science Process and Ethics. 3 units
Complete life cycle of a data science project. Requirements engineering and data acquisition. Management and integration of data of high volume, velocity, and variety. Deployment of data science products. Engagement with stakeholders. Ethical considerations, including privacy and fairness. 3 lectures.
DATA 402. Mathematical Foundations of Data Science. 3 units
Mathematical foundations of machine learning and data science. Principle of maximum likelihood. Inferential and predictive modeling and their comparison. Optimization techniques. Linear regression and linear classifiers. Mathematical foundations of neural networks and neural network analysis. Dimensionality reduction and its use in supervised and unsupervised learning. 3 lectures.
DATA 403. Data Science Projects Laboratory. 1 unit
DATA 441. Bioinformatics Capstone I. 2 units
Working with clients to design bioinformatics solutions to biological questions. Software requirements, elicitation techniques, data gathering, project planning, and project team organization. Ethics and professionalism. 2 laboratories.
DATA 442. Bioinformatics Capstone II. 2 units
Prerequisite: DATA 441.
Continue projects initiated in DATA 441. Team-based design, implementation of bioinformatics solutions and management of development teams. Technical documentation, quality assurance, and systems testing. Design and conduct empirical studies. Data visualization. Oral and written presentation. 2 laboratories.
DATA 451. Data Science Capstone I. 2 units
Prerequisite: DATA 401.
Working with clients to develop data-driven solutions for systems to be constructed in DATA 452. Specification and design requirements, elicitation techniques, research and data gathering methods; project planning, time and budget estimating; project team organization. Ethics and professionalism. 2 laboratories.
DATA 452. Data Science Capstone II. 2 units
Prerequisite: DATA 451.
Team-based design, implementation, deployment and delivery of a system or analytical methodology that involves working with and analyzing large quantities of data. Technical management of research and development teams. Technical documentation, quality assurance, integration and systems testing. Design and conduct of empirical studies. Visualization and presentation of results orally and in writing. 2 laboratories.
DATA 472. Data Science Seminar. 1 unit
Prerequisite: DATA 301.
Discussions of technical, societal and ethical aspects of modern data science theory and practice, concentrating on topics not covered in other courses. 1 seminar. Total credit limited to 4 units. Credit/No credit grading only.