Data Science
16 courses · UC Irvine · 2026-2027 Catalog
Classes: Fall 2026
DATA 200AP Intermediate Probability and Statistical Theory I
- Units
- 4
- Prerequisite
- Knowledge of basic statistics and linear algebra; Calculus I-III
Fundamental probability and distribution theory needed for statistical inference. Topics include axiomatic foundations of probability theory, discrete and continuous distributions, expectation and moment generating functions, multivariate distributions, transformations, sampling distributions, and limit theorems.
DATA 200BP Intermediate Probability and Statistical Theory II
- Units
- 4
- Prerequisite
- DATA 200AP with a minimum grade of B
Fundamental theory and methods for statistical inference. Topics include data reduction (sufficient, ancillary, and complete statistics), estimation (method of moments, maximum likelihood estimators, Bayes estimators), evaluating methods (mean squared error, best unbiased estimators, asymptotic evaluations), hypothesis testing, and confidence intervals.
DATA 200P Data Science Career Seminar
- Units
- 0
Covers one or more emerging topics in data science from industry professionals. The course content may vary.
DATA 210P Statistical Methods I
- Units
- 4
- Prerequisite
- DATA 200AP with a minimum grade of B-. Required: Knowledge of basic statistics and linear algebra
- Corequisite
- DATA 200BP
Statistical methods for analyzing data from multi-variable observational studies and experiments. Topics include model selection and model diagnostics for simple and multiple linear regression and generalized linear models.
DATA 211P Statistical Methods II
- Units
- 4
- Prerequisite
- DATA 210P with a minimum grade of B-
Statistical methods for designing experiments, visualizing, and analyzing experimental and observational data using generalized regression models, multivariate analysis, and methods suitable for dependent data.
DATA 220P Databases and Data Management
- Units
- 4
Introduction to the design of databases and the use of database management systems (DBMS) for managing and utilizing data. Topics include entity-relationship modeling for design, relational data model, relational algebra, relational schema design, and use of SQL (Structured Query Language).
DATA 260P Fundamentals of Algorithms in Data Science
- Units
- 4
Covers fundamental concepts in the design and analysis of algorithms and is geared toward data science applications. Topics include greedy algorithms, deterministic and randomized graph algorithms, models of network flow, fundamental algorithmic techniques, and NP-completeness.
DATA 273P Machine Learning and Data Mining
- Units
- 4
- Prerequisite
- COMPSCI 271P with a minimum grade of B-. Required: Python Programming knowledge
Utilizing techniques from statistics, optimization, and computer science, machine learning and data mining algorithms empower automated systems to process and analyze large data sets swiftly, enabling them to make predictions or decisions without any need for human intervention.
DATA 275P Machine Learning with Generative Models
- Units
- 4
- Prerequisite
- COMPSCI 273P with a minimum grade of B and DATA 200BP with a minimum grade of B. Recommended: Python programming and advanced statistics experience
Introduction to statistical machine learning with probabilistic generative models. Studies efficient inference algorithms based on optimization-based variational methods, and simulation-based Monte Carlo methods. Several approaches to learning from data are covered, including conditional models for discriminative learning.
DATA 280P Data Science Career Seminar
- Units
- 0
Covers one or more emerging topics in data science from industry professionals. Provides students with the opportunity to learn from experts in the data science industry through guest speaker presentations and Q&A sessions.
DATA 294P Hypothesis and Project Development
- Units
- 4
Supervised individual study on data science project development. Under instructor’s supervision, students either develop their own projects or undertake projects from industry which have potential to become their capstone projects for DATA 296P or DATA 297P.
DATA 295P Special Topics in Data Science
- Units
- 4
Covers one or more emerging topics in data science. Course content may vary.
DATA 296P Capstone Writing and Communication
- Units
- 4
- Prerequisite
- Required: Completion of at least 24 units in the Master of Data Science program
Written and oral communication for data science careers. Production of a detailed document describing the design, methods, analytic strategy, interpretation, and conclusions as related to the concurrent capstone design and analysis class and refinement of written documents and oral communications.
DATA 297P Capstone Design and Analysis
- Units
- 4
- Corequisite
- DATA 296P
Complete implementation of a data science analytic strategy for obtaining empirically-driven solutions to problems from science and industry. Focuses on the problem definition and analysis, data representation, algorithm selection, solution validation, and presentation of results.
DATA 298P Curricular Practical Training
- Units
- 2
Internship in which students work individually at an outside organization to gain experience with the challenges involved in data-related work.
DATA 299P Individual Study
- Units
- 2
Supervised individual study in data science.