Description
The ACMS Data Sciences and Statistics option is designed with strong Statistics and Modeling components. The track incorporates coursework in Computation, Statistics and Machine Learning, Databases and Data Visualization, as well as topics related to science and society. This option is unique in its double emphasis on Statistics and Modeling & Scientific Computing. Our graduates will have a unique blend of skills to build models for data, use them efficiently, and interpret them statistically.
Students in the Data Science and Statistics option will substitute Math/Stat 394 for Math/Stat 390.
Option Core (30 credits)
 PHYS 121, 122, 123 (5,5,5)

Either:
 AMATH 301: (4) Beginning Scientific Computing or
 STAT 302: (3) Statistical Software and Its Applications
 MATH/STAT 395: (3) Probability II
 STAT 391: (4) Quantitative Introductory Statistics for Data Science
 CSE 414: (4) Introduction to Database Systems
Option Electives (18 credits)
Group I
At least 6 credits from:
 STAT 403: (4) Intro to Resampling Inference
 STAT 421: (4) Applied Statistics and Experimental Design
 STAT 423: (4) Applied Regression and Analysis of Variance
 STAT 428: (4) Multivariate Analysis for the Social Sciences
 STAT 435: (4) Introduction to Statistical Machine Learning
 *MATH/STAT 396: (3) Probability III
 *BIOST/STAT 425: (3) Introduction to Nonparametric Statistics
 *STAT 427: (4) Introduction to Analysis of Categorical Data
 *STAT 441: (4) Multivariate Statistical Methods
 *MATH/STAT 491: (3) Introduction to Stochastic Processes
*Students who entered the ACMS program prior to Winter 2020 need to email us at: advising@math.washington.edu, if you choose to take one (or more) of the * courses above, to have it be counted towards your degree requirements in your DARS after you've registered for the course.
At least 6 credits from:
 AMATH 481: (5) Scientific Computing
 AMATH 482: (5) Computational Methods for Data Analysis
 AMATH 483: (5) HighPerformance Scientific Computing
 MATH 464: (3) Numerical Analysis I
 MATH 465: (3) Numerical Analysis II
 MATH 407: (3) Linear Optimization
 MATH 408: (3) Nonlinear Optimization
 MATH 409: (3) Discrete Optimization
 CSE 373: (4) Data Structures and Algorithms
 CSE 415: (3) Introduction to Artificial Intelligence
 CSE 417: (3) Algorithms and Computational Complexity
 CSE 472: (5) Introduction to Computational Linguistics
 HCDE 411: (5) Information Visualization
Group II
At least 6 additional credits from approved courses (at the 300 level or higher) in AMATH, CSE, MATH or STAT departments. The courses listed above in Group I are particularly recommended.
Requirements effective Spring Quarter 2016.