The requirements for the major can be found at YCPS Statistics and Data Science.
Please use this S&DS checklist to organize your course selections.
You can receive updates on the major by subscribing to the S&DS undergraduate student mailing list.
Prerequisite: One of
 MATH 120 (Calculus of Functions of Several Variables)
 ENAS 151 (Multivariable Calculus for Engineers)
 MATH 230 (Vector Calculus and Linear Algebra I)
 MATH 302 (Vector Calculus and Integration on Manifolds)
or equivalent, or DUS waived.
Senior Requirement: One of
 S&DS 491 (Senior Project, fall semester)
 S&DS 492 (Senior Project, spring semester)
Discipline Areas
A. Core Probability and Statistics: These are essential courses in probability and statistics. Every major should take at least two of these courses, and should probably take more. Students completing the BS must take S&DS 242.
 S&DS 238 (Probability and Statistics)
 S&DS 241 (Probability Theory)
 S&DS 242 (Theory of Statistics)
 S&DS 312 (Linear Models)
 S&DS 351 (Stochastic Processes)
B. Computational Skills: Every student in Data Science should be able to compute with data. While the main purpose of some of these courses is not computing, students who have taken at least two of these courses should be capable of digesting and processing data. While there are other courses that require a lot of programming, these ones are essential. Every major must take at least two of these courses.
 One of the following courses:
 S&DS 220 (Intro Statistics, Intensive)
 S&DS 230 (Data Exploration and Analysis)
 One of the following courses:
 CPSC 100 (Introduction to Computing and Programming)

CPSC 112 (Introduction to Programming)

CPSC 200 (Introduction to Information Systems)

CPSC 201 (Introduction to Computer Science)

ENAS 130 (Introduction to Computing for Engineers and Scientists)
 S&DS 425 (Statistical Case Studies)
 S&DS 262 (Computational Tools for Data Science)
 S&DS 265 (Introductory Machine Learning)
C. Methods of Data Science: These courses teach fundamental methods for dealing with data. They range from the practical to the theoretical. Every major must take at least two of these courses.
 S&DS 312 (Linear Models)
 S&DS 314 (Introduction to Causal Inference)
 S&DS 317 (Applied Machine Learning and Causal Inference)
 S&DS 361 (Data Analysis)
 S&DS 363 (Multivariate Statistics for Social Sciences)
 S&DS 365 (Intermediate Machine Learning)
 CPSC 446 (Data and Information Visualization)
 CPSC 477 (Natural Language Processing)
 CPSC 452 (Deep Learning Theory and Applications)
 S&DS 430 (Optimization Techniques)
 S&DS 431 (Optimization and Computation)
 S&DS 468 (Nonparametric Learning)
 EENG 400 (Dynamic and Discrete Optimization)
D. Mathematical Foundations and Theory: All students in the major must know linear algebra. If they have learned linear algebra through other courses (such as MATH 230/231), they may substitute another course from this category. Students pursuing the B.S. must take at least two courses from this list. Students who wish to pursue graduate school should take many.
 One of
 CPSC 365 (Algorithms)
 CPSC 366 (Intensive Algorithms)
 CPSC 469 (Randomized Algorithms)
 MATH 222 (Linear Algebra with Applications)
 MATH 225 (Linear Algebra and Matrix Theory)
 MATH 226 (Linear Algebra Intensive)
 MATH 240 (Advanced Linear Algebra)
 MATH 244 (Discrete Mathematics)
 MATH 255 (Analysis 1)
 MATH 256 (Analysis 1 Intensive)
 MATH 302 (Vector Analysis and Integration)
 MATH 305 (Analysis 2: Lesbegue Integration and Fourier Series)
 MATH 320 (Measure Theory and Integration)
 MATH 325 (Introduction to Functional Analysis)
 S&DS 364 (Information Theory)
 S&DS 400 (Advanced Probability)
 S&DS 410 (Statistical Inference)
 S&DS 411 (Selected Topics in Statistical Decision Theory)
 S&DS 669 (Statistical Learning Theory)
E. Efficient Computation and Big Data: These courses are for students who want to do serious programming or implement largescale analyses. None are required for the major. Students who wish to work in the software industry should take at least one of these.
 CPSC 223 (Data Structures and Programming Techniques)
 CPSC 323 (Introduction to Systems Programming and Computer Organization)
 CPSC 437 (Introduction to Databases)
 CPSC 424 (Parallel Programming Techniques)
F. Data Science in Context: Students are encouraged to take courses that involve the study of data in application areas. These courses will teach students how these data are obtained, how reliable they are, how they are used, and the types of inferences that can be made from them. These course selections should be approved by the DUS. Examples of such courses include
 PSYC 235 (Research Methods in Psychology)
 PSYC 258 (Computational Methods in Human Neuroscience)
 PLSC 454 (Data Science for Politics and Policy)
 ANTH 376 (Observing and Measuring Behavior)
 EVST 362 (Observing Earth from Space)
 GLBL 191 (Research Design and Survey Analysis)
 GLBL 195/PLSC 341 (Logic of Randomized Experiments in Political Science)
 LING 229 (Language and Computation II)
 LING 234 (Quantitative Linguistics using Corpora)
 LING 380 (Neural Networks and Language)
 S&DS 315 (Measuring Impact and Opinion Change)
G. Methods in Application Areas: These are methods courses in areas of applications. They help expose students to the cultures of fields that explore data. These course selections should be approved by the DUS. Examples of such courses include:
 S&DS 352/MB&B 452 (Biological Data Science, Mining and Modeling)
 EENG 445 (Biomedical Image Processing and Analysis)
 ENAS 962 (Theoretical Challenges in Network Science)
 ECON 136 (Econometrics)
 ECON 419 (Financial Time Series Econometrics)
 ECON 420 (Applied Microeconometrics)
 CPSC 453 (Unsupervised Learning for Big Data)
 CPSC 470 (Artificial Intelligence)
 CPSC 475 (Computational Vision & Biological Perception)
 LING 227/PSCY 327 (Language and Computation I)